Using ESTIMATE algorithm to establish an 8-mRNA signature prognosis prediction system and identify immunocyte infiltration-related genes in Pancreatic adenocarcinoma

Crosstalk of lactate metabolism-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in colon adenocarcinoma

Colon adenocarcinoma (COAD) is a common malignant tumor of digestive tract and lactate metabolism has been linked to tumor development and progression. In this study, we sought to investigate the influence of lactate metabolism-related genes (LRGs) prognosis. We also aimed to identify distinct LRG-related clusters and develop a risk signature for assessing patient prognosis, immunological characteristics, and response to therapy. We analyzed data from The Cancer Genome Atlas (TCGA) to reveal the expression and mutational features of LRGs in COAD patients. In the integrated TCGA and GSE39582 cohort, consensus clustering analysis was employed to classify patients into two distinct LRG-related clusters. Using differentially expressed genes (DRGs) from these two clusters, we established a LRG-related gene cluster and prognostic signature which was used to classify patients into high-risk and low-risk groups. An validation cohort was used to validate the predictive ability of risk signature and expression of 6 candidate LRGs was confirmed through quantitative real-time PCR (qRT-PCR). Nomograms were created to visually represent the clinical value of LRG-related signature. Furthermore, we extensively examined differences in immune cell infiltration, tumor mutational load (TMB), microsatellite instability (MSI) and drug sensitivity between two risk groups. Analysis of the integrated TCGA and GSE39582 cohorts revealed two distinct LRG-related clusters and gene clusters with significant differences in overall survival (OS) and tumor microenvironment. We developed a LRG-related signature comprising 6 candidate LRGs that reliably predicted OS and qRT-PCR validation confirmed the expression of LRGs. Based on the median risk score, patients were divided into low-risk and high-risk groups, with low-risk group showing better survival. Furthermore, patients in high-risk group were more sensitive to chemotherapy and associated with higher TMB, higher proportion of MSI-H. Our study provides a valuable method for guiding clinical management and personalized treatment of COAD patients, which offers new insights into individualized treatment strategies, ultimately improving patient outcomes.

Integrated bioinformatics analysis screened the key genes and pathways of idiopathic pulmonary fibrosis

To investigate the molecular mechanisms underlying idiopathic pulmonary fibrosis (IPF), we analyzed the GSE173355 and GSE173356 datasets obtained from the NCBI-GEO database. We identified differentially expressed genes (DEGs) and differentially methylated sites. Functional enrichment analysis was conducted for both DEGs and differentially methylated sites. Functional enrichment analysis was performed for both DEGs and differentially methylated sites, alongside an examination of immune-related scores, proportions, and GSVA enrichment scores of immune cells in IPF versus control samples. An integrated gene-methylation association analysis revealed 8 genes with expression levels negatively influenced by methylation. The Rap1 pathway, Focal adhesion, and Axon guidance were significantly enriched among both DEGs and differentially methylated sites. Immune-related scores were notably lower in the IPF group compared to the control group, with marked differences in immune cell proportions and GSVA enrichment scores. Screening of DEGs identified 361 differentially expressed immune-related genes (IRGs). Protein-protein interaction (PPI) network analysis using the STRING database and Cytoscape software unveiled 10 key genes and 3 core subnetworks. RT-qPCR validation in bleomycin-induced IPF mouse model and A549 EMT model confirmed the reliability of most findings. These results provide new insights into IPF pathogenesis and potential therapeutic strategies, necessitating further functional validation.

Single-cell insights into HNSCC tumor heterogeneity and programmed cell death pathways

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a prevalent malignancy often diagnosed in advanced stages. Despite advancements in therapy, it retains a high mortality rate and significant recurrence risk. This study utilizes single-cell sequencing (scRNA-seq) to unravel HNSCC's complexity, identify therapeutic targets, and refine prognostic models.

METHODS

Pseudotime trajectory and stemness analyses were performed on HNSCC tumor subpopulations, focusing on the C2 MALAT1+ Tumors subpopulation, which had the lowest CytoTRACE Score and represented the Lineage 2 endpoint in Slingshot analysis. The study examined programmed death and metabolic pathways in each subpopulation and developed a novel prognostic model using LASSO regression.

RESULTS

The C2 MALAT1+ Tumors subpopulation exhibited reduced expression of programmed death pathways (e.g., Entotic cell death, Apoptosis, Pyroptosis) and metabolic pathways (e.g., Riboflavin metabolism, Glycolysis/Gluconeogenesis). Key transcription factors included LEF1, RFX3, CREM, MZF1, and ZNF202. Prognostic models based on the MALAT1 Tumors Risk Score (MTRS) revealed worse survival and higher tumor purity in the high MTRS group. Risk genes included ADM, RPL31, EIF5B, and TAF7. Additionally, activated CD4 memory T cells were enriched in the high MTRS group, which also showed greater sensitivity to Cisplatin, Docetaxel, and Paclitaxel.

CONCLUSIONS

ScRNA-seq revealed the heterogeneity of HNSCC subpopulations, highlighting the unique features of the C2 MALAT1+ Tumors subpopulation. This study identified novel prognostic markers and therapeutic targets, offering insights into HNSCC progression, drug resistance, and potential treatments.

A Cuproptosis-Related gene Signature as a Prognostic Biomarker in Thyroid Cancer Based on Transcriptomics

Thyroid cancer (THCA) is the most prevalent endocrine tumor, and its incidence continues to increase every year. However, the processes underlying the aggressive progression of thyroid cancer are unknown. We concentrated on the prognostic and biological importance of thyroid cancer cuproptosis-related genes in this investigation. Genomic and clinical data were obtained from the UCSC XENA website, and cuproptosis-related genes were obtained from the FerrDb website. We performed differential expression analysis and Cox regression analysis to identify possible predictive targets associated with thyroid cancer prognosis. To assess the role of CDKN2A in thyroid cancer and the ability to predict prognosis on the basis of the CDKN2A expression level, we performed immunohistochemical staining, survival analysis, immunological analysis, functional analysis, and clinical analysis with respect to CDKN2A gene expression. CDKN2A expression levels were found to be inversely correlated with thyroid cancer prognosis. Higher levels of CDKN2A expression were associated with higher T, N, and clinicopathological stage and more residual tumor cells. Through univariate and multivariate Cox regression analyses, the CDKN2A expression level was shown to be linked with thyroid cancer patients' overall survival (OS). Moreover, we discovered that CDKN2A expression was linked to a dysfunctional tumor immune microenvironment. The study shows that CDKN2A, a cuproptosis-related gene, can be used as a prognostic marker for thyroid cancer.

Disulfidptosis-based molecular clustering and prognostic signatures predict patient survival and the immune landscape in patients with colon cancer

INTRODUCTION

Disulfidptosis is a unique type of programmed cell death that is distinct from previously known forms of cell death, such as pyroptosis, apoptosis, and necroptosis. Researchers have studied the significance of many forms of cell death in various diseases, particularly malignant tumors, in great detail in recent years. Therefore, how disulfidptosis affects colon cancer and how it functions in the immune system are unknown.

METHODS

Disulfidptosis-related gene (DRG) expression information was obtained from the TCGA-COAD cohort. Patients were categorized into two DRG groups using consensus cluster analysis, and the disulfidptosis-related differentially expressed genes (DRDEGs) were subsequently identified by differential analysis of the two clusters. Univariate Cox regression analysis of the DRDEGs was used to identify prognosis-related DEGs (PRDEGs). The screened PRDEGs were then subjected to LASSO-Cox regression analysis to determine the prognostic model on the basis of ten genes. Immunohistochemistry was used to verify the expression and prognostic value of marker genes.

RESULTS

In the two DRG clusters, the characteristics of the tumor microenvironment (TME) significantly differed by the TME scores and infiltration levels of 23 human immune cell subpopulations. Prognostically meaningful risk scores were found, with a greater chance of mortality (p = 4.4e-7) for patients in the high-risk category. Furthermore, notable differences in TME scores, immune cell infiltration, and immune checkpoint expression were detected among the risk categories. The ROC curves revealed that the nomogram's 1-, 2-, and 3-year AUCs were 0.75, 0.76, and 0.77, respectively, demonstrating the superior predictive capacity of the nomogram. Immunohistochemistry revealed that patients with high FABP4 and low ADAM8 and FSTL3 expressions had a better prognosis.

CONCLUSION

The prognostic features based on 10 PRDEGs performed well in predicting survival, TME status, and response to immunity in COAD patients, helping provide personalized immunotherapy strategies for patients.

