ConsensusCluster: a software tool for unsupervised cluster discovery in numerical data

Integration analysis of cell division cycle-associated family genes revealed potential mechanisms of gliomagenesis and constructed an artificial intelligence-driven prognostic signature

Cell division cycle-associated (CDCA) gene family members are essential cell proliferation regulators and play critical roles in various cancers. However, the function of the CDCA family genes in gliomas remains unclear. This study aims to elucidate the role of CDCA family members in gliomas using in vitro and in vivo experiments and bioinformatic analyses. We included eight glioma cohorts in this study. An unsupervised clustering algorithm was used to identify novel CDCA gene family clusters. Then, we utilized multi-omics data to elucidate the prognostic disparities, biological functionalities, genomic alterations, and immune microenvironment among glioma patients. Subsequently, the scRNA-seq analysis and spatial transcriptomic sequencing analysis were carried out to explore the expression distribution of CDCA2 in glioma samples. In vivo and in vitro experiments were used to investigate the effects of CDCA2 on the viability, migration, and invasion of glioma cells. Finally, based on ten machine-learning algorithms, we constructed an artificial intelligence-driven CDCA gene family signature called the machine learning-based CDCA gene family score (MLCS). Our results suggested that patients with the higher expression levels of CDCA family genes had a worse prognosis, more activated RAS signaling pathways, and more activated immunosuppressive microenvironments. CDCA2 knockdown inhibited the proliferation, migration, and invasion of glioma cells. In addition, the MLCS had robust and favorable prognostic predictive ability and could predict the response to immunotherapy and chemotherapy drug sensitivity.

Mitochondrial RNA modification-based signature to predict prognosis of lower grade glioma: a multi-omics exploration and verification study

Mitochondrial RNA modification (MRM) plays a crucial role in regulating the expression of key mitochondrial genes and promoting tumor metastasis. Despite its significance, comprehensive studies on MRM in lower grade gliomas (LGGs) remain unknown. Single-cell RNA-seq data (GSE89567) was used to evaluate the distribution functional status, and correlation of MRM-related genes in different cell types of LGG microenvironment. We developed an MRM scoring system by selecting potential MRM-related genes using LASSO regression analysis and the Random Survival Forest algorithm, based on multiple bulk RNA-seq datasets from TCGA, CGGA, GSE16011, and E-MTAB-3892. Analysis was performed on prognostic and immunological features, signaling pathways, metabolism, somatic mutations and copy number variations (CNVs), treatment responses, and forecasting of potential small-molecule agents. A total of 35 MRM-related genes were selected from the literature. Differential expression analysis of 1120 normal brain tissues and 529 LGGs revealed that 22 and 10 genes were upregulated and downregulated, respectively. Most genes were associated with prognosis of LGG. METLL8, METLL2A, TRMT112, and METTL2B were extensively expressed in all cell types and different cell cycle of each cell type. Almost all cell types had clusters related to mitochondrial RNA processing, ribosome biogenesis, or oxidative phosphorylation. Cell-cell communication and Pearson correlation analyses indicated that MRM may promoting the development of microenvironment beneficial to malignant progression via modulating NCMA signaling pathway and ICP expression. A total of 11 and 9 MRM-related genes were observed by LASSO and the RSF algorithm, respectively, and finally 6 MRM-related genes were used to establish MRM scoring system (TRMT2B, TRMT11, METTL6, METTL8, TRMT6, and TRUB2). The six MRM-related genes were then validated by qPCR in glioma and normal tissues. MRM score can predict the malignant clinical characteristics, abundance of immune infiltration, gene variation, clinical outcome, the enrichment of signaling pathways and metabolism. In vitro experiments demonstrated that silencing METTL8 significantly curbs glioma cell proliferation and enhances apoptosis. Patients with a high MRM score showed a better response to immunotherapies and small-molecule agents such as arachidonyl trifluoromethyl ketone, MS.275, AH.6809, tacrolimus, and TTNPB. These novel insights into the biological impacts of MRM within the glioma microenvironment underscore its potential as a target for developing precise therapies, including immunotherapeutic approaches.

Molecular characterization, clinical value, and cancer-immune interactions of genes related to disulfidptosis and ferroptosis in colorectal cancer

BACKGROUND

This research strived to construct a new signature utilizing disulfidptosis-related ferroptosis (SRF) genes to anticipate response to immunotherapy, prognosis, and drug sensitivity in individuals with colorectal cancer (CRC).

METHODS

The data for RNA sequencing as well as corresponding clinical information of individuals with CRC, were extracted from The Cancer Genome Atlas (TCGA) dataset. SRF were constructed with the help of the random forest (RF), least absolute shrinkage and selection operator (LASSO), and stepwise regression algorithms. To validate the SRF model, we applied it to an external cohort, GSE38832. Prognosis, immunotherapy response, drug sensitivity, molecular functions of genes, and somatic mutations of genes were compared across the high- and low-risk groups (categories). Following this, all statistical analyses were conducted with the aid of the R (version 4.23) software and various packages of the Cytoscape (version 3.8.0) tool.

RESULTS

SRF was developed based on five genes (ATG7, USP7, MMD, PLIN4, and THDC2). Both univariate and multivariate Cox regression analyses established SRF as an independent, prognosis-related risk factor. Individuals from the high-risk category had a more unfavorable prognosis, elevated tumor mutational burden (TMB), and significant immunosuppressive status. Hence, they might have better outcomes post-immunotherapy and might benefit from the administration of pazopanib, lapatinib, and sunitinib.

CONCLUSION

In conclusion, SRF can act as a new biomarker for prognosis assessment. Moreover, it is also a good predictor of drug sensitivity and immunotherapy response in CRC but should undergo optimization before implementation in clinical settings.

Identification of a novel prognostic cuproptosis-associated LncRNA signature for predicting prognosis and immunotherapy response in patients with esophageal cancer

Nowadays, effective prognostic models for esophageal cancer (ESCA) are still lacking. Long noncoding RNAs (lncRNAs) are commonly utilized as indicators for diagnosing cancer and forecasting patient outcomes. Cuproptosis is regulated by multiple genes and is crucial to the progression of ESCA. However, it is not yet clear what role the cuproptosis-associated lncRNAs (CuALs) play in ESCA. To tackle this problem, a prognostic signature incorporating three CuALs was created. This signature was constructed by the use of the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Subsequently, the signature effectively stratified ESCA samples into a high-risk group and a low-risk group. Those in the low-risk group demonstrated extended overall survival (OS), as well as increased infiltration of T cells, macrophages, and NK cells, suggesting a potentially enhanced response to immunotherapy. The ROC curve analysis demonstrated that this prognostic signature outperformed conventional clinical factors in predicting patient prognosis (AUC = 0.708). K-M survival analysis and correlation analysis identified UGDH-AS1 (a CuAL) as a protective factor positively associated with patient prognosis. The results of RT-qPCR and wound healing assays indicated that UGDH-AS1 is overexpressed in ESCA and could inhibit cancer cell migration. In general, the prognostic signature of CuALs demonstrated a robust capability in forecasting the immune environment and patient prognosis, highlighting its potential as a tool for enhancing personalized treatment strategies in ESCA.

Lipid Metabolism-Related Gene Markers Used for Prediction Prognosis, Immune Microenvironment, and Tumor Stage of Pancreatic Cancer

Recently, more and more evidence shows that lipid metabolism disorder has been observed in tumor, which impacts tumor cell proliferation, survival, invasion, metastasis, and response to the tumor microenvironment (TME) and tumor treatment. However, hitherto there has not been sufficient research to demonstrate the role of lipid metabolism in pancreatic cancer. This study contrives to get an insight into the relationship between the characteristics of lipid metabolism and pancreatic cancer. We collected samples of patients with pancreatic cancer from the Gene Expression Omnibus (GEO), the Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the International Cancer Genome Consortium (ICGC) databases. Firstly, we implemented univariate regression analysis to get prognosis-related lipid metabolism genes screened and a construction of protein-protein interaction (PPI) network ensued. Then, contingent on our screening results, we explored the molecular subtypes mediated by lipid metabolism-related genes and the correlated TME cell infiltration. Additionally, we studied the disparately expressed genes among disparate lipid metabolism subtypes and established a scoring model of lipid metabolism-related characteristics using the least absolute shrinkage and selection operator (LASSO) regression analysis. At last, we explored the relationship between the scoring model and disease prognosis, tumor stage, tumor microenvironment, and immunotherapy. Two subtypes, C1 and C2, were identified, and lipid metabolism-related genes were studied. The result indicated that the patients with subtype C2 have a significantly lower survival rate than that of the patients with subtype C1, and we found difference in abundance of different immune-infiltrating cells. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed the association of these differentially expressed genes with functions and pathways related to lipid metabolism. Finally, we established a scoring model of lipid metabolism-related characteristics based on the disparately expressed genes. The results show that our scoring model have a substantial effect on forecasting the prognosis of patients with pancreatic cancer. The lipid metabolism model is an important biomarker of pancreatic cancer. Using the model, the relationship between disease prognosis, molecular subtypes, TME cell infiltration characteristics, and immunotherapy in pancreatic cancer patients could be explored.

PIK3CA mutation-driven immune signature as a prognostic marker for evaluating the tumor immune microenvironment and therapeutic response in breast cancer

PURPOSE

Gene mutations drive tumor immune microenvironment (TIME) heterogeneity, in turn affecting prognosis and immunotherapy efficacy. PIK3CA is the most frequently mutated gene in breast cancer (BC), yet its relevance to BC prognosis remains controversial. Herein, we sought to determine the impact of PIK3CA mutation-driven immune genes (PDIGs) on BC prognosis in relation to TIME heterogeneity.

METHODS

PIK3CA mutation characteristics were compared and verified between the TCGA-BRCA dataset and a patient cohort from our hospital. PIK3CA mutation-driven differentially expressed genes were identified for consensus clustering and weighted gene co-expression network analysis to select the modules most relevant to the immune subtype. Thereafter, the two were intersected to obtain PDIGs. Univariate Cox, LASSO, and multivariate Cox regression analyses were sequentially performed on PDIGs to obtain a PIK3CA mutation-driven immune signature (PDIS), which was then validated using the Gene Expression Omnibus (GEO) database. Differences in functional enrichment, mutation landscape, immune infiltration, checkpoint gene expression, and drug response were compared between different risk groups.

RESULTS

PIK3CA mutation frequencies in the TCGA and validation cohorts were 34.49% and 40.83%, respectively. PIK3CA mutants were significantly associated with ER, PR, and molecular BC subtypes in our hospital cohort. The PDIS allowed for effective risk stratification and exhibited prognostic power in TCGA and GEO sets. The low-risk patients exhibited greater immune infiltration, higher expression of common immune checkpoint factors, and lower scores for tumor immune dysfunction and exclusion.

CONCLUSION

The PDIS can be used as an effective prognostic model for predicting immunotherapy response to guide clinical decision-making.