Identification and validation of prognostic biomarkers related to tumor immune invasion in pancreatic cancer

Background

The diagnosis and treatment of pancreatic adenocarcinoma (PAAD) remain clinically challenging, and new molecular markers for prognostic assessment and targeted therapy are urgently needed. The tumor microenvironment (TME) and immune invasion play an important role in pancreatic cancer development and progression. Therefore, immunotherapeutic strategies based on the TME and immune invasion may have important clinical value.

Methods

In this study, we extracted transcriptome and clinicopathological data for 179 PAAD samples from the TCGA database and evaluated the immune composition, stromal composition, and infiltrating immune cell landscape in the tumor samples. Then, we identified relevant differentially expressed genes (DEGs) and performed functional annotation and prognostic correlation analysis to identify prognostic biomarkers for pancreatic cancer, the correlation between biomarkers and tumor immune invasion was analyzed to reveal the molecular immune mechanism of pancreatic cancer. Finally, GEO databases (GES71729), GEPIA, TISIDB, TIMER databases and RT-PCR were used for further analysis.

Results

CXCL10 and CXCL11 were highly expressed in pancreatic cancer and associated with poor prognosis of patients through cell adhesion molecules chemokine signaling, cytokine-cytokine receptor interaction, natural killer cell-mediated cytotoxicity, and Toll-like receptor signaling pathways. Finally, the correlation between CXCL10 and CXCL11 and tumor immune invasion was analyzed. The results confirmed that the expression levels of CXCL10 and CXCL11 were positively correlated with the contents of CD8+ T cells. Activated memory CD4+ T cells, M1 macrophages and resting mast cells. The levels of CXCL10 and CXCL11 were related to but negatively correlated with the contents of memory B cells, Tregs and M0 macrophages.

Conclusion

Our study demonstrates that CXCL10 and CXCL11 are novel biomarkers of TME and immune cell infiltration in pancreatic cancer by affecting the distribution of immune cells. CXCL10 and CXCL11 may be new targets for molecular targeted therapy and immunotherapy of pancreatic cancer.

Identification of M2 macrophage markers for predicting outcome and therapeutic response in osteosarcoma: Integrated analysis of single-cell and bulk RNA-sequencing

Identification of effective biomarkers is crucial to improve the efficacy of immunotherapy in patients with osteosarcoma. Tumor-associated M2 macrophages, an important immune cell type in the tumor immune microenvironment, are closely related to the formation and progression of tumors. However, the relationships of M2 macrophages and prognosis and the immunotherapy response to osteosarcoma remain unclear. In this study, we obtained single-cell RNA sequencing (scRNA-seq) data of osteosarcoma from the gene expression omnibus (GEO) database and performed trajectory analysis and cell communication analysis. We then identified M2 macrophage marker genes based on scRNA-seq data of osteosarcoma, and constructed a risk-score model using these genes. Next, we compared the survival status and immune features of patients with high and low risk scores. Based on scRNA-seq data, we found that macrophages were the major immune cell type in the osteosarcoma microenvironment, and the high proportion of M2 macrophages might result from the transition of macrophages M1 to M2. M2 macrophages communicated with osteoblastic cells via the APP, MIF, and SPP1 signaling pathways, facilitating osteosarcoma development. Moreover, we identified 189 osteosarcoma-related M2 macrophage marker genes and screened out 10 key genes used for model constrcution. These 10 genes consisted of two known M2 macrophage markers and eight novel M2 macrophage marker genes. Low-risk patients have a statistically significant survival advantage, which was verified in the four GEO datasets. Low-risk patients also displayed a high abundance of tumor-infiltrating immune cells, indicative of an "hot" immune phenotype, while high-risk patients displayed an opposite immunologic feature. Notably, our analysis of two independent immunotherapy cohorts revealed that low-risk patients had good immunotherapy responses and outcomes. Additionally, we determined 32 evidently correlated pairs between risk score and drug sensitivity. This study reveals a new prognostic signature based on M2 macrophage marker genes that can help optimize personalized prognosis and improve immunotherapy outcomes in patients with osteosarcoma and also provides a method for identifying effective biomarkers based on integrated analysis of single-cell and bulk RNA sequencing.

Single-cell RNA sequencing and immune microenvironment analysis reveal PLOD2-driven malignant transformation in cervical cancer

Background

Cervical cancer is the fourth most common cancer in women globally, and the main cause of the disease has been found to be ongoing HPV infection. Cervical cancer remains the primary cause of cancer-related death despite major improvements in screening and treatment approaches, especially in low- and middle-income nations. Therefore, it is crucial to investigate the tumor microenvironment in advanced cervical cancer in order to identify possible treatment targets.

Materials and methods

In order to better understand malignant cervical cancer epithelial cells (EPCs), this study used bulk RNA-seq data from UCSC in conjunction with single-cell RNA sequencing data from the ArrayExpress database. After putting quality control procedures into place, cell type identification and clustering analysis using the Seurat software were carried out. To clarify functional pathways, enrichment analysis and differential gene expression were carried out. The CIBERSORT and ESTIMATE R packages were used to evaluate the immune microenvironment characteristics, and univariate and multivariate Cox regression analyses were used to extract prognostic features. Furthermore, assessments of drug sensitivity and functional enrichment were carried out.

Results

Eight cell types were identified, with EPCs showing high proliferative and stemness features. Five EPC subpopulations were defined, with C1 NNMT+ CAEPCs driving tumor differentiation. A NNMT CAEPCs Risk Score (NCRS) model was developed, revealing a correlation between elevated NCRS scores and adverse patient outcomes characterized by immune evasion. In vitro experiments validated that the prognostic gene PLOD2 significantly enhances proliferation, migration, and invasion of cervical cancer cells.

Conclusion

This investigation delineated eight cell types and five subpopulations of malignant EPCs in cervical cancer, establishing the C1 NNMT+ CAEPCs as a crucial therapeutic target. The NCRS model demonstrated its prognostic capability, indicating that higher scores are associated with poorer clinical outcomes. The validation of PLOD2 as a prognostic gene highlights its therapeutic potential, underscoring the critical need for integrating immunotherapy and targeted treatment strategies to enhance diagnostic and therapeutic approaches in cervical cancer.

Multi-omics Analysis of Histone-related Genes in Osteosarcoma: A Multidimensional Integrated Study Revealing Drug Sensitivity and Immune Microenvironment Characteristics

IntroductionOsteosarcoma (OS) is a highly aggressive primary bone malignancy with poor prognosis. Histone modifications play crucial roles in tumor progression, but their systematic investigation in OS remains unexplored.MethodsThis study integrated single-cell RNA sequencing data and large-scale clinical information to systematically analyze the spatial heterogeneity of histone modifications in OS and their clinical significance. We employed Seurat for single-cell data analysis, CellChat for cell-cell communication network analysis, and LASSO Cox regression to construct a prognostic model. Additionally, we conducted functional enrichment analysis, immune characteristics analysis, and drug sensitivity prediction.ResultsWe identified five major cell types in the OS microenvironment and discovered significant differences in histone modification levels among different cell types, with osteosarcoma cells and endothelial cells exhibiting higher modification levels. Cell-cell communication network analysis revealed the importance of signaling pathways such as SPP1, CypA, MIF, IGFBP, and VEGF in OS. Based on nine histone modification-related genes, we constructed an efficient prognostic model (AUC values of 0.713, 0.845, and 0.888 for 1-, 3-, and 5-year predictions, respectively), which was validated in an external cohort (AUC = 0.808). Immune microenvironment analysis showed significantly higher proportions of CD8+ T cells and Treg cells in the low-risk group. Drug sensitivity analysis revealed that the low-risk group was more sensitive to Imatinib, Rapamycin, and Sunitinib, while the high-risk group was more sensitive to MAPK pathway inhibitors.ConclusionThis study systematically revealed the spatial heterogeneity of histone modifications in OS and their clinical significance for the first time, proposing an "epigenetic-immune" regulatory network hypothesis and developing a histone modification-based prognostic model. Our proposed "epigenetic-guided personalized medication strategy" provides new insights for precision treatment of OS, potentially significantly improving patient prognosis.

Clinical and Immunological Significance of ANKRD52 in Pan-Cancer

Ankyrin repeat domain 52 (ANKRD52) is a regulatory component of the protein phosphatase 6 (PP6) holoenzyme. Evidence has emerged to suggest involvement of ANKRD52 in tumor metastases and cancer cell escape from T cell-mediated elimination and immunotherapy but there has been no research across different cancer types. The current study explored the biological functions of ANKRD52 by combining data from many databases. The aim was to expose new diagnostic or treatment biomarkers for malignant tumors. The roles of ANKRD52 with respect to immunotherapy in 33 human cancer types were analyzed by combining data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), UCSC Xena, the Tumor Immune Estimation Resource (TIMER), TISIDB and Cellminer. Bioinformatics methods were used to analyze the association between ANKRD52 expression and prognosis, immunological indicators (immune cell infiltration, ESTIMATE scores and tumor microenvironment (TME) signatures), tumor mutational burden (TMB), microsatellite instability (MSI) and drug sensitivity. ANKRD52 expression was generally higher in 24 tumor tissues than in normal tissues and was associated with poor prognosis, especially in kidney chromophobe (KICH). Lower expression was observed in advanced cancer. ANKRD52 expression was strongly linked to major immunological indicators, such as immune cell infiltration, ESTIMATE scores, TME signatures, as well as expression of immune and tumor-related genes. Expression was also associated with indicators of immunotherapy efficacy and outcome, such as TMB in 7 cancer types and MSI in 12. In addition, ANKRD52 expression was linked to sensitivity to a number of anticancer drugs. ANKRD52 had a distinct immune function in breast invasive carcinoma (BRCA) that correlated negatively with most immune indicators. Expression was enriched in proliferation-, differentiation- and metabolism-related pathways and linked to other immune cells and TME signatures. A nomogram to predict 3- or 5-year overall survival (OS) of patients with BRCA was constructed. ANKRD52 may have utility as an oncological and immunological biomarker. New insights into oncogenesis are presented and the development of ANKRD52-targeting to increase the therapeutic efficacy of immunotherapy combined with chemotherapy explored.

Five-gene prognostic model based on autophagy-dependent cell death for predicting prognosis in lung adenocarcinoma

Non-small cell lung adenocarcinoma (LUAD) is the predominant form of lung cancer originating from lung epithelial cells, making it the most prevalent pathological type. Currently, reliable indicators for predicting treatment efficacy and disease prognosis are lacking. Despite extensive validation of autophagy-dependent cell death (ADCD) in solid tumor studies and its correlation with immunotherapy effectiveness and cancer prognosis, systematic research on ADCD-related genes in LUAD is limited. We utilized AddModuleScore, ssGSEA, and WGCNA to identify genes associated with ADCD across single-cell and bulk transcriptome datasets. The TCGA dataset, comprising 598 cases, was randomly divided into training and validation sets to develop an ADCD-related LUAD prediction model. Internal validation was performed using the TCGA validation set. For external validation, datasets GSE13213 (119 LUAD samples), GSE26939 (115 LUAD samples), GSE29016 (39 LUAD samples), and GSE30219 (86 LUAD samples) were employed. We evaluated the model's accuracy and effectiveness in predicting prognostic risk. Additionally, CIBERSORT, ESTIMATE, and ssGSEA techniques were used to explore immunological characteristics, drug response, and gene expression in LUAD. Real-time RT-PCR was conducted to assess variations in mRNA expression levels of the gene XCR1 between cancerous and normal tissues in 10 lung cancer patients. We identified 249 genes associated with autophagy-dependent cell death (ADCD) at both single-cell and bulk transcriptome levels. Univariate COX regression analysis revealed that 18 genes were significantly associated with overall survival (OS). Using LASSO-Cox analysis, we developed an ADCD signature based on five genes (BIRC3, TAP1, SLAMF1, XCR1, and HLA-DMB) and created the ADCD-related risk scoring system (ADCDRS). Validation of this model demonstrated its ability to predict disease prognosis and its correlation with clinical characteristics, immune cell infiltration, and the tumor microenvironment. To enhance clinical applicability, we integrated an ADCDRS nomogram. Furthermore, we identified potential drugs targeting specific risk subgroups. We successfully identified a model based on five ADCD genes to predict disease prognosis and treatment efficacy in LUAD, as well as to assess the tumor immune microenvironment. An efficient and practical ADCDRS nomogram was designed.

A disulfidptosis-related lncRNA signature for analyzing tumor microenvironment and clinical prognosis in hepatocellular carcinoma

Introduction

Disulfidptosis is a recently identified form of non-apoptotic programmed cell death which distinguishes itself from classical cell death pathways. However, the prognostic implications of disulfidptosis-related long non-coding RNAs (DRLs) and their underlying mechanisms in hepatocellular carcinoma (HCC) remain largely unexplored.

Methods

In this study, we leveraged RNA-sequencing data and clinical information of HCC patients from the TCGA database. Through expression correlation and prognostic correlation analyses, we identified a set of top-performing long non-coding RNAs. Subsequently, a 5-DRLs predictive signature was established by conducting a Lasso regression analysis.

Results

This signature effectively stratified patients into high- and low-risk groups, revealing notable differences in survival outcomes. Further validation through univariate and multivariate Cox regression analyses confirmed that the risk score derived from our signature independently predicted the prognosis of HCC patients. Moreover, we observed significant disparities in immune cell infiltration and tumor mutation burden (TMB) between the two risk groups, shedding light on the potential connection between immune-related mechanisms and disulfidptosis. Notably, the signature also exhibited predictive value in the context of chemotherapeutic drug sensitivity and immunotherapy efficacy for HCC patients. Finally, we performed experimental validation at both cellular and patient levels and successfully induced a disulfidptosis phenotype in HCC cells.

Discussion

In general, this multifaceted approach provides a comprehensive overview of DRLs profiles in HCC, culminating in the establishment of a novel risk signature that holds promise for predicting prognosis and therapy outcomes of HCC patients.

Enhanced glycolysis causes extracellular acidification and activates acid-sensing ion channel 1a in hypoxic pulmonary hypertension

In pulmonary hypertension (PHTN), a metabolic shift to aerobic glycolysis promotes a hyperproliferative, apoptosis-resistant phenotype in pulmonary arterial smooth muscle cells (PASMCs). Enhanced glycolysis induces extracellular acidosis, which can activate proton-sensing membrane receptors and ion channels. We previously reported that activation of the proton-gated cation channel acid-sensing ion channel 1a (ASIC1a) contributes to the development of hypoxic PHTN. Therefore, we hypothesize that enhanced glycolysis and subsequent acidification of the PASMC extracellular microenvironment activate ASIC1a in hypoxic PHTN. We observed decreased oxygen consumption rate and increased extracellular acidification rate in PASMCs from chronic hypoxia (CH)-induced PHTN rats, indicating a shift to aerobic glycolysis. In addition, we found that intracellular alkalization and extracellular acidification occur in PASMCs following CH and in vitro hypoxia, which were prevented by the inhibition of glycolysis with 2-deoxy-d-glucose (2-DG). Inhibiting H+ transport/secretion through carbonic anhydrases, Na+/H+ exchanger 1, or vacuolar-type H+-ATPase did not prevent this pH shift following hypoxia. Although the putative monocarboxylate transporter 1 (MCT1) and -4 (MCT4) inhibitor syrosingopine prevented the pH shift, the specific MCT1 inhibitor AZD3965 and/or the MCT4 inhibitor VB124 were without effect, suggesting that syrosingopine targets the glycolytic pathway independent of H+ export. Furthermore, 2-DG and syrosingopine prevented enhanced ASIC1a-mediated store-operated Ca2+ entry in PASMCs from CH rats. These data suggest that multiple H+ transport mechanisms contribute to extracellular acidosis and that inhibiting glycolysis-rather than specific H+ transporters-more effectively prevents extracellular acidification and ASIC1a activation. Together, these data reveal a novel pathological relationship between glycolysis and ASIC1a activation in hypoxic PHTN.NEW & NOTEWORTHY In pulmonary hypertension, a metabolic shift to aerobic glycolysis drives a hyperproliferative, apoptosis-resistant phenotype in pulmonary arterial smooth muscle cells. We demonstrate that this enhanced glycolysis induces extracellular acidosis and activates the proton-gated ion channel, acid-sensing ion channel 1a (ASIC1a). Although multiple H+ transport/secretion mechanisms are upregulated in PHTN and likely contribute to extracellular acidosis, inhibiting glycolysis with 2-deoxy-d-glucose or syrosingopine effectively prevents extracellular acidification and ASIC1a activation, revealing a promising therapeutic avenue.

Integrating single-cell RNA-seq to identify fibroblast-based molecular subtypes for predicting prognosis and therapeutic response in bladder cancer

BACKGROUND

Bladder cancer (BLCA) is a highly aggressive and heterogeneous disease, posing challenges for diagnosis and treatment. Cancer immunotherapy has recently emerged as a promising option for patients with advanced and drug-resistant cancers. Fibroblasts, a significant component of the tumor microenvironment, play a crucial role in tumor progression, but their precise function in BLCA remains uncertain.