Identification of an angiogenesis-related risk score model for survival prediction and immunosubtype screening in multiple myeloma

BACKGROUND

Multiple myeloma (MM) is an incurable B-cell malignancy, but with the emergence of immunotherapy, a potential cure is hopeful. The individualized interaction between the tumor and bone marrow (BM) microenvironment determines the response to immunotherapy. Angiogenesis is a constant hallmark of the BM microenvironment in MM. However, little is known about the potency ability of angiogenesis-associated genes (AAGs) to regulate the immune microenvironment of MM patients.

METHODS

We comprehensively dissected the associations between angiogenesis and genomic landscapes, prognosis, and the immune microenvironment by integrating 36 AAGs. Immunohistochemistry was performed to verify the correlation between angiogenic factor expression and patient prognosis. Single-sample gene set enrichment analysis was applied to quantify the relative abundance of 28 infiltrating cells. The AAG score was constructed using the least absolute shrinkage and selection operator Cox regression model.

RESULTS

Angiogenesis was closely correlated with MM patient prognosis, and the mutation intensity of the AAGs was low. Immunohistochemistry confirmed that high microvessel density predicted poor prognosis. Three AAG clusters and two gene clusters with distinct clinical outcomes and immune characteristics were identified. The established AAG_score model performed well in predicting patient prognosis and active immunotherapy response. The high-AAG_score subgroup was characterized by reduced immune cell infiltration, poor prognosis, and inactive immunotherapy response. Multivariate analyses indicated that the AAG_score was strongly robust and independent among the prognostic variables.

CONCLUSION

This study revealed that angiogenesis is significantly related to MM patient prognosis and immune phenotype. Evaluating the AAG signature was conducive to predicting patient response to immunotherapy and guiding more efficacious immunotherapy strategies.

Identification of angiogenesis-related subtypes, the development of a prognosis model, and features of tumor microenvironment in colon cancer

Angiogenesis is associated with tumor progression, prognosis, and treatment effect. However, the angiogenesis' underlying mechanisms in the tumor microenvironment (TME) still remain unclear. Understanding the dynamic interactions between angiogenesis and TME in colon adenocarcinoma (COAD) is necessary. We downloaded the transcriptome data and corresponding clinical data of colon cancer patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. We identified two distinct angiogenesis-related molecular subtypes (subtype A and subtype B) and assessed the clinical features, prognosis, and infiltrating immune cells of patients in the two subtypes. According to the prognostic differential genes, we defined two different gene clusters to further explore the correlation between angiogenesis and tumor heterogeneity. Then, we construct the prognostic risk scoring model angiogenesis-related gene (ARG-score) including seven genes (ARMCX2, latent transforming growth factor β binding protein 1, ADAM8, FABP4, CCL11, CXCL11, ITLN1) using Lasso-multivariate cox method. We analyzed the correlation between ARG-score and prognosis, clinicopathological features, TME, molecular feature, cancer stem cells (CSCs), and microsatellite instability (MSI) status. To assess the application value of ARG-score in clinical treatment, immunophenotype score was used to predict patients' immunotherapy response in colon cancer. We found the mutations of ARGs in TCGA-COAD dataset from genetic levels and discussed their expression patterns based on TCGA and GEO datasets. We observed important differences in clinicopathological features, prognosis, immune feature, molecular feature between the two molecular subgroups. Then, we established an ARG-score for predicting OS and validated its predictive capability. A high ARG-score characterized by higher transcription level of ARGs, suggested lower MSI-high (MSI-H), lower immune score, and worse clinical stage and survival outcome. Additionally, the ARG-score was remarkably related to the CSCs index and immunotherapy sensitivity. We found two new molecular subtypes and two gene clusters based on ARGs and established an ARG-score. Multilayered analysis revealed that ARGs were remarkably correlated to the heterogeneity of colon cancer patients and explained the process of tumorigenesis and progression better. The ARG-score can help us better assess patients' survival outcomes and provide guidance for individualized treatment.

Cuproptosis facilitates immune activation but promotes immune escape, and a machine learning-based cuproptosis-related signature is identified for predicting prognosis and immunotherapy response of gliomas

AIMS

Cell death, except for cuproptosis, in gliomas has been extensively studied, providing novel targets for immunotherapy by reshaping the tumor immune microenvironment through multiple mechanisms. This study aimed to explore the effect of cuproptosis on the immune microenvironment and its predictive power in prognosis and immunotherapy response.

METHODS

Eight glioma cohorts were included in this study. We employed the unsupervised clustering algorithm to identify novel cuproptosis clusters and described their immune microenvironmental characteristics, mutation landscape, and altered signaling pathways. We verified the correlation among FDX1, SLC31A1, and macrophage infiltration in 56 glioma tissues. Next, based on multicenter cohorts and 10 machine learning algorithms, we constructed an artificial intelligence-driven cuproptosis-related signature named CuproScore.

RESULTS

Our findings suggested that glioma patients with high levels of cuproptosis had a worse prognosis owing to immunosuppression caused by unique immune escape mechanisms. Meanwhile, we experimentally validated the positive association between cuproptosis and macrophages and its tumor-promoting mechanism in vitro. Furthermore, our CuproScore exhibited powerful and robust prognostic predictive ability. It was also capable of predicting response to immunotherapy and chemotherapy drug sensitivity.

CONCLUSIONS

Cuproptosis facilitates immune activation but promotes immune escape. The CuproScore could predict prognosis and immunotherapy response in gliomas.

Clinical and therapeutical significances of the cluster and signature based on oxidative stress for osteosarcoma

BACKGROUND

It is of great clinical significance to find out the ideal tumor biomarkers and therapeutic targets to improve the prognosis of patients with osteosarcoma (OS). Oxidative stress (OXS) can directly target intracellular macromolecules and exhibit dual effects of tumor promotion and suppression.

METHODS

OXS-related genes (OXRGs) were extracted from public databases, including TARGET and GEO. Univariate Cox regression analysis, Random Survival Forest algorithm, and LASSO regression were performed to identify prognostic genes and establish the OXS-signature. The efficacy of the OXS-signature was further evaluated by Kaplan-Meier curves and timeROC package. Evaluation of immunological characteristics was achieved based on ESTIMATE algorithm and ssGSEA. Submap algorithm was used to explore the response to anti-PD1 and anti-CTLA4 therapy for OS. Drug response prediction was conducted by using pRRophetic package. The expression values of related genes in the OXS-signature were detected with PCR assays.

RESULTS

Two OXS-clusters were identified for OS, with remarkable differences of clusters presented in prognosis. Kyoto Encyclopedia of Genes Genomes (KEGG) analysis showed that differentially expressed genes (DEGs) between the OXS-clusters were significantly enriched in several immune-related pathways. Patients with lower OS-scores attained better clinical outcomes, and presented more sensitivity to ICB therapy. By contrast, OS patients with higher OS-scores revealed more sensitivity to certain drugs. Furthermore, critical genes, RHBDL2 and CGREF1 from the model, were significantly higher expressed in OS cell lines.

CONCLUSIONS

Our study identified the clusters and signature based on OXS, which would lay the foundation for molecular experimental research, disease prevention and treatment of OS.

Integrated analysis of bulk RNA-seq and single-cell RNA-seq reveals the function of pyrocytosis in the pathogenesis of abdominal aortic aneurysm

Pyrocytosis is involved in the development of abdominal aortic aneurysm (AAA), we explored the pyrocytosis-related hub genes in AAA and conducted a diagnostic model based on the pyrocytosis-related genes score (PRGs). A total of 2 bulk RNA-seq (GSE57691 and GSE47472) datasets and pyrocytosis-related genes were integrated to obtain 24 pyrocytosis-related different expression genes (DEGs). The LASSO Cox regression analysis was conducted to filter out 7 genes and further establish the nomogram signature based on the PRGs that exhibited a good diagnosis value. Weighted gene co-expression network analysis (WGCNA) established 14 gene modules and further identified 6 hub genes which were involved in the regulatory process of pyrocytosis in AAA. At the single cell level, we further identified 3 immune cells were highly associated with the pyrocytosis process in AAA. Finally, the cell-cell communication demonstrated that fibroblasts and endothelial cells and myeloid cells maintained close communications. Here, we identified the dysfunctional expressed pyrocytosis-related genes and immune cells in AAA, which provide a comprehensive understanding of the pathogenesis of AAA.

Development of a novel vasculogenic mimicry-associated gene signature for the prognostic assessment of osteosarcoma patients

BACKGROUND

Osteosarcoma (OS) is a form of primary bone malignancy associated with poor prognostic outcomes. Recent work has highlighted vasculogenic mimicry (VM) as a key mechanism that supports aggressive tumor growth. The patterns of VM-associated gene expression in OS and the relationship between these genes and patient outcomes, however, have yet to be defined.

METHODS

Here, 48 VM-related genes were systematically assessed to examine correlations between the expression of these genes and OS patient prognosis in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) cohort. Patients were classified into three OS subtypes. Differentially expressed genes for these three OS subtypes were then compared with hub genes detected in a weighted gene co-expression network analysis, leading to the identification of 163 overlapping genes that were subject to further biological activity analyses. A three-gene signature (CGREF1, CORT, and GALNT14) was ultimately constructed through a least absolute shrinkage and selection operator Cox regression analysis, and this signature was used to separate patients into low- and high-risk groups. The K-M survival analysis, receiver operating characteristic analysis, and decision curve analysis were adopted to evaluate the prognostic prediction performance of the signature. Furthermore, the expression patterns of three genes derived from the prognostic model were validated by quantitative real-time polymerase chain reaction (RT-qPCR).

RESULTS

VM-associated gene expression patterns were successfully established, and three VM subtypes of OS that were associated with patient prognosis and copy number variants were defined. The developed three-gene signature was constructed, which served as independent prognostic markers and prediction factors for the clinicopathological features of OS. Finally, lastly, the signature may also have a guiding effect on the sensitivity of different chemotherapeutic drugs.

CONCLUSION

Overall, these analyses facilitated the development of a prognostic VM-associated gene signature capable of predicting OS patient outcomes. This signature may be of value for both studies of the mechanistic basis for VM and clinical decision-making in the context of OS patient management.

Identification of aneuploidy-related gene signature to predict survival in head and neck squamous cell carcinomas

BACKGROUND

To parse the characteristics of aneuploidy related riskscore (ARS) model in head and neck squamous cell carcinomas (HNSC) and their predictive ability on patient prognosis.

METHODS

Molecular subtyping of HNSC specimens was clustered by Copy Number Variation (CNV) data from The Cancer Genome Atlas (TCGA) dataset applying consistent clustering, followed by immune condition evaluation, differentially expressed genes (DEGs) analysis and DEGs function annotation. Weighted gene co-expression network analysis (WGCNA), protein-protein interaction, Univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analysis were implemented to construct an ARS model. A nomogram for clinic practice was designed by rms package. Immunotherapy evaluation and drug sensitivity prediction were also carried out.