METHODS

Single-cell RNA sequencing (scRNA-seq) data for BLCA were obtained from the Gene Expression Omnibus database. The R package "Seurat" was used for processing scRNA-seq data, with uniform manifold approximation and projection (UMAP) for downscaling and cluster identification. The FindAllMarkers function identified marker genes for each cluster. Differentially expressed genes influencing overall survival (OS) of BLCA patients were identified using the limma package. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between high- and low-risk groups were investigated. RT-qPCR and immunohistochemistry validated the expression of prognostic genes.

RESULTS

Fibroblast marker genes identified three molecular subtypes in the testing set. A prognostic signature comprising ten genes stratified BLCA patients into high- and low-score groups. This signature was validated in one internal and two external validation sets. High-score patients exhibited increased immune cell infiltration, elevated chemokine expression, and enhanced immune checkpoint expression but had poorer OS and a reduced response to immunotherapy. Six sensitive anti-tumor drugs were identified for the high-score group. RT-qPCR and immunohistochemistry showed that CERCAM, TM4SF1, FN1, ANXA1, and LOX were highly expressed, while EMP1, HEYL, FBN1, and SLC2A3 were downregulated in BLCA.

CONCLUSION

A novel fibroblast marker gene-based signature was established, providing robust predictions of survival and immunotherapeutic response in BLCA patients.

A novel angiogenesis-associated risk score predicts prognosis and characterizes the tumor microenvironment in colon cancer

Background

Angiogenesis of the tumor microenvironment (TME) can promote the proliferation and metastases of colon cancer (CC). However, there is a lack of bioinformatics analysis to comprehensively clarify the molecular characteristics, immune interaction characteristics and predictive values of angiogenesis characteristics in CC patients. This study aimed to perform a comprehensive elucidation of the correlation between angiogenesis and CC for the purpose of improving the clinical management of CC.

Methods

Angiogenesis-associated genes (AAGs) were evaluated in the population of CC patients from the Cancer Genome Atlas database and Gene Expression Omnibus dataset. The expression, prognostic role, and immune cell infiltration of AAGs were assessed first. And then we established the AAGs score to further explore the prognosis and treatment response of angiogenesis characteristics in individual patient.

Results

Totally, we identified two different molecular subtypes of angiogenesis, and there was a significant difference in the background of genome, expression profiles, prognosis, and characteristics of TME between two subtypes. And the AAGs score was independently associated with over survival in CC patients, the prognostic value was significant and confirmed in the entire cohort. And we also constructed a nomogram based on the risk score and clinical parameters to maximize the predictive ability of the risk score. Additionally, the AAGs score was significantly correlated with the tumor mutation burden score, cancer stem cell score and drug sensitivity.

Conclusions

Our study elucidated the role of angiogenesis characteristics in CC and the AAGs score could help clinicians plan for individual management with chemotherapy agents and promote the development of immunotherapy in CC. Prospective studies need to be conducted to further confirm our findings.

Identification and validation of a signature based on myofibroblastic cancer-associated fibroblast marker genes for predicting prognosis, immune infiltration, and therapeutic response in bladder cancer

PURPOSE

Myofibroblastic cancer-associated fibroblasts (myCAFs) are important components of the tumor microenvironment closely associated with tumor stromal remodeling and immunosuppression. This study aimed to explore myCAFs marker gene biomarkers for clinical diagnosis and therapy for patients with bladder cancer (BC).

MATERIALS AND METHODS

BC single-cell RNA sequencing (scRNA-seq) data were obtained from the National Center for Biotechnology Information Sequence Read Archive. Transcriptome and clinical data were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Subsequently, univariate Cox and LASSO (Least Absolute Shrinkage and Selection Operator regression) regression analyses were performed to construct a prognostic signature. Immune cell activity was estimated using single-sample gene set enrichment analysis whilst the TIDE (tumor immune dysfunction and exclusion) method was employed to assess patient response to immunotherapy. The chemotherapy response of patients with BC was evaluated using genomics of drug sensitivity in cancer. Furthermore, Immunohistochemistry was used to verify the correlation between MAP1B expression and immunotherapy efficacy. The scRNA-seq data were analyzed to identify myCAFs marker genes.

RESULTS

Combined with bulk RNA-sequencing data, we constructed a two-gene (COL6A1 and MAP1B) risk signature. In patients with BC, the signature demonstrated outstanding prognostic value, immune infiltration, and immunotherapy response. This signature served as a crucial guide for the selection of anti-tumor chemotherapy medications. Additionally, immunohistochemistry confirmed that MAP1B expression was significantly correlated with immunotherapy efficacy.

CONCLUSIONS

Our findings revealed a typical prognostic signature based on myCAF marker genes, which offers patients with BC a novel treatment target alongside theoretical justification.

Endoplasmic reticulum stress regulators exhibit different prognostic, therapeutic and immune landscapes in pancreatic adenocarcinoma

Endoplasmic reticulum stress (ERS) and unfolded protein response are the critical processes of tumour biology. However, the roles of ERS regulatory genes in pancreatic adenocarcinoma (PAAD) remain elusive. A novel ERS-related risk signature was constructed using the Lasso regression analysis. Its prognostic value, immune effect, metabolic influence, mutational feature and therapeutic correlation were comprehensively analysed through multiple bioinformatic approaches. The biofunctions of KDELR3 and YWHAZ in pancreatic cancer (PC) cells were also investigated through colony formation, Transwell assays, flow cytometric detection and a xenograft model. The upstream miRNA regulatory mechanism of KDELR3 was predicted and validated. ERS risk score was identified as an independent prognostic factor and could improve traditional prognostic model. Meanwhile, it was closely associated with metabolic reprogramming and tumour immune. High ERS risk enhanced glycolysis process and nucleotide metabolism, but was unfavourable for anti-tumour immune response. Moreover, ERS risk score could act as a potential biomarker for predicting the efficacy of ICBs. Overexpression of KDELR3 and YWHAZ stimulated the proliferation, migration and invasion of SW1990 and BxPC-3 cells. Silencing KDELR3 suppressed tumour growth in a xenograft model. miR-137 could weaken the malignant potentials of PC cells through inhibiting KDELR3 (5'-AGCAAUAA-3'). ERS risk score greatly contributed to PAAD clinical assessment. KDELR3 and YWHAZ possessed cancer-promoting capacities, showing promise as a novel treatment target.

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.

Integrative landscape analysis of prognostic model biomarkers and immunogenomics of disulfidptosis-related genes in breast cancer based on LASSO and WGCNA analyses

BACKGROUND

Disulfidptosis is a novel type of programmed cell death. However, the value of disulfidptosis-related genes (DRGs) in the prediction of breast cancer prognosis is unclear.

METHODS

RNA-seq data of 1231 patients, together with information on patient clinical characteristics and prognosis, were downloaded from TCGA. DRGs were identified between cancerous and non-cancerous tissues. The LASSO algorithm was used to assign half of the samples to the training set. Risk scores were used for construction of a prognostic model for risk stratification and prognosis prediction, and the clinical applicability was examined using a line diagram. The relationships between risk scores, immune cell infiltration, molecular subtypes, and responses to immunotherapy and chemotherapy were examined.

RESULTS

We identified and obtained four DRG-related prognostic lncRNAs (AC009097.2, AC133552.5, YTHDF3-AS1, and AC084824.5), which were used for establishing the risk model. Longer survival was associated with low risk. The DRG-associated lncRNAs were found to independently predict patient prognosis. The AUCs under the ROCs for one-, three-, and 5-year survival in the training cohort were 0.720, 0.687, and 0.692, respectively. The model showed that the high-risk patients had reduced overall survival as well as high tumor mutation burdens. Furthermore, high-risk patients showed increased sensitivity to therapeutic drugs, including docetaxel, paclitaxel, and oxaliplatin.

CONCLUSION

The risk score model was effective for predicting both prognosis and sensitivity to therapeutic drugs, suggesting its possible usefulness for the management of patients with breast cancer.

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.

A novel tetraspanin-related gene signature for predicting prognosis and immune invasion status of lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD), the most common subtype of lung cancer, is the primary contributor to cancer-linked fatalities. Dysregulation in the proliferation of cells and death is primarily involved in its development. Recently, tetraspanins, a group of transmembrane proteins, have gained increasing attention for their potential role in the progression of LUAD. Hence, our endeavor involved the development of a novel tetraspanin-based model for the prognostication of lung cancer.

METHODS

A comprehensive set of bioinformatics tools was utilized to evaluate the expression of tetraspanin-related genes and assess their significance regarding prognosis. Hence, a robust risk signature was established through machine learning. The prognosis-predictive value of the signature was evaluated in terms of clinical application, functional enrichment, and the immune landscape.

RESULTS

The research first identified differential expression of tetraspanin genes in patients with LUAD via publicly available databases. The resulting data were indicative of the value that nine of them held regarding prognosis. Five distinct elements were utilized in the establishment of a tetraspanin-related model (TSPAN7, TSPAN11, TSPAN14, UPK1B, and UPK1A). Furthermore, as per the median risk scores, the participants were classified into high- and low-risk groups. The model was validated using inner and outer validation sets. Notably, consensus clustering and prognostic score grouping analysis revealed that tetraspanin-related features affect tumor prognosis by modulating tumor immunity. A nomogram based on the tetraspanin gene was constructed with the aim of enhancing the poor prognosis of high-risk groups and facilitating clinical application.

CONCLUSION

Through machine learning algorithms and in vitro experiments, a novel tetraspanin-associated signature was developed and validated for survival prediction in patients with LUAD that reflects tumor immune infiltration. This could potentially provide new and improved measures for diagnosis and therapeutic interventions for LUAD.

Radiogenomic Features of GIMAP Family Genes in Clear Cell Renal Cell Carcinoma: An Observational Study on CT Images

GTPases of immunity-associated proteins (GIMAP) genes include seven functional genes and a pseudogene. Most of the GIMAPs have a role in the maintenance and development of lymphocytes. GIMAPs could inhibit the development of tumors by increasing the amount and antitumor activity of infiltrating immunocytes. Knowledge of key factors that affect the tumor immune microenvironment for predicting the efficacy of immunotherapy and establishing new targets in ccRCC is of great importance. A computed tomography (CT)-based radiogenomic approach was used to detect the imaging phenotypic features of GIMAP family gene expression in ccRCC. In this retrospective study we enrolled 193 ccRCC patients divided into two groups: ccRCC patients with GIMAP expression (n = 52) and ccRCC patients without GIMAP expression (n = 141). Several imaging features were evaluated on preoperative CT scan. A statistically significant correlation was found with absence of endophytic growth pattern (p = 0.049), tumor infiltration (p = 0.005), advanced age (p = 0.018), and high Fuhrman grade (p = 0.024). This study demonstrates CT imaging features of GIMAP expression in ccRCC. These results could allow the collection of data on GIMAP expression through a CT-approach and could be used for the development of a targeted therapy.

Integrating Bulk and Single-Cell RNA Sequencing Reveals Heterogeneity, Tumor Microenvironment, and Immunotherapeutic Efficacy Based on Sialylation-Related Genes in Bladder Cancer

Background

As known abnormal sialylation exerts crucial roles in the growth, metastasis, and immune evasion of cancers, but the molecular characteristics and roles in bladder cancer (BLCA) remain unclear. This study intends to establish BLCA risk stratification based on sialylation-related genes and elucidate its role in prognosis, tumor microenvironment, and immunotherapy of BLCA.

Methods

Bulk RNA-seq and scRNA-seq data were downloaded from open-access databases. The scRNA-seq data were processed using the R package "Seurat" to identify the core cell types. The tumor sub-typing of BLCA samples was performed by the R package "ConsensusClusterPlus" in the bulk RNA-seq data. Signature genes were identified by the R package "limma" and univariate regression analysis to calculate risk scores using the R package "GSVA" and establish risk stratification of BLCA patients. Finally, the differences in clinicopathological characteristics, tumor microenvironment, and immunotherapy efficacy between the different groups were investigated.

Results

5 core cell types were identified in the scRNA-seq dataset, with monocytes and macrophages presenting the greatest percentage, sialylation-related gene expression, and sialylation scores. The bulk RNA-seq samples were classified into 3 tumor subtypes based on 19 prognosis-related sialylation genes. The 10 differential expressed genes (DEGs) with the smallest p-values were collected as signature genes, and the risk score was calculated, with the samples divided into high and low-risk score groups. The results showed that patients in the high-risk score group exhibited worse survival outcomes, higher tumor grade, more advanced stage, more frequency of gene mutations, higher expression levels of immune checkpoints, and lower immunotherapy response.

Conclusion

We established a novel risk stratification of BLCA from a glycomics perspective, which demonstrated good accuracy in determining the prognostic outcome, clinicopathological characteristics, immune microenvironment, and immunotherapy efficacy of patients, and we are proposing to apply it to direct the choice of clinical treatment options for patients.

Identification of disulfidptosis related subtypes, characterization of tumor microenvironment infiltration, and development of DRG prognostic prediction model in RCC, in which MSH3 is a key gene during disulfidptosis

Disulfidptosis is a newly discovered mode of cell death induced by disulfide stress. However, the prognostic value of disulfidptosis-related genes (DRGs) in renal cell carcinoma (RCC) remains to be further elucidated. In this study, consistent cluster analysis was used to classify 571 RCC samples into three DRG-related subtypes based on changes in DRGs expression. Through univariate regression analysis and LASSO-Cox regression analysis of differentially expressed genes (DEGs) among three subtypes, we constructed and validated a DRG risk score to predict the prognosis of patients with RCC, while also identifying three gene subtypes. Analysis of DRG risk score, clinical characteristics, tumor microenvironment (TME), somatic cell mutations, and immunotherapy sensitivity revealed significant correlations between them. A series of studies have shown that MSH3 can be a potential biomarker of RCC, and its low expression is associated with poor prognosis in patients with RCC. Last but not least, overexpression of MSH3 promotes cell death in two RCC cell lines under glucose starvation conditions, indicating that MSH3 is a key gene in the process of cell disulfidptosis. In summary, we identify potential mechanism of RCC progression through DRGs -related tumor microenvironment remodeling. In addition, this study has successfully established a new disulfidptosis-related genes prediction model and discovered a key gene MSH3. They may be new prognostic biomarkers for RCC patients, provide new insights for the treatment of RCC patients, and may inspire new methods for the diagnosis and treatment of RCC patients.

Attenuation of In Vitro and In Vivo Virulence Is Associated with Repression of Gene Expression of AIG1 Gene in Entamoeba histolytica

Entamoeba histolytica virulence results from complex host-parasite interactions implicating multiple amoebic components (e.g., Gal/GalNAc lectin, cysteine proteinases, and amoebapores) and host factors (microbiota and immune response). UG10 is a strain derived from E. histolytica virulent HM-1:IMSS strain that has lost its virulence in vitro and in vivo as determined by a decrease of hemolytic, cytopathic, and cytotoxic activities, increased susceptibility to human complement, and its inability to form liver abscesses in hamsters. We compared the transcriptome of nonvirulent UG10 and its parental HM-1:IMSS strain. No differences in gene expression of the classical virulence factors were observed. Genes downregulated in the UG10 trophozoites encode for proteins that belong to small GTPases, such as Rab and AIG1. Several protein-coding genes, including iron-sulfur flavoproteins and heat shock protein 70, were also upregulated in UG10. Overexpression of the EhAIG1 gene (EHI_180390) in nonvirulent UG10 trophozoites resulted in augmented virulence in vitro and in vivo. Cocultivation of HM-1:IMSS with E. coli O55 bacteria cells reduced virulence in vitro, and the EhAIG1 gene expression was downregulated. In contrast, virulence was increased in the monoxenic strain UG10, and the EhAIG1 gene expression was upregulated. Therefore, the EhAIG1 gene (EHI_180390) represents a novel virulence determinant in E. histolytica.

An inflammation-related gene landscape predicts prognosis and response to immunotherapy in virus-associated hepatocellular carcinoma

Background

Due to the viral infection, chronic inflammation significantly increases the likelihood of hepatocellular carcinoma (HCC) development. Nevertheless, an inflammation-based signature aimed to predict the prognosis and therapeutic effect in virus-related HCC has rarely been established.

Method

Based on the integrated analysis, inflammation-associated genes (IRGs) were systematically assessed. We comprehensively investigated the correlation between inflammation and transcriptional profiles, prognosis, and immune cell infiltration. Then, an inflammation-related risk model (IRM) to predict the overall survival (OS) and response to treatment for virus-related HCC patients was constructed and verified. Also, the potential association between IRGs and tumor microenvironment (TME) was investigated. Ultimately, hub genes were validated in plasma samples and cell lines via qRT-PCR. After transfection with shCCL20 combined with overSLC7A2, morphological change of SMMC7721 and huh7 cells was observed. Tumorigenicity model in nude mouse was established.