RESULTS

We stratified HNSC patients into three different molecular subgroups, with the best prognosis in C1 cluster among 3 clusters. C1 cluster displayed greatest immune infiltration status. The most DEGs between C1 and C2 groups, mainly enriched in cell cycle and immune function. We constructed a nine-gene ARS model (ICOS, IL21R, CCR7, SELL, CYTIP, ZAP70, CCR4, S1PR4 and CD79A) that effectively differentiates between high- and low-risk patients. Patients in low ARS group showed a higher sensitivity to immunotherapy. A nomogram built by integrating ARS and clinic-pathological characteristics helped predict clinic survival benefit. Drug sensitivity evaluation found that 4/9 inhibitor drugs (MK-8776, AZD5438, PD-0332991, PHA-665752) acted on the cell cycle.

CONCLUSIONS

We classified 3 molecular subtypes for HNSC patients and established an ARS prognostic model, which offered a prospective direction for prognosis in HNSC.

Construction and validation of a novel angiogenesis pattern to predict prognosis and immunotherapy efficacy in colorectal cancer

BACKGROUND

Evidence suggests that the tumor microenvironment (TME) affects the tumor active response to immunotherapy. Tumor angiogenesis is closely related to the TME. Nonetheless, the effects of angiogenesis on the TME of colorectal cancer (CRC) remain unknown.

METHODS

We comprehensively assessed the angiogenesis patterns in CRC based on 36 angiogenesis-related genes (ARGs). Subsequently, we evaluated the prognostic values and therapeutic sensitivities of angiogenesis patterns using multiple methods. We then performed the machine learning algorithm and functional experiments to identify the prognostic key ARGs. Ultimately, the regulation of gut microbiota on the expression of ARGs was further investigated by using whole genome sequencing.

RESULTS

Two angiogenesis clusters were identified and angiogenesis cluster B was characterized by increased stromal and immunity activation with unfavorable odds of survival. Further, an ARG_score including 9 ARGs to predict recurrence-free survival (RFS) was established and its predominant predictive ability was confirmed. The low ARG_score patients were characterized by a high mutation burden, high microsatellite instability, and immune activation with better prognosis. Moreover, patients with high KLK10 expression were associated with a hot tumor immune microenvironment, poorer immune checkpoint blocking treatment, and shorter survival. The in vitro experiments also indicated that Fusobacterium nucleatum (F.n) infection significantly induced KLK10 expression in CRC.

CONCLUSIONS

The quantification of angiogenesis patterns could contribute to predict TME characteristics, prognosis, and individualized immunotherapy strategies. Furthermore, our findings suggest that F.n may influence CRC progression through ARGs, which could serve as a clinical biomarker and therapeutic target for F.n-infected CRC patients.

Prognostic value analysis of cholesterol and cholesterol homeostasis related genes in breast cancer by Mendelian randomization and multi-omics machine learning

Introduction

The high incidence of breast cancer (BC) prompted us to explore more factors that might affect its occurrence, development, treatment, and also recurrence. Dysregulation of cholesterol metabolism has been widely observed in BC; however, the detailed role of how cholesterol metabolism affects chemo-sensitivity, and immune response, as well as the clinical outcome of BC is unknown.

Methods

With Mendelian randomization (MR) analysis, the potential causal relationship between genetic variants of cholesterol and BC risk was assessed first. Then we analyzed 73 cholesterol homeostasis-related genes (CHGs) in BC samples and their expression patterns in the TCGA cohort with consensus clustering analysis, aiming to figure out the relationship between cholesterol homeostasis and BC prognosis. Based on the CHG analysis, we established a CAG_score used for predicting therapeutic response and overall survival (OS) of BC patients. Furthermore, a machine learning method was adopted to accurately predict the prognosis of BC patients by comparing multi-omics differences of different risk groups.

Results

We observed that the alterations in plasma cholesterol appear to be correlative with the venture of BC (MR Egger, OR: 0.54, 95% CI: 0.35-0.84, p<0.006). The expression patterns of CHGs were classified into two distinct groups(C1 and C2). Notably, the C1 group exhibited a favorable prognosis characterized by a suppressed immune response and enhanced cholesterol metabolism in comparison to the C2 group. In addition, high CHG score were accompanied by high performance of tumor angiogenesis genes. Interestingly, the expression of vascular genes (CDH5, CLDN5, TIE1, JAM2, TEK) is lower in patients with high expression of CHGs, which means that these patients have poorer vascular stability. The CAG_score exhibits robust predictive capability for the immune microenvironment characteristics and prognosis of patients(AUC=0.79). It can also optimize the administration of various first-line drugs, including AKT inhibitors VIII Imatinib, Crizotinib, Saracatinib, Erlotinib, Dasatinib, Rapamycin, Roscovitine and Shikonin in BC patients. Finally, we employed machine learning techniques to construct a multi-omics prediction model(Risklight),with an area under the feature curve (AUC) of up to 0.89.

Conclusion

With the help of CAG_score and Risklight, we reveal the signature of cholesterol homeostasis-related genes for angiogenesis, immune responses, and the therapeutic response in breast cancer, which contributes to precision medicine and improved prognosis of BC.

The heterogeneity of tumour immune microenvironment revealing the CRABP2/CD69 signature discriminates distinct clinical outcomes in breast cancer

BACKGROUND

It has been acknowledged that the tumour immune microenvironment (TIME) plays a critical role in determining therapeutic responses and clinical outcomes in breast cancer (BrCa). Thus, the identification of the TIME features is essential for guiding therapy and prognostic assessment for BrCa.

METHODS

The heterogeneous cellular composition of the TIME in BrCa by single-cell RNA sequencing (scRNA-seq). Two subtype-special genes upregulated in the tumour-rich subtype and the immune-infiltrating subtype were extracted, respectively. The CRABP2/CD69 signature was established based on CRABP2 and CD69 expression, and its predictive values for the clinical outcome and the neoadjuvant chemotherapy (NAT) responses were validated in multiple cohorts. Moreover, the oncogenic role of CRABP2 was explored in BrCa cells.

RESULTS

Based on the heterogeneous cellular composition of the TIME in BrCa, the BrCa samples could be divided into the tumour-rich subtype and the immune-infiltrating subtype, which exhibited distinct prognosis and chemotherapeutic responses. Next, we extracted CRABP2 as the biomarker for the tumour-rich subtype and CD69 as the biomarker for the immune-infiltrating subtype. Based on the CRABP2/CD69 signature, BrCa samples were re-divided into three subtypes, and the CRABP2highCD69low subtype exhibited the worst prognosis and the lowest chemotherapeutic response, while the CRABP2lowCD69high subtype showed the opposite results. Furthermore, CARBP2 functioned as a novel oncogene in BrCa, which promoted tumour cell proliferation, migration, and invasion, and CRABP2 inhibition triggered the activation of cytotoxic T lymphocytes (CTLs).

CONCLUSION

The CRABP2/CD69 signature is significantly associated with the TIME features and could effectively predict the clinical outcome. Also, CRABP2 is determined to be a novel oncogene, which could be a therapeutic target in BrCa.

Integrative evaluation and experimental validation of the immune-modulating potential of dysregulated extracellular matrix genes in high-grade serous ovarian cancer prognosis

BACKGROUND

High-grade serous ovarian cancer (HGSOC) is a challenging malignancy characterized by complex interactions between tumor cells and the surrounding microenvironment. Understanding the immune landscape of HGSOC, particularly the role of the extracellular matrix (ECM), is crucial for improving prognosis and guiding therapeutic interventions.

METHODS AND RESULTS

Using univariate Cox regression analysis, we identified 71 ECM genes associated with prognosis in seven HGSOC populations. The ECMscore signature, consisting of 14 genes, was validated using Cox proportional hazards regression with a lasso penalty. Cox regression analyses demonstrated that ECMscore is an excellent indicator for prognostic classification in prevalent malignancies, including HGSOC. Moreover, patients with higher ECMscores exhibited more active stromal and carcinogenic activation pathways, including apical surface signaling, Notch signaling, apical junctions, Wnt signaling, epithelial-mesenchymal transition, TGF-beta signaling, and angiogenesis. In contrast, patients with relatively low ECMscores showed more active immune-related pathways, such as interferon alpha response, interferon-gamma response, and inflammatory response. The relationship between the ECMscore and genomic anomalies was further examined. Additionally, the correlation between ECMscore and immune microenvironment components and signals in HGSOC was examined in greater detail. Moreover, the expression of MGP, COL8A2, and PAPPA and its correlation with FAP were validated using qRT-PCR on samples from HGSOC. The utility of ECMscore in predicting the prospective clinical success of immunotherapy and its potential in guiding the selection of chemotherapeutic agents were also explored. Similar results were obtained from pan-cancer research.

CONCLUSION

The comprehensive evaluation of the ECM may help identify immune activation and assist patients in HGSOC and even pan-cancer in receiving proper therapy.

Characterization of tumor microenvironment and tumor immunology based on the double-stranded RNA-binding protein related genes in cervical cancer

BACKGROUND

Cervical cancer is one of the most common gynecological cancers threatening women's health worldwide. Double-stranded RNA-binding proteins (dsRBPs) regulate innate immunity and are therefore believed to be involved in virus-related malignancies, however, their role in cervical cancer is not well known.

METHODS

We performed RNA-seq of tumor samples from cervical cancer patients in local cohort and also assessed the RNA-seq and clinical data derived from public datasets. By using single sample Gene Set Enrichment Analysis (ssGSEA) and univariate Cox analysis, patients were stratified into distinct dsRBP clusters. Stepwise Cox and CoxBoost were performed to construct a risk model based on optimal dsRBPs clusters-related differentially expressed genes (DEGs), and GSE44001 and CGCI-HTMCP-CC were employed as two external validation cohorts. Single cell RNA sequencing data from GSE168652 and Scissor algorithm were applied to evaluated the signature-related cell population.

RESULTS

The expression of dsRBP features was found to be associated with HPV infection and carcinogenesis in CESC. However, only Adenosine deaminases acting on RNA (ADAR) and Dicer, Drosha, and Argonautes (DDR) exhibited significant correlations with the overall survival (OS) of CESC patients. Based on these findings, CESC patients were divided into three dsRBP clusters. Cluster 3 showed superior OS but lower levels of ADAR and DDR. Additionally, Cluster 3 demonstrated enhanced innate immunity, with significantly higher activity in cancer immunity cycles, immune scores, and levels of tumor-infiltrating immune cells, particularly CD8+ T cells. Furthermore, a risk model based on nine dsRBP cluster-related DEGs was established. The accuracy of survival prediction for 1 to 5 years was consistently above 0.78, and this model's robust predictive capacity was confirmed by two external validation sets. The low-risk group exhibited significantly higher levels of immune checkpoints, such as PDCD1 and CTLA4, as well as a higher abundance of CD8+ T cells. Analysis of single-cell sequencing data revealed a significant association between the dsRBP signature and glycolysis. Importantly, low-risk patients showed improved OS and a higher response rate to immunotherapy, along with enduring clinical benefits from concurrent chemoradiotherapy.