Results

An inflammatory response-related gene signature model, containing MEP1A, CCL20, ADORA2B, TNFSF9, ICAM4, and SLC7A2, was constructed by conjoint analysis of least absolute shrinkage and selection operator (LASSO) Cox regression and gaussian finite mixture model (GMM). Besides, survival analysis attested that higher IRG scores were positively relevant to worse survival outcomes in virus-related HCC patients, which was testified by external validation cohorts (the ICGC cohort and GSE84337 dataset). Univariate and multivariate Cox regression analyses commonly proved that the IRG was an independent prognostic factor for virus-related HCC patients. Thus, a nomogram with clinical factors and IRG was also constructed to superiorly predict the prognosis of patients. Featured with microsatellite instability-high, mutation burden, and immune activation, lower IRG score verified a superior OS for sufferers. Additionally, IRG score was remarkedly correlated with the cancer stem cell index and drug susceptibility. The measurement of plasma samples further validated that CCL20 upexpression and SLC7A2 downexpression were positively related with virus-related HCC patients, which was in accord with the results in cell lines. Furthermore, CCL20 knockdown combined with SLC7A2 overexpression availably weakened the tumor growth in vivo.

Conclusions

Collectively, IRG score, serving as a potential candidate, accurately and stably predicted the prognosis and response to immunotherapy in virus-related HCC patients, which could guide individualized treatment decision-making for the sufferers.

Subtypes analysis and prognostic model construction based on lysosome-related genes in colon adenocarcinoma

Background: Lysosomes are essential for the development and recurrence of cancer. The relationship between a single lysosome-related gene and cancer has previously been studied, but the relationship between the lysosome-related genes (LRGs) and colon adenocarcinoma (COAD) remains unknown. This research examined the role of lysosome-related genes in colon adenocarcinoma. Methods: 28 lysosome-related genes associated with prognosis (PLRGs) were found by fusing the gene set that is differently expressed between tumor and non-tumor in colon adenocarcinoma with the gene set that is related to lysosomes. Using consensus unsupervised clustering of PLRGs, the colon adenocarcinoma cohort was divided into two subtypes. Prognostic and tumor microenvironment (TME) comparisons between the two subtypes were then made. The PLRGs_score was constructed using the least absolute shrinkage and selection operator regression (LASSO) method to quantify each patient's prognosis and provide advice for treatment. Lastly, Western Blot and immunohistochemistry (IHC) were used to identify MOGS expression at the protein level in colon adenocarcinoma tissues. Results: PLRGs had more somatic mutations and changes in genetic level, and the outcomes of the two subtypes differed significantly in terms of prognosis, tumor microenvironment, and enrichment pathways. Then, PLRGs_score was established based on two clusters of differential genes in the cancer genome atlas (TCGA) database, and external verification was performed using the gene expression omnibus (GEO) database. Then, we developed a highly accurate nomogram to enhance the clinical applicability of the PLRGs_score. Finally, a higher PLRGs_score was associated with a poorer overall survival (OS), a lower tumor mutation burden (TMB), a lower cancer stem cell (CSC) index, more microsatellite stability (MSS), and a higher clinical stage. MOGS was substantially elevated at the protein level in colon adenocarcinoma as additional confirmation. Conclusion: Overall, based on PLRGs, we identified two subtypes that varied significantly in terms of prognosis and tumor microenvironment. Then, in order to forecast patient prognosis and make treatment suggestions, we developed a diagnostic model with major significance for prognosis, clinical relevance, and immunotherapy. Moreover, we were the first to demonstrate that MOGS is highly expressed in colon adenocarcinoma.

GIMAP7 induces oxidative stress and apoptosis of ovarian granulosa cells in polycystic ovary syndrome by inhibiting sonic hedgehog signalling pathway

Polycystic ovary syndrome (PCOS) is a gynaecological endocrine disease. The objective of the present study was to investigate the role of GTPase immunity-associated protein (GIMAP) 7 in PCOS. A PCOS rat model was established using dehydroepiandrosterone injection. The data showed that GIMAP7 was mainly located in granulosa cells and was abundantly expressed in the ovarian granulosa cells of PCOS rats. GIMAP7 silencing decreased blood glucose levels, HOMA-IR scores, and number of cystic follicles. In addition, GIMAP7 silencing corrected erratic oestrous cycles, inhibited apoptosis and reduced c-caspase-3 protein expression in the ovarian tissues of PCOS rats. GIMAP7 silencing reduced malondialdehyde (MDA) but increased glutathione (GSH) and superoxide dismutase (SOD) levels in the serum and ovarian tissues of PCOS rats. The effects of GIMAP7 were further investigated in human ovarian granulosa KGN cells. GIMAP7 silencing increased the viability, promoted proliferation, and increased the percentage of S-phase KGN cells. The apoptosis rate was significantly decreased by GIMAP7 silencing. GIMAP7 also inhibited oxidative stress in KGN cells, resulting in decreased levels of reactive oxygen species (ROS) and MDA and increased levels of GSH and SOD. Notably, GIMAP7 inhibited the sonic hedgehog (SHH) signalling pathway, and GIMAP7 silencing increased the expression of the SHH signalling pathway downstream genes SHH, SMO, and Gli1. Inhibition of the SHH signalling pathway using cyclopamine reduced the effect of GIMAP7 silencing on KGN cells. This study proved that GIMAP7 promotes oxidative stress and apoptosis in ovarian granulosa cells in PCOS by inhibiting the SHH signalling pathway.

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.

Prognosis and immune features of pyroptosis-related RNA patterns in low-grade glioma

Purpose

Low-grade gliomas (LGG), which are malignant primary brain tumors, are more prevalent in young adults. Pyroptosis, an inflammatory form of programmed cell death, has been shown in recent years to be directly associated with tumor growth and tumor microenvironment (TME). However, the correlation between LGG and pyroptosis remained to be explored. In this research, we explored pyroptosis-related gene expression patterns and their prognostic significance based on transcriptome profiles and clinical data in LGG.

Methods

We identified 31 pyroptosis-related genes differentially expressed at the mRNA level between the data of LGG patients from TCGA and the data of normal brain tissues from GTEx. Univariate Cox regression analysis was used to screen 16 differentially expressed genes (DEGs) based on survival data. Next, the prognostic model was established using LASSO Cox regression, which divided LGG patients into high- and low- risk subgroups and showed an independent prognostic value for overall survival (OS) combined with clinical factors in the CGGA test cohort. Pyroptosis and immune cells were correlated through the CIBERSORT R package and the TIMER database.

Results

Based on the analyses of 523 LGG and 1152 normal tissues, nine significant differential genes were identified. The AUC remained at about 0.74 when combined with the risk score and clinical factors. Enrichment analyses revealed that DEGs were mainly enriched in cytokine-cytokine receptor interactions, immune response and chemokine signaling pathways. Immune cell enrichment analysis demonstrated that scores for most immune cell types differed significantly between the high-and low-risk groups, and further infiltrating analysis showed obvious differences between these two risk subgroups.

Conclusion

Pyroptosis-related genes play a pivotal role in LGG and are associated with tumor immunity, which may be beneficial to the prognosis and immunotherapy of LGG.

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.

Identification and validation of transferrin receptor protein 1 for predicting prognosis and immune infiltration in lower grade glioma

INTRODUCTION

Transferrin receptor protein 1 (TFRC), an ananda molecule associated with ferroptosis, has been identified as affecting a wide spectrum of pathological processes in various cancers, but the prognostic value correlates with the tumor microenvironment of TFRC in lower-grade glioma (LGG) is still unclear.

MATERIALS AND METHODS

Clinical pathological information and gene expression data of patients with LGG come from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GTEx, Oncomine, UCSC Xena, and GEO databases. We then used various bioinformatics methods and mathematical models to analyze those data, aiming to investigate the clinical significance of TFRC in LGG and illustrate its association with tumor immunity. In addition, the molecular function and mechanisms of TFRC were revealed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Immunohistochemical experiments and single-cell analysis have been performed.

RESULTS

TFRC expression was highly expressed in many tumors and showed a poor prognosis. Including gliomas, it was significantly associated with several poor clinical prognostic variables, tumor immune microenvironment, tumor mutational burden (TMB), m6a modification, and ferroptosis in LGG. TFRC as a key factor was further used to build a prediction nomogram. The C-index, calibration curve, and decision curve analysis showed the nomogram was clinically useful and calibration was accurate. At the same time, we also demonstrated that promoter hypomethylation of DNA upstream of TFRC could lead to high TFRC expression and poor overall survival. There is a significant correlation between TFRC and CD8 + T cell, macrophage cell infiltration, and several immune checkpoints, such as PD-L1(cd274), CTLA4, and PD1, suggesting a novel direction for future clinical application. Functional and molecular mechanism analysis showed an association of TFRC expression with immune-related pathways through GSEA, GO, and KEGG analysis. Finally, immunohistochemical experiments and single-cell analysis confirmed the expression of TFRC in glioma.

CONCLUSION

TFRC may be a potential prognostic biomarker and an immunotherapeutic target for glioma.