CONCLUSIONS

dsRBP played a crucial role in the regulation of prognosis and tumor immunology in cervical cancer, and its prognostic signature provides a strategy for risk stratification and immunotherapy evaluation.

Prognostic model of kidney renal clear cell carcinoma using aging-related long noncoding RNA signatures identifies THBS1-IT1 as a potential prognostic biomarker for multiple cancers

Aging is responsible for the main intrinsic triggers of cancers; however, the studies of aging risk factors in cancer animal models and cancer patients are rare and insufficient to be represented in cancer clinical trials. For a better understanding of the complex regulatory networks of aging and cancers, 8 candidate aging related long noncoding RNAs (CarLncs) identified from the healthy aging models, centenarians and their offsprings, were selected and their association with kidney renal clear cell carcinoma (KIRC) was explored by series of cutting edge analyses such as support vector machine (SVM) and random forest (RF) algorithms. Using data downloaded from TCGA and GTEx databases, a regulatory network of CarLncs-miRNA-mRNA was constructed and five genes within the network were screened out as aging related feature genes for developing KIRC prognostic models. After a strict filtering pipeline for modeling, a formula using the transcript per million (TPM) values of feature genes "LncAging_score = 0.008* MMP11 + 0.066* THBS1-IT1 + (-0.014)* DYNLL2 + (-0.030)* RMND5A+ 0.008* PEG10" was developed. ROC analysis and nomogram suggest our model achieves a great performance in KIRC prognosis. Among the 8 CarLncs, we found that THBS1-IT1 was significantly dysregulated in 12 cancer types. A comprehensive pan-cancer analysis demonstrated that THBS1-IT1 is a potential prognostic biomarker in not only KIRC but also multiple cancers, such as LUSC, BLCA, GBM, LGG, MESO, PAAD, STAD and THCA, it was correlated with tumor microenvironment (TME) and tumor immune cell infiltration (TICI) and its high expression was related with poor survival.

Establishment of an endoplasmic reticulum stress-associated lncRNAs model to predict prognosis and immunological characteristics in hepatocellular carcinoma

BACKGROUND

The endoplasmic reticulum stress (ERS) and unfolded protein response (UPR) pathways play an essential role in the pathophysiology of hepatocellular carcinoma (HCC), and activation of the UPR pathway is strongly associated with tumor growth. However, the function of ERS-associated long non-coding RNAs (lncRNAs) in HCC is less recognized.

METHODS

We have used TCGA (The Cancer Genome Atlas) to obtain clinical and transcriptome data for HCC patients and the GSEA (Gene Set Enrichment Analysis) molecular signature database to get the ERS gene. ERS-associated prognostic lncRNA was determined using univariate Cox regression study. Then, least absolute shrinkage and selection operator and multivariate Cox regression study were used to construct ERS-associated lncRNAs risk model. Next, we use Kaplan-Meier (KM) survival study, time-dependent receiver operating characteristic (ROC) curve, univariate and multivariate Cox regression study to validate and evaluate the risk model. GSEA reveals the underlying molecular mechanism of the risk model. In addition, differences in Immune cell Infiltration Study, half-maximal inhibitory concentration (IC50) and immune checkpoints blockade (ICB) treatment between high and low risk groups were analyzed.

RESULTS

We constructed a risk model consisting of 6 ERS-associated lncRNAS (containingMKLN1-AS, LINC01224, AL590705.3, AC008622.2, AC145207.5, and AC026412.3). The KM survival study showed that the prognosis of HCC patients in low-risk group was better than that in high-risk group. ROC study, univariate and multivariate Cox regression study showed that the risk model had good predictive power for HCC patients. Our verification sample verified the aforesaid findings. GSEA suggests that several tumor- and metabolism-related signaling pathways are associated with risk groups. Simultaneously, we discovered that the risk models may help in the treatment of ICB and the selection of chemotherapeutic drugs.

CONCLUSIONS

In this article, we created an ERS-associated lncRNAs risk model to help prognostic diagnosis and personalized therapy in HCC.

Integrative analysis of lysine acetylation-related genes and identification of a novel prognostic model for oral squamous cell carcinoma

Introduction: Oral squamous cell carcinoma (OSCC), which accounts for a high proportion of oral cancers, is characterized by high aggressiveness and rising incidence. Lysine acetylation is associated with cancer pathogenesis. Lysine acetylation-related genes (LARGs) are therapeutic targets and potential prognostic indicators in various tumors, including oral squamous cell carcinoma. However, systematic bioinformatics analysis of the Lysine acetylation-related genes in Oral squamous cell carcinoma is still unexplored. Methods: We analyzed the expression of 33 Lysine acetylation-related genes in oral squamous cell carcinoma and the effects of their somatic mutations on oral squamous cell carcinoma prognosis. Consistent clustering analysis identified two lysine acetylation patterns and the differences between the two patterns were further evaluated. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to develop a lysine acetylation-related prognostic model using TCGA oral squamous cell carcinoma datasets, which was then validated using gene expression omnibus (GEO) dataset GSE41613. Results: Patients with lower risk scores had better prognoses, in both the overall cohort and within the subgroups These patients also had "hot" immune microenvironments and were more sensitive to immunotherapy. Disscussion: Our findings offer a new model for classifying oral squamous cell carcinoma and determining its prognosis and offer novel insights into oral squamous cell carcinoma diagnosis and treatment.

Immunogenic cell death-based prognostic model for predicting the response to immunotherapy and common therapy in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a malignant tumor in the respiratory system. The efficacy of current treatment modalities varies greatly, and individualization is evident. Therefore, finding biomarkers for predicting treatment prognosis and providing reference and guidance for formulating treatment options is urgent. Cancer immunotherapy has made distinct progress in the past decades and has a significant effect on LUAD. Immunogenic Cell Death (ICD) can reshape the tumor's immune microenvironment, contributing to immunotherapy. Thus, exploring ICD biomarkers to construct a prognostic model might help individualized treatments. We used a lung adenocarcinoma (LUAD) dataset to identify ICD-related differentially expressed genes (DEGs). Then, these DEGs were clustered and divided into subgroups. We also performed variance analysis in different dimensions. Further, we established and validated a prognostic model by LASSO Cox regression analysis. The risk score in this model was used to evaluate prognostic differences by survival analysis. The treatment prognosis of various therapies were also predicted. LUAD samples were divided into two subgroups. The ICD-high subgroup was related to an immune-hot phenotype more sensitive to immunotherapy. The prognostic model was constructed based on six ICD-related DEGs. We found that high-risk score patients responded better to immunotherapy. The ICD prognostic model was validated as a standalone factor to evaluate the ICD subtype of individual LUAD patients, which might contribute to more effective therapies.

Subtype and prognostic analysis of immunogenic cell death-related gene signature in prostate cancer

Background

Immunogenic cell death (ICD) plays a vital role in tumor progression and immune response. However, the integrative role of ICD-related genes and subtypes in the tumor microenvironment (TME) in prostate cancer (PCa) remains unknown.

Materials and methods

The sample data were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Memorial Sloan Kettering Cancer Center (MSKCC) prostate cancer-related databases. We first divided the subtypes based on ICD genes from 901 PCa patients and then identified the prognosis- related genes (PRGs) between different ICD subtypes. Subsequently, all the patients were randomly split into the training and test groups. We developed a risk signature in the training set by least absolute shrinkage and selection operator (LASSO)-Cox regression. Following this, we verified this prognostic signature in both the training test and external test sets. The relationships between the different subgroups and clinical pathological characteristics, immune infiltration characteristics, and mutation status of the TME were examined. Finally, the artificial neural network (ANN) and fundamental experiment study were constructed to verify the accuracy of the prognostic signature.

Results

We identified two ICD clusters with immunological features and three gene clusters composed of PRGs. Additionally, we demonstrated that the risk signature can be used to evaluate tumor immune cell infiltration, prognostic status, and an immune checkpoint inhibitor. The low-risk group, which has a high overlap with group C of the gene cluster, is characterized by high ICD levels, immunocompetence, and favorable survival probability. Furthermore, the tumor progression genes selected by the ANN also exhibit potential associations with risk signature genes.

Conclusion

This study identified individuals with high ICD levels in prostate cancer who may have more abundant immune infiltration and revealed the potential effects of risk signature on the TME, immune checkpoint inhibitor, and prognosis of PCa.

Ferroptosis-Related lncRNAs Act as Novel Prognostic Biomarkers in the Gastric Adenocarcinoma Microenvironment, Immunotherapy, and Chemotherapy

Ferroptosis, a form of programmed cell death akin to necrosis, is managed by iron and is distinguished by lipid peroxidation. Gastric cancer is a highly aggressive form of cancer, responsible for the third highest number of cancer-related deaths globally. Despite this, the potential of ferroptosis to predict the occurrence of this cancer is yet to be determined. In this research, a comprehensive examination was conducted to explore the link between long noncoding RNAs (lncRNAs) and ferroptosis, in order to uncover an lncRNA signature that can predict drug susceptibility and tumor mutational burden (TMB) in gastric adenocarcinoma. We conducted an in-depth analysis of the GC immune microenvironment and immunotherapy, with a particular focus on ferroptosis-related lncRNA prognostic biomarkers, and further explored the correlation between these factors and prognosis, immune infiltration, single nucleotide variation (SNV), and drug sensitivity for gastric adenocarcinoma patients. Through our investigations, we have discovered five lncRNA signatures related to ferroptosis that can accurately forecast the prognosis of gastric adenocarcinoma patients and also regulate the proliferation, migration, and occurrence of ferroptosis in gastric adenocarcinoma cells. In conclusion, this lncRNA signature associated with ferroptosis may be employed as a prognostic indicator for gastric adenocarcinoma, thus presenting a potential solution.

The Effect of Lung Resection for NSCLC on Circulating Immune Cells: A Pilot Study

This pilot study sought to evaluate the circulating levels of immune cells, particularly regulatory T-cell (Treg) subsets, before and after lung resection for non-small cell lung cancer. Twenty-five patients consented and had specimens collected. Initially, peripheral blood of 21 patients was collected for circulating immune cell studies. Two of these patients were excluded due to technical issues, leaving 19 patients for the analyses of circulating immune cells. Standard gating and high-dimensional unsupervised clustering flow cytometry analyses were performed. The blood, tumors and lymph nodes were analyzed via single-cell RNA and TCR sequencing for Treg analyses in a total of five patients (including four additional patients from the initial 21 patients). Standard gating flow cytometry revealed a transient increase in neutrophils immediately following surgery, with a variable neutrophil-lymphocyte ratio and a stable CD4-CD8 ratio. Unexpectedly, the total Treg and Treg subsets did not change with surgery with standard gating in short- or long-term follow-up. Similarly, unsupervised clustering of Tregs revealed a dominant cluster that was stable perioperatively and long-term. Two small FoxP3hi clusters slightly increased following surgery. In the longer-term follow-up, these small FoxP3hi Treg clusters were not identified, indicating that they were likely a response to surgery. Single-cell sequencing demonstrated six CD4+FoxP3+ clusters among the blood, tumors and lymph nodes. These clusters had a variable expression of FoxP3, and several were mainly, or only, present in tumor and lymph node tissue. As such, serial monitoring of circulating Tregs may be informative, but not completely reflective of the Tregs present in the tumor microenvironment.

Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma

We used pyroptosis-related genes to establish a risk-score model for prognostic prediction of liver hepatocellular carcinoma (LIHC) patients. A total of 52 pyroptosis-associated genes were identified. Then, data for 374 LIHC patients and 50 normal individuals were acquired from the TCGA database. Through gene expression analyses, differentially expressed genes (DEGs) were determined. The 13 pyroptosis-related genes (PRGs) confirmed as potential prognostic factors through univariate Cox regression analysis were entered into Lasso and multivariate Cox regression to build a PRGs prognostic signature, containing four PRGs (BAK1, GSDME, NLRP6, and NOD2) determined as independent prognostic factors. mRNA levels were evaluated by qRT-PCR, while overall survival (OS) rates were assessed by the Kaplan-Meier method. Enrichment analyses were done to establish the mechanisms associated with differential survival status of LIHC patients from a tumor immunology perspective. Additionally, a risk score determined by the prognostic model could divide LIHC patients into low- and high-risk groups using median risk score as cut-off. A prognostic nomogram, derived from the prognostic model and integrating clinical characteristics of patients, was constructed. The prognostic function of the model was also validated using GEO, ICGC cohorts, and online databases Kaplan-Meier Plotter. Small interfering RNA-mediated knockdown of GSDME, as well as lentivirus-mediated GSDME knockdown, were performed to validate that knockdown of GSDME markedly suppressed growth of HCC cells both in vivo and in vitro. Collectively, our study demonstrated a PRGs prognostic signature that had great clinical value in prognosis assessment.

Machine learning-based glycolysis-associated molecular classification reveals differences in prognosis, TME, and immunotherapy for colorectal cancer patients

Background

Aerobic glycolysis is a process that metabolizes glucose under aerobic conditions, finally producing pyruvate, lactic acid, and ATP for tumor cells. Nevertheless, the overall significance of glycolysis-related genes in colorectal cancer and how they affect the immune microenvironment have not been investigated.

Methods

By combining the transcriptome and single-cell analysis, we summarize the various expression patterns of glycolysis-related genes in colorectal cancer. Three glycolysis-associated clusters (GAC) were identified with distinct clinical, genomic, and tumor microenvironment (TME). By mapping GAC to single-cell RNA sequencing analysis (scRNA-seq), we next discovered that the immune infiltration profile of GACs was similar to that of bulk RNA sequencing analysis (bulk RNA-seq). In order to determine the kind of GAC for each sample, we developed the GAC predictor using markers of single cells and GACs that were most pertinent to clinical prognostic indications. Additionally, potential drugs for each GAC were discovered using different algorithms.

Results

GAC1 was comparable to the immune-desert type, with a low mutation probability and a relatively general prognosis; GAC2 was more likely to be immune-inflamed/excluded, with more immunosuppressive cells and stromal components, which also carried the risk of the poorest prognosis; Similar to the immune-activated type, GAC3 had a high mutation rate, more active immune cells, and excellent therapeutic potential.

Conclusion

In conclusion, we combined transcriptome and single-cell data to identify new molecular subtypes using glycolysis-related genes in colorectal cancer based on machine-learning methods, which provided therapeutic direction for colorectal patients.

Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis

Objective

Staphylococcus aureus (SA)-induced osteomyelitis (OM) is one of the most common refractory diseases in orthopedics. Early diagnosis is beneficial to improve the prognosis of patients. Ferroptosis plays a key role in inflammation and immune response, while the mechanism of ferroptosis-related genes (FRGs) in SA-induced OM is still unclear. The purpose of this study was to determine the role of ferroptosis-related genes in the diagnosis, molecular classification and immune infiltration of SA-induced OM by bioinformatics.

Methods

Datasets related to SA-induced OM and ferroptosis were collected from the Gene Expression Omnibus (GEO) and ferroptosis databases, respectively. The least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were combined to screen out differentially expressed-FRGs (DE-FRGs) with diagnostic characteristics, and gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to explore specific biological functions and pathways. Based on these key DE-FRGs, a diagnostic model was established, and molecular subtypes were divided to explore the changes in the immune microenvironment between molecular subtypes.

Results

A total of 41 DE-FRGs were identified. After screening and intersecting with LASSO and SVM-RFE algorithms, 8 key DE-FRGs with diagnostic characteristics were obtained, which may regulate the pathogenesis of OM through the immune response and amino acid metabolism. The ROC curve indicated that the 8 DE-FRGs had excellent diagnostic ability for SA-induced OM (AUC=0.993). Two different molecular subtypes (subtype 1 and subtype 2) were identified by unsupervised cluster analysis. The CIBERSORT analysis revealed that the subtype 1 OM had higher immune cell infiltration rates, mainly in T cells CD4 memory resting, macrophages M0, macrophages M2, dendritic cells resting, and dendritic cells activated.

Conclusion

We developed a diagnostic model related to ferroptosis and molecular subtypes significantly related to immune infiltration, which may provide a novel insight for exploring the pathogenesis and immunotherapy of SA-induced OM.

A Novel Molecular Signature of Cancer-Associated Fibroblasts Predicts Prognosis and Immunotherapy Response in Pancreatic Cancer

Cancer-associated fibroblasts (CAFs), a prominent population of stromal cells, play a crucial role in tumor progression, prognosis, and treatment response. However, the relationship among CAF-based molecular signatures, clinical outcomes, and tumor microenvironment infiltration remains largely elusive in pancreatic cancer (PC). Here, we collected multicenter PC data and performed integrated analysis to investigate the role of CAF-related genes (CRGs) in PC. Firstly, we demonstrated that α-SMA⁺ CAFs were the most prominent stromal components and correlated with the poor survival rates of PC patients in our tissue microarrays. Then, we discriminated two diverse molecular subtypes (CAF clusters A and B) and revealed the significant differences in the tumor immune microenvironment (TME), four reported CAF subpopulations, clinical characteristics, and prognosis in PC samples. Furthermore, we analyzed their association with the immunotherapy response of PC patients. Lastly, a CRG score was constructed to predict prognosis, immunotherapy responses, and chemosensitivity in pancreatic cancer patients. In summary, these findings provide insights into further research targeting CAFs and their TME, and they pave a new road for the prognosis evaluation and individualized treatment of PC patients.

Diagnostic signature, subtype classification, and immune infiltration of key m6A regulators in osteomyelitis patients

Background: As a recurrent inflammatory bone disease, the treatment of osteomyelitis is always a tricky problem in orthopaedics. N6-methyladenosine (m6A) regulators play significant roles in immune and inflammatory responses. Nevertheless, the function of m6A modification in osteomyelitis remains unclear. Methods: Based on the key m6A regulators selected by the GSE16129 dataset, a nomogram model was established to predict the incidence of osteomyelitis by using the random forest (RF) method. Through unsupervised clustering, osteomyelitis patients were divided into two m6A subtypes, and the immune infiltration of these subtypes was further evaluated. Validating the accuracy of the diagnostic model for osteomyelitis and the consistency of clustering based on the GSE30119 dataset. Results: 3 writers of Methyltransferase-like 3 (METTL3), RNA-binding motif protein 15B (RBM15B) and Casitas B-lineage proto-oncogene like 1 (CBLL1) and three readers of YT521-B homology domain-containing protein 1 (YTHDC1), YT521-B homology domain-containing family 3 (YTHDF2) and Leucine-rich PPR motif-containing protein (LRPPRC) were identified by difference analysis, and their Mean Decrease Gini (MDG) scores were all greater than 10. Based on these 6 significant m6A regulators, a nomogram model was developed to predict the incidence of osteomyelitis, and the fitting curve indicated a high degree of fit in both the test and validation groups. Two m6A subtypes (cluster A and cluster B) were identified by the unsupervised clustering method, and there were significant differences in m6A scores and the abundance of immune infiltration between the two m6A subtypes. Among them, two m6A regulators (METTL3 and LRPPRC) were closely related to immune infiltration in patients with osteomyelitis. Conclusion: m6A regulators play key roles in the molecular subtypes and immune response of osteomyelitis, which may provide assistance for personalized immunotherapy in patients with osteomyelitis.

Crosstalk of angiogenesis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in colorectal adenocarcinoma

Background

Colorectal adenocarcinoma (COAD) is one of the most common malignancies and angiogenesis is vital to the development of cancer. Here, we explored the roles of angiogenesis-related genes (ARGs) that affect the prognosis of COAD and constructed risk models to assess patient prognosis, immune characteristics, and treatment outcomes.

Methods

We comprehensively characterized the transcriptional and genetic modifications of 48 ARGs in COAD and evaluated the expression patterns. We identified two ARG subgroups using the consensus clustering algorithm. Based on the differentially expressed genes (DEGs) of two ARG subtypes, we calculated risk score, namely ARG_scores, and calssified COAD patients into different risk groups. To investigate the expression of ARG_score-related genes, qRT-PCR was performed. Subsequently, we mapped the nomogram to visually and accurately describe the value of the application of ARG_score. Finally, the correlation between ARG_score and clinical features, immune infiltration along with drug sensitivity were explored.

Results

We identified two ARG related subgroups and there were great differences in overall survival (OS) and tumor microenvironment. Then, we created an ARG_score for predicting overall survival based on eight DEGs and confirmed its reliable predictive power in COAD patients, with higher ARG_score associated with worse prognosis. Furthermore, eight ARG_score-related genes expression was investigated by qRT-PCR. To make the ARG_score clinically feasible, we created a highly reliable nomogram. We also found a higher proportion of microsatellite instability-high (MSI-H) and higher tumor mutational burden (TMB) in the high-risk group. In addition, ARG_score was notably correlated with cancer stem cell indices and drug sensitivity.

Conclusion

This scoring model has potential clinical application value in the prognosis, immune microenvironment and therapeutic drug sensitivity of COAD, which provides new insights for personalized treatment.