CXCL9-modified CAR T cells improve immune cell infiltration and antitumor efficacy

Chimeric antigen receptor (CAR) T cells remain unsatisfactory in treating solid tumors. The frequency of tumor-infiltrating T cells is closely related to the good prognosis of patients. Augmenting T cell accumulation in the tumor microenvironment is essential for tumor clearance. To overcome insufficient immune cell infiltration, innovative CAR designs need to be developed immediately. CXCL9 plays a pivotal role in regulating T cell migration and inhibiting tumor angiogenesis. Therefore, we engineered CAR T cells expressing CXCL9 (CART-CXCL9). The addition of CXCL9 enhanced cytokine secretion and cytotoxicity of CAR T cells and endowed CAR T cells with the ability to recruit activated T cells and antiangiogenic effect. In tumor-bearing mice, CART-CXCL9 cells attracted more T cell trafficking to the tumor site and inhibited angiogenesis than conventional CAR T cells. Additionally, CART-CXCL9 cell therapy slowed tumor growth and prolonged mouse survival, displaying superior antitumor activity. Briefly, modifying CAR T cells to express CXCL9 could effectively improve CAR T cell efficacy against solid tumors.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and therapeutic response in lung squamous cell carcinoma

Cancer immunotherapy is an increasingly successful strategy for treating patients with advanced or conventionally drug-resistant cancers. T cells have been proved to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in lung squamous cell carcinoma (LUSC). In this study, we first performed a comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data from the gene expression omnibus (GEO) database to identify 72 T-cell marker genes. Subsequently, we constructed a 5-gene prognostic signature in the training cohort based on the T-cell marker genes from the cancer genome atlas (TCGA) database, which was further validated in the testing cohort and GEO cohort. The areas under the receiver operating characteristic curve at 1-, 3-, and 5-years were 0.614, 0.713 and 0.702 in the training cohort, 0.669, 0.603 and 0.645 in the testing cohort, 0.661, 0.628 and 0.590 in the GEO cohort, respectively. Furthermore, we created a highly reliable nomogram to facilitate clinical application. Gene set enrichment analysis showed that immune-related pathways were mainly enriched in the high-risk group. Tumor immune microenvironment indicated that high-risk group exhibited higher immune score, stromal score, and immune cell infiltration levels. Moreover, genes of the immune checkpoints and human leukocyte antigen family were all overexpressed in high-risk group. Drug sensitivity revealed that low-risk group was sensitive to 8 chemotherapeutic drugs and high-risk group to 4 chemotherapeutic drugs. In short, our study reveals a novel prognostic signature based on T-cell marker genes, which provides a new target and theoretical support for LUSC patients.

NEUROD1 predicts better prognosis in pancreatic cancer revealed by a TILs-based prognostic signature

It has been well-defined that tumor-infiltrating lymphocytes (TILs) play critical roles in pancreatic cancer (PaCa) progression. This research aimed to comprehensively explore the composition of TILs in PaCa and their potential clinical significance. A total of 178 samples from the TCGA and 63 samples from the GSE57495 dataset were enrolled in our study. ImmuCellAI was applied to calculate the infiltrating abundance of 24 immune cell types in PaCa and further survival analysis revealed the prognostic values of TILs in PaCa. Moreover, the Hallmark enticement analysis of differentially expressed genes (DEGs) between low- and high-risk groups was performed as well. Immunohistochemistry staining was used to evaluate NEUROD1 expression. As result, different kinds of TILs had distinct infiltrating features. In addition, Specific TILs subsets had notable prognostic values in PaCa. We further established a 6-TILs signature to assess the prognosis of PaCa patients. Kaplan-Meier and Cox regression analyses both suggested the significant prognostic value of the signature in PaCa. Based on the prognostic signature, we screened a great deal of potential prognostic biomarkers and successfully validated NEUROD1 as a novel prognostic biomarker in PaCa. Overall, the current study illuminated the immune cells infiltrating the landscape in PaCa and identified a TILs-dependent signature and NEUROD1 for prognostic prediction in PaCa patients.

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.

Identification of cuproptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer

Breast cancer (BC) is now the most frequent and lethal cancer among women. Cuproptosis is a newly identified programmed cell death process that has been connected to tumor therapeutic sensitivity, patient outcomes, and the genesis of cancer. Cuproptosis-related genes (CRGs) are involved in breast cancer, although their roles and potential mechanisms are still unclear. First, we examined the effect of gene mutations and copy number changes on overall survival in 1168 breast cancer samples. Breast cancer patients were split into two molecular categories as determined by the variation in CRG based on clinicopathological traits, overall survival, and cell-infiltrating traits in tumor microenvironments. In addition, we created and validated a CRG score to calculate breast cancer patients' OS. Finally, we created a comprehensive nomogram for the clinical use of the CRG score. Patients whose CRG scores were low showed increased odds of developing OS, a larger mutation load, and immunological activation than those with high CRG scores. The CRG score, the cancer stem cell index, and the responsiveness to chemotherapy or targeted therapies were also shown to be statistically significantly correlated. Our thorough examination of CRGs in breast cancer patients demonstrated that they may be useful predictors of prognosis, clinical characteristics, and tumor microenvironment. These findings provide fresh insight into CRGs in breast cancer and might inspire brand-new approaches to both diagnosing and treating patients there.

Cuproptosis scoring system to predict the clinical outcome and immune response in bladder cancer

Cuproptosis is a novel copper ion-dependent cell death type being regulated in cells, and this is quite different from the common cell death patterns such as apoptosis, pyroptosis, necroptosis, and ferroptosis. Interestingly, like with death patterns, cuproptosis-related genes have recently been reported to regulate the occurrence and progression of various tumors. However, in bladder cancer, the link between cuproptosis and clinical outcome, tumor microenvironment (TME) modification, and immunotherapy is unknown. To determine the role of cuprotosis in the tumor microenvironment, we systematically examined the characteristic patterns of 10 cuproptosis-related genes in bladder cancer (BLCA). By analyzing principal component data, we established a cuproptosis score to determine the degree of cuproptosis among patients. Finally, we evaluated the potential of these values in predicting BLCA prognosis and treatment responses. A comprehensive study of the mutations of cuproptosis-related genes in BLCA specimens was conducted at the genetic level, and their expression and survival patterns were evaluated using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Two cuproptosis patterns were constructed based on the transcription level of 10 cuproptosis-related genes, featuring differences in the prognosis and the infiltrating landscape of immune cells (especially T and dendritic cells) with interactions between cuproptosis and the TME. Our study further demonstrated that cuproptosis score may predict prognosis, immunophenotype sensitivity to chemotherapy, and immunotherapy response among bladder cancer patients. The development and progression of bladder cancer are likely to be influenced by cuproptosis, which may involve a diverse and complex TME. The cuproptosis pattern evaluated in our study may enhance understanding of immune infiltrations and guide more potent immunotherapy interventions.

Prognostic and Immunological Value of GNB4 in Gastric Cancer by Analyzing TCGA Database

Background

Gastric cancer (GC) represents a universal malignant tumor of the digestive system. Stromal and immune cells belong to two main nontumor components exerting a vital function in the tumor microenvironment.

Methods

Based on TCGA database, this study downloaded clinical information and gene profiles of GC. The ESTIMATE algorithm was adopted for evaluating the score of immune-infiltrating cells. This work employed Sangerbox to explore the differentially denoted genes (DEGs) related to stromal, immunity, and prognosis. Besides, the STRING database was involved in order to detect the association among the proteins. The MCODE module of Cytoscape software was used to screen key genes. Oncomine and GEPIA databases were used, aiming to study the differences in key genes in healthy gastric mucosa and GC. At last, we adopted TISDIB and TIMER databases for analyzing the association of guanine nucleotide binding protein subunit-4 (GNB4) between gastric cancer and tumor immune cells. qRT-PCR was applied for exploring differential GNB4 expression between GC and normal gastric mucosa and investigating the relation of GNB4 with tumor-infiltrating lymphocytes (TILs).

Results

Patients undergoing a great stromal score exhibited worse prognostic outcome, and cases having a low immune score had better prognosis. Overall, altogether 656 genes were upregulated with 5 genes being downregulated, which were matrix immune-related differential genes. Furthermore, 18 genes were screened as hub genes on the basis of the univariate Cox risk model of TCGA database (82 differential genes predicted poor GC survival). Oncomine and GEPIA databases revealed that GNB4 expression in gastric cancer was obviously higher in comparison with that in normal gastric mucosa. The GSEA, TISDIB, and TIMER databases revealed that GNB4 is involved in various tumor signal pathways and immune and metabolic processes. qRT-PCR demonstrated that GNB4 expression in gastric cancer was notably higher in comparison with that in normal gastric mucosa, showing significant association with matrix TILs.