Construction of a mitochondrial dysfunction related signature of diagnosed model to obstructive sleep apnea

Background: The molecular mechanisms underlying obstructive sleep apnea (OSA) and its comorbidities may involve mitochondrial dysfunction. However, very little is known about the relationships between mitochondrial dysfunction-related genes and OSA. Methods: Mitochondrial dysfunction-related differentially expressed genes (DEGs) between OSA and control adipose tissue samples were identified using data from the Gene Expression Omnibus database and information on mitochondrial dysfunction-related genes from the GeneCards database. A mitochondrial dysfunction-related signature of diagnostic model was established using least absolute shrinkage and selection operator Cox regression and then verified. Additionally, consensus clustering algorithms were used to conduct an unsupervised cluster analysis. A protein-protein interaction network of the DEGs between the mitochondrial dysfunction-related clusters was constructed using STRING database and the hub genes were identified. Functional analyses, including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA), were conducted to explore the mechanisms involved in mitochondrial dysfunction in OSA. Immune cell infiltration analyses were conducted using CIBERSORT and single-sample GSEA (ssGSEA). Results: we established mitochondrial dysfunction related four-gene signature of diagnostic model consisted of NPR3, PDIA3, SLPI, ERAP2, and which could easily distinguish between OSA patients and controls. In addition, based on mitochondrial dysfunction-related gene expression, we identified two clusters among all the samples and three clusters among the OSA samples. A total of 10 hub genes were selected from the PPI network of DEGs between the two mitochondrial dysfunction-related clusters. There were correlations between the 10 hub genes and the 4 diagnostic genes. Enrichment analyses suggested that autophagy, inflammation pathways, and immune pathways are crucial in mitochondrial dysfunction in OSA. Plasma cells and M0 and M1 macrophages were significantly different between the OSA and control samples, while several immune cell types, especially T cells (γ/δ T cells, natural killer T cells, regulatory T cells, and type 17 T helper cells), were significantly different among mitochondrial dysfunction-related clusters of OSA samples. Conclusion: A novel mitochondrial dysfunction-related four-gen signature of diagnostic model was built. The genes are potential biomarkers for OSA and may play important roles in the development of OSA complications.

Construction of m6A-based prognosis signature and prediction for immune and anti-angiogenic response

Background: Increasing evidence illustrated that m6A regulator-mediated modification plays a crucial role in regulating tumor immune and angiogenesis microenvironment. And the combination of immune checkpoint inhibitor and anti-angiogenic therapy has been approved as new first-line therapy for advanced HCC. This study constructed a novel prognosis signature base on m6A-mediated modification and explored the related mechanism in predicting immune and anti-angiogenic responses.

Methods: Gene expression profiles and clinical information were collected from TCGA and GEO. The ssGSEA, MCPCOUNT, and TIMER 2.0 algorithm was used to Estimation of immune cell infiltration. The IC₅₀ of anti-angiogenic drugs in GDSC was calculated by the “pRRophetic” package. IMvigor210 cohort and Liu et al. cohort were used to validate the capability of immunotherapy response. Hepatocellular carcinoma single immune cells sequencing datasets GSE140228 were collected to present the expression landscapes of 5 hub genes in different sites and immune cell subpopulations of HCC patients.

Results: Three m6A clusters with distinct immune and angiogenesis microenvironments were identified by consistent cluster analysis based on the expression of m6A regulators. We further constructed a 5-gene prognosis signature (termed as m6Asig-Score) which could predict both immune and anti-angiogenic responses. We illustrated that high m6Asig-Score is associated with poor prognosis, advanced TNM stage, and high TP53 mutation frequency. Besides, the m6Asig-Score was negatively associated with immune checkpoint inhibitors and anti-angiogenic drug response. We further found that two of the five m6Asig-Score inner genes, B2M and SMOX, were associated with immune cell infiltration, immune response, and the sensitivity to sorafenib, which were validated in two independent immunotherapy cohorts and the Genomics of Drug Sensitivity in Cancer (GDSC) database.

Conclusion: We constructed a novel prognosis signature and identified B2M and SMOX for predicting immune and anti-angiogenic efficacy in HCC, which may guide the combined treatment strategies of immunotherapy and anti-angiogenic therapy in HCC.

A novel angiogenesis-based molecular signature related to prognosis and tumor immune interactions of pancreatic cancer

Angiogenesis, a hallmark of cancer, is related to prognosis, tumor progression, and treatment response. Nevertheless, the correlation of angiogenesis-based molecular signature with clinical outcome and immune cell infiltration has not been thoroughly studied in pancreatic cancer. In this study, multiple bioinformatics methods were combined to evaluate prognosis, immune cell infiltration, and the alterations of angiogenesis-related genes (ARGs) in PC samples, and further establish a novel angiogenesis-related gene signature. Moreover, the protein and mRNA expression levels of four angiogenesis risk genes were determined by Human Protein Atlas (HPA) database and qPCR analysis, respectively. Here, we recognized two distinct angiogenesis subtypes and two gene subtypes, and revealed the critical roles of ARGs in the tumor immune microenvironment (TIME), clinical features, and prognosis. Consequently, we established an ARGs score to predict prognosis and therapeutic response of PC patients, and validated its robust predictive ability. Additionally, the ARGs score was markedly associated with clinical outcomes, tumor mutation burden (TMB), and chemotherapeutic drug sensitivity. In brief, our findings imply that the ARGs score is a robust prognostic indicator and may contribute to the development of effective individualized therapies for PC.

LncRNA MALAT1 regulates METTL3-mediated PD-L1 expression and immune infiltrates in pancreatic cancer

Pancreatic cancer is the fourth leading cause of cancer death in the United States. The main methods of treating pancreatic cancer are surgery and chemotherapy, but the treatment efficacy is low with a poor prognosis. Immunotherapy represented by PD-1/PD-L1 has brought a milestone progress in the treatment of pancreatic cancer. However, the unique tumor microenvironment of pancreatic cancer presents challenges for immunotherapy. In addition, m6A is a common RNA modification and a potential molecular target in tumor therapy. The expression pattern of m6A in pancreatic cancer is still unclear. LncRNAs also play an essential role in pancreatic cancer development and treatment. In this study, we found that some m6A regulators were significantly elevated in pancreatic cancer and associated with the expression of PD-1/PD-L1. Moreover, we observed that METTL3 can increase the expression of PD-L1. Notably, METTL3 positively regulates the expression of lncRNA MALAT1 in pancreatic cancer cells. Strikingly, lncRNA MALAT1 increased the expression of PD-L1 in pancreatic cancer cells. This finding indicated that METTL3 regulated the expression of PD-L1 possibly via targeting lncRNA MALAT1 in pancreatic cancer cells. Lastly, MALAT1 governed the viability of pancreatic cancer cells. Taken together, lncRNA MALAT1 is involved in METTL3-mediated promotion of PD-L1 expression in pancreatic cancer.

Molecular characteristics, clinical significance, and cancer immune interactions of cuproptosis and ferroptosis-associated genes in colorectal cancer

Objective

To systematically analyze the expression of cuproptosis and ferroptosis genes and their impact on the development, prognosis, tumor microenvironment (TME), and treatment response in colorectal cancer (CRC) patients

Methods

We systematically evaluated 33 cuproptosis and ferroptosis-related genes and comprehensively identified the correlations between cuproptosis and ferroptosis-related genes and transcriptional patterns, prognosis, and clinical features. Three distinct subgroups were identified in CRC using the TCGA database and the GEO database. We next assessed the relationship between the molecular features, prognostic significance, and clinical indicators of the prognostic genes in the cuproptosis and ferroptosis-related gene clusters. In addition, a PAC_score, which accurately predicted the prognosis of CRC patients and the efficacy of immunomodulatory mAbs, was obtained.

Results

Patients in the low expression group (low expression of cuproptosis and ferroptosis-related genes) had a longer survival compared to the high expression group. We identified two distinct prognosis-associated molecular subtypes and observed an association between clinical information and prognosis. The enrichment analysis of differential genes associated with prognosis showed that the main enrichment was related to biological processes such as metastasis and metabolism. Next, the PCA_score for predicting overall survival (OS) was established and its reliable predictive value in CRC patients was confirmed. Furthermore, highly reliable nomogram was created to facilitate the clinical feasibility of the PCA_score. It was found that the immunomodulatory mAbs, PD-L1 and CTLA4 were highly expressed in the low PCA_score score group with statistically significance.

Conclusion

Overall, the PCA scores of prognostic differential genes in the cuproptosis and ferroptosis-related gene clusters were strongly associated with clinical characteristics, prognosis, and immunotherapy in CRC patients. This data may promote further exploration of more effective immunotherapy strategies for CRC.

The cuproptosis-related signature associated with the tumor environment and prognosis of patients with glioma

Background

Copper ions are essential for cellular physiology. Cuproptosis is a novel method of copper-dependent cell death, and the cuproptosis-based signature for glioma remains less studied.

Methods

Several glioma datasets with clinicopathological information were collected from TCGA, GEO and CGGA. Robust Multichip Average (RMA) algorithm was used for background correction and normalization, cuproptosis-related genes (CRGs) were then collected. The TCGA-glioma cohort was clustered using ConsensusClusterPlus. Univariate Cox regression analysis and the Random Survival Forest model were performed on the differentially expressed genes to identify prognostic genes. The cuproptosis-signature was constructed by calculating CuproptosisScore using Multivariate Cox regression analysis. Differences in terms of genomic mutation, tumor microenvironment, and enrichment pathways were evaluated between high- or low-CuproptosisScore. Furthermore, drug response prediction was carried out utilizing pRRophetic.

Results

Two subclusters based on CRGs were identified. Patients in cluster2 had better clinical outcomes. The cuproptosis-signature was constructed based on CuproptosisScore. Patients with higher CuproptosisScore had higher WHO grades and worse prognosis, while patients with lower grades were more likely to develop IDH mutations or MGMT methylation. Univariate and Multivariate Cox regression analysis demonstrated CuproptosisScore was an independent prognostic factor. The accuracy of the signature in prognostic prediction was further confirmed in 11 external validation datasets. In groups with high-CuproptosisScore, PIK3CA, MUC16, NF1, TTN, TP53, PTEN, and EGFR showed high mutation frequency. IDH1, TP53, ATRX, CIC, and FUBP1 demonstrated high mutation frequency in low-CuproptosisScore group. The level of immune infiltration increased as CuproptosisScore increased. SubMap analysis revealed patients with high-CuproptosisScore may respond to anti-PD-1 therapy. The IC50 values of Bexarotene, Bicalutamide, Bortezomib, and Cytarabine were lower in the high-CuproptosisScore group than those in the low-CuproptosisScore group. Finally, the importance of IGFBP2 in TCGA-glioma cohort was confirmed.

Conclusion

The current study revealed the novel cuproptosis-based signature might help predict the prognosis, biological features, and appropriate treatment for patients with glioma.

Characterization of tumor immune microenvironment and cancer therapy for head and neck squamous cell carcinoma through identification of a genomic instability-related lncRNA prognostic signature

Head and neck squamous cell carcinoma (HNSCC) represents one of the most prevalent and malignant tumors of epithelial origins with unfavorable outcomes. Increasing evidence has shown that dysregulated long non-coding RNAs (lncRNAs) correlate with tumorigenesis and genomic instability (GI), while the roles of GI-related lncRNAs in the tumor immune microenvironment (TIME) and predicting cancer therapy are still yet to be clarified. In this study, transcriptome and somatic mutation profiles with clinical parameters were obtained from the TCGA database. Patients were classified into GI-like and genomic stable (GS)-like groups according to the top 25% and bottom 25% cumulative counts of somatic mutations. Differentially expressed lncRNAs (DElncRNAs) between GI- and GS-like groups were identified as GI-related lncRNAs. These lncRNA-related coding genes were enriched in cancer-related KEGG pathways. Patients totaling 499 with clinical information were randomly divided into the training and validation sets. A total of 18 DElncRNAs screened by univariate Cox regression analysis were associated with overall survival (OS) in the training set. A GI-related lncRNA signature that comprised 10 DElncRNAs was generated through least absolute shrinkage and selection operator (Lasso)-Cox regression analysis. Patients in the high-risk group have significantly decreased OS vs. patients in the low-risk group, which was verified in internal validation and entire HNSCC sets. Integrated HNSCC sets from GEO confirmed the notable survival stratification of the signature. The time-dependent receiver operating characteristic curve demonstrated that the signature was reliable. In addition, the signature retained a strong performance of OS prediction for patients with various clinicopathological features. Cell composition analysis showed high anti-tumor immunity in the low-risk group which was evidenced by increased infiltrating CD8+ T cells and natural killer cells and reduced cancer-associated fibroblasts, which was convinced by immune signatures analysis via ssGSEA algorithm. T helper/IFNγ signaling, co-stimulatory, and co-inhibitory signatures showed increased expression in the low-risk group. Low-risk patients were predicted to be beneficial to immunotherapy, which was confirmed by patients with progressive disease who had high risk scores vs. complete remission patients. Furthermore, the drugs that might be sensitive to HNSCC were identified. In summary, the novel prognostic GILncRNA signature provided a promising approach for characterizing the TIME and predicting therapeutic strategies for HNSCC patients.

Consensus clustering of gene expression profiles in peripheral blood of acute ischemic stroke patients

Acute ischemic stroke (AIS) is a primary cause of mortality and morbidity worldwide. Currently, no clinically approved immune intervention is available for AIS treatment, partly due to the lack of relevant patient classification based on the peripheral immunity status of patients with AIS. In this study, we adopted the consensus clustering approach to classify patients with AIS into molecular subgroups based on the transcriptomic profiles of peripheral blood, and we identified three distinct AIS molecular subgroups and 8 modules in each subgroup by the weighted gene co-expression network analysis. Remarkably, the pre-ranked gene set enrichment analysis revealed that the co-expression modules with subgroup I-specific signature genes significantly overlapped with the differentially expressed genes in AIS patients with hemorrhagic transformation (HT). With respect to subgroup II, exclusively male patients with decreased proteasome activity were identified. Intriguingly, the majority of subgroup III was composed of female patients who showed a comparatively lower level of AIS-induced immunosuppression (AIIS). In addition, we discovered a non-linear relationship between female age and subgroup-specific gene expression, suggesting a gender- and age-dependent alteration of peripheral immunity. Taken together, our novel AIS classification approach could facilitate immunomodulatory therapies, including the administration of gender-specific therapeutics, and attenuation of the risk of HT and AIIS after ischemic stroke.

Exploring the relationship between pyroptosis, infiltrating immune cells and Kawasaki disease with resistance to intravenous immunoglobulin (IVIG) via bioinformatic analysis

BACKGROUND

Kawasaki disease (KD) is a kind of vasculitis predominantly afflicting children younger than five. Although intravenous immunoglobulin (IVIG) has been regarded as the first-line therapy, there are some children unresponsive to it, resulting in higher risk of coronary artery aneurysms (CAA), the most severe complication of KD. Pyroptosis is an inflammatory apoptosis, which resembles the traits of IVIG-resistance. Therefore, our research aims to find relationships between KD with IVIG-resistance and pyroptosis, and provide the underlying mechanisms of IVIG-resistance.

METHODS

The transcriptome data of three datasets were downloaded from Gene Expression Omnibus (GEO) database. CIBERSORTx and WGCNA were combined to identify the coexpression gene network correlated with the up-regulated immune cells in KD, using differentially expressed genes (DEGs) overlapped in GSE68004 and GSE73461. The key genes in hub module were intersected with pyroptosis-related genes (PRGs). Then KD patients were divided into subgroups according to the expression of remaining genes, along with the construction of risk score (RS) based on the least absolute shrinkage and selection operator (LASSO) regression analysis. Besides, we explored the clinical value of RS between IVIG-responsive and -resistant KD patients in GSE16797. In addition, the biological pathways between subgroups were evaluated using Gene Set Variation Analysis (GSVA).

RESULTS

A total of 4246 DEGs and three immune cells, including Monocytes, M0 macrophage, and neutrophils, were analyzed with P < 0.05 between KD and healthy controls (HCs). The lightcyan module was the hub module based on WGCNA, and only NLRC4, CASP1, CASP4, GSDMD, IL1B and PYCARD in the hub module were overlapped with PRGs. Then KD patients in GSE68004 were stratified into two clusters on the basis of the expression levels of six genes. RS was built with five out of six genes (exclude PYCARD) according to the LASSO analysis, which could differentiate C1 from C2, IVIG-responsive from -resistant KD patients. Besides, the high-risk group (C1) tended to be with increased levels of inflammation, immune responses and infiltration of neutrophils according to the analysis of GSVA and CIBERSORTx.

CONCLUSION

We built a pyroptosis-related RS to evaluate the degree of pyroptosis and infiltrating immune cells in subgroups of KD, and associated it with the responsiveness to IVIG, which might help us to further understand the pathological process during IVIG-nonresponse.

Association of aging-related genes with prognosis and immune infiltration in pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is one of the deadliest malignancies. Aging is described as the degeneration of physiological function, which is complexly correlated with cancer. It is significant to explore the influences of aging-related genes (ARGs) on PAAD. Based on The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets, we used univariate Cox regression analysis and acquired eight differentially expressed ARGs with prognostic values. Two molecular subtypes were identified based on these ARGs to depict PAAD patients’ overall survival (OS) and immune microenvironments preliminarily. Cluster 1 had a poor OS as well as a worse immune microenvironment. Through least absolute shrinkage and selection operator (LASSO) regression analysis, we constructed a seven-ARG risk signature based on the TCGA dataset and verified it in Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) to predict the prognoses, immune microenvironments, signal pathways, tumor mutations, and drug sensitivity of PAAD patients. The high-risk group possessed an unfavorable OS compared with that of the low-risk group. We also verified the independence and clinical availability of the risk signature by Cox regression analyses and the establishment of a nomogram, respectively. The higher risk score was associated with several clinical factors such as higher grade and advanced tumor stage as well as lower immunoscore and cluster 1. The negative associations of risk scores with immune, stroma, and estimate scores proved the terrible immune microenvironment in the high-risk group. Relationships between risk score and immune checkpoint gene expression as well as signal pathways provided several therapeutic targets. PAAD patients in the low-risk group possessed lower tumor mutations as well as a higher susceptibility to axitinib and vorinostat. The high-risk group bore a higher TMB and cisplatin and dasatinib may be better options. We used immunohistochemistry and qPCR to confirm the expression of key ARGs with their influences on OS. In conclusion, we identified two ARG-mediated molecular subtypes and a novel seven-ARG risk signature to predict prognoses, immune microenvironments, signal pathways, tumor mutations, and drug sensitivity of PAAD patients.

METTL3 Is Associated With the Malignancy of Esophageal Squamous Cell Carcinoma and Serves as a Potential Immunotherapy Biomarker

Methyltransferase-like 3 (METTL3) is an RNA methyltransferase mediating N6 methyladenosine (m6A) modification. Its role in cancer pathogenesis and progression has attracted increasing attention. However, the immunological role, possible immune mechanism, and clinical significance of METTL3 in esophageal squamous cell carcinoma (ESCC) remain to be confirmed. The Tumor Genome Atlas (TCGA) provided clinical and transcriptome sequencing data for this study (162 tumor tissue samples and 11 normal tissue samples), while the Immunology Database and Analysis Portal (immport, https://www.immport.org/home) provided 2483 immune-related genes. METTL3 was substantially expressed in ESCC and linked to poor prognosis in ESCC, according to the findings. Functional analysis revealed that METTL3 is mainly involved in chromosomal homologous recombination and DNA mismatch repair processes, which could be potential mechanisms for tumor disease development and progression. Analysis on the TISIDB website shows that effector memory CD8 T cells, NK cells, neutrophils and other cells are highly correlated with METTL3 expression. We screened immune genes associated with METTL3 by Spearman’s analysis and performed functional analysis. These immune genes were mostly linked with immune processes, such as cytokine receptors, the MAPK signaling pathway, and natural killer cell-mediated cytotoxicity, indicating that METTL3 is a key molecule in the immune regulation of esophageal cancer. In addition, based on METTL3-related immune genes, we separated the patients into several subgroups and constructed a prognostic prediction model consisting of six immune genes. As an independent prognostic indicator for ESCC, the risk score of this model can be employed. A nomogram was also developed to accurately evaluate individual prognoses based on clinical indicators and risk scores. In summary, this study suggests that METTL3 is not only a potential pathogenic molecule for esophageal carcinogenesis and progression but also a potential biological marker for forecasting ESCC patient prognosis and could serve as a basis for clinical decision making.

Molecular Characteristics, Clinical Significance, and Cancer Immune Interactions of Angiogenesis-Associated Genes in Gastric Cancer

Background

Immunotherapy has evolved as a critical option to treat diverse cancers. The active response to immunotherapy relies on the unique interaction between cancer and the tumor microenvironment (TME). Angiogenesis is one of the hallmarks of cancer. However, the association between angiogenesis and clinical outcome, immune cell infiltration, and immunotherapy remains unknown in gastric cancer (GC).

Methods

We systematically assessed 36 angiogenesis-associated genes (AAGs) and comprehensively identified the correlation between angiogenesis and transcriptional patterns, prognosis, and immune cell infiltration. The AAG_score was applied to quantify the angiogenesis subtypes of each patient. We then evaluated their values in prognostic prediction and therapeutic responses in GC.

Results

We discussed the mutations of AAGs in GC specimens from genetic levels and identified their expression patterns from TCGA and GEO cohorts. We determined two different molecular subtypes and observed that AAG mutations were related to patients’ clinicopathological characteristics, prognosis, and infiltrating TME. Next, an AAG_score for predicting overall survival (OS) was established and its reliable predictive ability in GC patients was confirmed. Furthermore, we created a highly reliable nomogram to facilitate the clinical viability of the AAG_score. A low AAG_score, characterized by elevated microsatellite instability-high, mutation burden, and immune activation, demonstrated a superior OS. Additionally, the AAG_score was remarkedly correlated with the cancer stem cell index and drug susceptibility.

Conclusion

Collectively, we identified a prognostic AAG signature for GC patients. This signature may contribute to clarifying the characteristics of TME and enable the exploration of more potent immunotherapy strategies.

Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer

Purpose

The hypoxic microenvironment is involved in the tumorigenesis of ovarian cancer (OC). Therefore, we aim to develop a non-invasive radiogenomics approach to identify a hypoxia pattern with potential application in patient prognostication.

Methods

Specific hypoxia-related genes (sHRGs) were identified based on RNA-seq of OC cell lines cultured with different oxygen conditions. Meanwhile, multiple hypoxia-related subtypes were identified by unsupervised consensus analysis and LASSO–Cox regression analysis. Subsequently, diversified bioinformatics algorithms were used to explore the immune microenvironment, prognosis, biological pathway alteration, and drug sensitivity among different subtypes. Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.

Results

One hundred forty sHRGs and three types of hypoxia-related subtypes were identified. Among them, hypoxia-cluster-B, gene-cluster-B, and high-risk subtypes had poor survival outcomes. The subtypes were closely related to each other, and hypoxia-cluster-B and gene-cluster-B had higher hypoxia risk scores. Notably, the low-risk subtype had an active immune microenvironment and may benefit from immunotherapy. Finally, a four-feature radiogenomics model was constructed to reveal hypoxia risk status, and the model achieved area under the curve (AUC) values of 0.900 and 0.703 for the training and testing cohorts, respectively.

Conclusion

As a non-invasive approach, computed tomography-based radiogenomics biomarkers may enable the pretreatment prediction of the hypoxia pattern, prognosis, therapeutic effect, and immune microenvironment in patients with OC.

Analysis of Ferroptosis-Related LncRNAs Signatures Associated with Tumor Immune Infiltration and Experimental Validation in Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most aggressive subtype of renal cell carcinoma. Ferroptosis is an iron-dependent programmed cell death. Long non-coding RNAs (lncRNAs) emerge as a critical role in regulating cancer progression.

Objective

This study aimed to identify molecular regulation of ferroptosis-related lncRNAs (FRLs) in ccRCC.

Methods

The prognostic value of FRLs was investigated in ccRCC samples downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. The FRLs were screened out by Pearson correlation test. The 465 FRLs confirmed as potential prognostic factors through univariate Cox regression analysis were entered into Lasso and multivariate Cox regression to build a FRLs prognostic signature. A risk score based on the prognostic model divided ccRCC patients into low- and high-risk groups. A prognostic nomogram, derived from the prognostic signature and integrating clinical characteristics, was constructed. Gene set enrichment analysis (GSEA) revealed the immune- and tumor-associated pathways. Two distinct clusters were identified with different immune signatures through consensus clustering analysis. The prognostic value of some hub FRLs was externally validated via three GEO datasets (GSE46699, GSE53757 and GSE66272) and online databases. Finally, the three FRLs (LINC00460, LINC00941 and LINC02027) were verified through in vitro experiments.

Results

The FRLs prognostic signature, including 7 independent prognostic lncRNAs, exhibited good accuracy in predicting overall survival (OS) of ccRCC patients. This signature was correlated with immune infiltration and immune checkpoint blockade (ICB). We correlated two distinct clusters with immune infiltration signature of ccRCC. The worse prognosis of cluster 2 was probably mediated by immune evasion. We also found that the expression levels of LINC00460 and LINC00941 in ccRCC cell lines were higher than those in HK-2 cells, but LINC02027 showed the inverse trend.

Conclusion

Collectively, our study demonstrated a FRLs prognostic signature which had great clinical value in prognosis assessment.

Immune Score-based Molecular Subtypes and Signature Associated with Clinical Outcome in Hepatoblastoma

Background: This study aimed to identify genes related to the immune score of hepatoblastoma, examine the characteristics of the immune microenvironment of hepatoblastoma, and construct a risk scoring system for predicting the prognosis of hepatoblastoma. Methods: Through using the gene chip data of patients with hepatoblastoma with survival data in the ArrayExpress and GEO databases, the immune score of hepatoblastoma was calculated by the ESITIMATE algorithm, and the prognostic value of immune score in patients with hepatoblastoma was studied by the survival analysis. Genes related to the immune score were identified by the WGCNA algorithm. According to these genes, patients with hepatoblastoma were clustered unsupervised. Finally, the risk scoring system was constructed according to the immune score-related genes. Results: The immune score calculated by the ESTIMATE algorithm had a good prognostic value in patients with hepatoblastoma. Patients with high immune scores had better OS than those with low immune scores (P < 0.001). A total of 146 immune score-related genes were identified by WGCNA analysis, and univariate COX regression analysis indicated that 59 of the genes had prognostic value. According to the unsupervised clustering results of the 146 immune score-related genes, patients with hepatoblastoma could be divided into two subtypes with different prognoses, namely molecular subtype 1 and subtype 2, with molecular subtype 1 having a better prognosis. The immunocyte infiltration analysis results showed that the difference between the two subtypes was mainly in activated CD4 T cells, activated dendritic cells, CD56 bright natural killer cells, the macrophage, and regulatory T cells. According to the immune score-related genes, a risk scoring system was constructed based on a five-gene signature. After the cut-off value was determined, patients with hepatoblastoma were divided into a high-risk group and a low-risk group. The prognosis of the two groups was different. Conclusions: The immune score has a good prognostic value in patients with hepatoblastoma. Based on the different expression patterns of immune score-related genes, hepatoblastoma can be divided into two different prognostic molecular subtypes, showing different immunocyte infiltration patterns. The established risk scoring system based on a five-gene signature has a good predictive value in patients with hepatoblastoma.

Identification and validation of an autophagy-related signature for predicting survival in lower-grade glioma

Abnormal levels of autophagy have been implicated in the pathogenesis of multiple diseases, including cancer. However, little is known about the role of autophagy-related genes (ARGs) in low-grade gliomas (LGG). Accordingly, the aims of this study were to assess the prognostic values of ARGs and to establish a genetic signature for LGG prognosis. Expression profile data from patients with and without primary LGG were obtained from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression databases, respectively, and consensus clustering was used to identify clusters of patients with distinct prognoses. Nineteen differentially expressed ARGs were selected with threshold values of FDR < 0.05 and |log2 fold change (FC)| ≥ 2, and functional analysis revealed that these genes were associated with autophagy processes as expected. An autophagy-related signature was established using a Cox regression model of six ARGs that separated patients from TCGA training cohort into high- and low-risk groups. Univariate and multivariate Cox regression analysis indicated that the signature-based risk score was an independent prognostic factor. The signature was successfully validated using the TCGA testing, TCGA entire, and Chinese Glioma Genome Atlas cohorts. Stratified analyses demonstrated that the signature was associated with clinical features and prognosis, and gene set enrichment analysis revealed that autophagy- and cancer-related pathways were more enriched in high-risk patients than in low-risk patients. The prognostic value and expression of the six signature-related genes were also investigated. Thus, the present study constructed and validated an autophagy-related prognostic signature that could optimize individualized survival prediction in LGG patients.

Quantitative Proteomics Identifies Secreted Diagnostic Biomarkers as well as Tumor-Dependent Prognostic Targets for Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the third most common and most malignant urological cancer, with a 5-year survival rate of 10% for patients with advanced tumors. Here, we identified 10,160 unique proteins by in-depth quantitative proteomics, of which 955 proteins were significantly regulated between tumor and normal adjacent tissues. We verified four putatively secreted biomarker candidates, namely, PLOD2, FERMT3, SPARC, and SIRPα, as highly expressed proteins that are not affected by intratumor and intertumor heterogeneity. Moreover, SPARC displayed a significant increase in urine samples of patients with ccRCC, making it a promising marker for the detection of the disease in body fluids. Furthermore, based on molecular expression profiles, we propose a biomarker panel for the robust classification of ccRCC tumors into two main clusters, which significantly differed in patient outcome with an almost three times higher risk of death for cluster 1 tumors compared with cluster 2 tumors. Moreover, among the most significant clustering proteins, 13 were targets of repurposed inhibitory FDA-approved drugs. Our rigorous proteomics approach identified promising diagnostic and tumor-discriminative biomarker candidates which can serve as therapeutic targets for the treatment of ccRCC. IMPLICATIONS: Our in-depth quantitative proteomics analysis of ccRCC tissues identifies the putatively secreted protein SPARC as a promising urine biomarker and reveals two molecular tumor phenotypes.

Epigenetic alterations are associated with tumor mutation burden in non-small cell lung cancer

Background

To profile genomic and epigenomic of a naïve Chinese non-small cell lung cancer (NSCLC) cohort and investigate the association between tumor mutation burden (TMB) and DNA methylation (DNAm) to explore potential alternative/complimentary biomarkers for NSCLC immunotherapies.

Methods

A total of 89 tumor tissues with matched normal tissues from Chinese NSCLC patients were collected and subjected to whole exome sequencing (WES). From comparison, each patient was evaluated for the TMB value and divided into high, medium and low TMB based on TMB tertile distribution and then relatively high and low TMB samples were selected and subjected to DNAm profiling.

Results

Patients in the low (n = 30), medium (n = 29), and high (n = 30) TMB tertiles had 1.1–2.5, 2.5–4.1, and 4.2–13.9 mutations/Mb, respectively. A statistical directly association between differential methylation probes (DMPs) and TMB level was observed in our cohort (r = 0.63, P value =0.0003) and this was confirmed by using TCGA NSCLC dataset (r = 0.43, P value =0.006). Relatively high TMB group (n = 16, 7.5–13.9 mutations/Mb) harbors more differential DMPs while less in relatively low TMB group (n = 13, 1.1–2.4 mutations/Mb). Eight hundred fifty-eight differential methylation regions (DMRs) were found in relatively high TMB group. In addition, 437 genes show DNAm aberrance status in high TMB patient group and 99 have been reported as its association with lung cancer.

Conclusion

To our knowledge, this is the first report for direct link between the methylome alterations and TMB in NSCLCs. High TMB NSCLCs had more DNAm aberrance and copy number variations (CNVs). In addition, the TMB distribution of Chinese NSCLCs population is lower than that of TCGA.

Electronic supplementary material

The online version of this article (10.1186/s40425-019-0660-7) contains supplementary material, which is available to authorized users.

Comprehensive clinical profiling of the Gauting locoregional lung adenocarcinoma donors

A comprehensive characterization of lung adenocarcinoma (LADC) clinical features is currently missing. We prospectively evaluated Caucasian patients with early‐stage LADC. Patients with LADC diagnosed between 2011 and 2015 were prospectively assessed for lung resection with curative intent. Fifty clinical, pathologic, radiologic, and molecular variables were recorded. Patients were followed till death/study conclusion. The main findings were compared to a separate cohort from France. Of 1943 patients evaluated, 366 were enrolled (18.8%; 181 female; 75 never‐smokers; 28% of registered Bavarian cases over the study period). Smoking and obstruction were significantly more prevalent in GLAD compared with adult Bavarians (P < 0.0001). Ever‐smoker tumors were preferentially localized to the upper lobes. We observed 120 relapses and 74 deaths over 704 cumulative follow‐up years. Median overall and disease‐free survival were >7.5 and 3.6 years, respectively. Patients aged <45 or >65 years, resected >60 days postdiagnosis, with abnormal FVC/DL_COV_A, N2/N3 stage, or solid histology had significantly decreased survival estimates. These were fit into a weighted locoregional LADC death risk score that outperformed pTNM7 in predicting survival in the GLAD and in our second cohort. We define the clinical gestalt of locoregional LADC and provide a new clinical tool to predict survival, findings that may aid future management and research design.