Conclusion

The selected key gene GNB4 is a potential biomarker to guide the immunotherapy of gastric cancer.

N7-Methylguanosine Regulatory Genes Profoundly Affect the Prognosis, Progression, and Antitumor Immune Response of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a common abdominal cancer with poor survival outcomes. Although there is growing evidence that N7-methylguanosine (m7G) is closely associated with tumor prognosis, development, and immune response, few studies focus on this topic.

Methods

The novel m7G risk signature was constructed through the Lasso regression analysis. Its prognostic value was evaluated through a series of survival analyses and was tested in ICGC-LIRI, GSE14520, and GSE116174 cohorts. CIBERSORT, ssGSEA, and ESTIMATE methods were applied to explore the effects of the m7G risk score on tumor immune microenvironment (TIM). The GSEA method was used to evaluate the impacts of the m7G risk score on glycolysis, ferroptosis, and pyroptosis. The human protein atlas (HPA) database was used to clarify the histological expression levels of five m7G signature genes. The biofunctions of NCBP2 in hepatocellular cancer (HC) cells were confirmed through qPCR, CCK8, and transwell assays.

Results

Five m7G regulatory genes comprised the novel risk signature. The m7G risk score was identified as an independent prognostic factor of HCC and could increase the decision-making benefit of traditional prognostic models. Besides, we established a nomogram containing the clinical stage and m7G risk score to predict the survival rates of HCC patients. The prognostic value of the m7G model was successfully validated in ICGC and GSE116174 cohorts. Moreover, high m7G risk led to a decreased infiltration level of CD8+ T cells, whereas it increased the infiltration levels of Tregs and macrophages. The glycolysis and pyroptosis processes were found to be enriched in the HCC patients with high m7G risk. Finally, overexpression of NCBP2 could promote the proliferation, migration, and invasion of HC cells.

Conclusions

The m7G risk score was closely related to the prognosis, antitumor immune process, glycolysis, and malignant progression of HCC. NCBP2 has pro-oncogenic abilities, showing promise as a novel treatment target.

Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome

Despite known histological, biological, and clinical differences between lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), relatively little is known about the spatial differences in their corresponding immune contextures. Our study of over 1000 LUAD and LUSC tumors revealed that computationally derived patterns of tumor-infiltrating lymphocytes (TILs) on H&E images were different between LUAD (N = 421) and LUSC (N = 438), with TIL density being prognostic of overall survival in LUAD and spatial arrangement being more prognostically relevant in LUSC. In addition, the LUAD-specific TIL signature was associated with OS in an external validation set of 100 NSCLC treated with more than six different neoadjuvant chemotherapy regimens, and predictive of response to therapy in the clinical trial CA209-057 (n = 303). In LUAD, the prognostic TIL signature was primarily comprised of CD4+ T and CD8+ T cells, whereas in LUSC, the immune patterns were comprised of CD4+ T, CD8+ T, and CD20+ B cells. In both subtypes, prognostic TIL features were associated with transcriptomics-derived immune scores and biological pathways implicated in immune recognition, response, and evasion. Our results suggest the need for histologic subtype-specific TIL-based models for stratifying survival risk and predicting response to therapy. Our findings suggest that predictive models for response to therapy will need to account for the unique morphologic and molecular immune patterns as a function of histologic subtype of NSCLC.

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.

The Trinity: Interplay among Cancer Cells, Fibroblasts, and Immune Cells in Pancreatic Cancer and Implication of CD8+ T Cell-Orientated Therapy

The microenvironment in tumors is complicated and is constituted by different cell types and stromal proteins. Among the cell types, the abundance of cancer cells, fibroblasts, and immune cells is high and these cells work as the “Trinity” in promoting tumorigenesis. Although unidirectional or bidirectional crosstalk between two independent cell types has been well characterized, the multi-directional interplays between cancer cells, fibroblasts, and immune cells in vitro and in vivo are still unclear. We summarize recent studies in addressing the interaction of the “Trinity” members in the tumor microenvironment and propose a functional network for how these members communicate with each other. In addition, we discuss the underlying mechanisms mediating the interplay. Moreover, correlations of the alterations in the distribution and functionality of cancer cells, fibroblasts, and immune cells under different circumstances are reviewed. Finally, we point out the future application of CD8⁺ T cell-oriented therapy in the treatment of pancreatic cancer.

Pyroptosis-Related Risk Signature Exhibits Distinct Prognostic, Immune, and Therapeutic Landscapes in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a common abdominal cancer. The existing therapeutic approaches often fail to achieve satisfactory results. Pyroptosis, an inflammatory form of programmed cell death, provides new ideas for anticancer treatment. However, the roles of pyroptosis-related (PR) genes (PRGs) in HCC remain elusive. Methods: Differentially expressed genes (DEGs) (n = 22) were screened out using TCGA and GTEx databases. A novel PR risk signature was constructed through Lasso regression analysis. Its prognostic value was evaluated through a series of survival analyses and was tested in ICGC and GSE14520 cohorts. CIBERSORT, ssGSEA, and ESTIMATE methods were employed to determine the effects of the PR risk score on the tumor immune microenvironment (TIM). The TIDE scoring system, IMvigor210 cohort, GSE109211 dataset, and GSDC database were applied to explore the associations of the PR risk score with therapeutic effects. The biofunctions of WNK1 in hepatocellular cancer (HC) cells were confirmed through qPCR, colony formation, and Transwell assays. Results: Overall, 22 of 45 PRGs (48.9%) were abnormally expressed in HCC samples. Then, a PR risk signature consisting of eight PRGs was constructed. A high PR risk score led to an unfavorable prognosis. The PR risk score was identified as an independent prognostic factor of HCC and could increase the decision-making benefit of the traditional TNM model. In addition, we established a nomogram containing the clinical stage and PR risk score to predict the survival rates of HCC patients. The prognostic value of the PR model was successfully validated in ICGC and GSE14520 cohorts. Moreover, high PR risk conferred the decreased infiltration level of CD8⁺ T cells and weakened the activities of "cytolytic activity" pathways. As for therapeutic correlation, a high PR risk score seemed to imply a poor efficacy of PD-1/L1 inhibitors and sorafenib. Finally, the overexpression of WNK1 could promote the proliferation, migration, and invasion of HC cells. Conclusions: The PR risk score was closely related to the prognosis, antitumor immune process, therapeutic outcomes, and malignant progression of HCC. WNK1, the core regulator of pyroptosis, possesses pro-oncogenic abilities, showing promise as a novel treatment target.

An Integrated Pan-Cancer Analysis of ADAMTS12 and Its Potential Implications in Pancreatic Adenocarcinoma

Background

A Disintegrin and Metallopeptidase with Thrombospondin Type 1 Motif 12 (ADAMTS12), a member of the ADAMTS family of multidomain extracellular protease enzymes, is involved in the progression of many tumors. However, a pan-cancer analysis of this gene has not yet been performed. Its role in pancreatic adenocarcinoma (PAAD) also remains unclear.

Methods

The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression data (GTEx) databases were used to analyze ADAMTS12 expression in pan-cancer. We assessed the expression, clinical characteristics, prognostic significance, copy number alteration, methylation, and mutation of ADAMTS12 and its correlation with the tumor immune microenvironment. qRT-PCR and immunohistochemistry assays were also performed to validate the expression of ADAMTS12 in PAAD.

Results

Through bioinformatics analysis and preliminary experimental verification, ADAMTS12 was found to be substantially overexpressed in PAAD. High expression level of ADAMTS12 was correlated with worse survival rates in patients with PAAD and high infiltration levels of tumor-associated macrophages, cancer-associated fibroblasts, immune checkpoint proteins, and immunosuppressive genes.

Conclusion

Our findings suggest ADAMTS12 as a potential prognostic biomarker in PAAD. Elevated ADAMTS12 expression may also indicate an immunosuppressive microenvironment.

Development and validation of a novel 3-gene prognostic model for pancreatic adenocarcinoma based on ferroptosis-related genes

Background

Molecular markers play an important role in predicting clinical outcomes in pancreatic adenocarcinoma (PAAD) patients. Analysis of the ferroptosis-related genes may provide novel potential targets for the prognosis and treatment of PAAD.

Methods

RNA-sequence and clinical data of PAAD was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public databases. The PAAD samples were clustered by a non-negative matrix factorization (NMF) algorithm. The differentially expressed genes (DEGs) between different subtypes were used by “limma_3.42.2” package. The R software package clusterProfiler was used for functional enrichment analysis. Then, a multivariate Cox proportional and LASSO regression were used to develop a ferroptosis-related gene signature for pancreatic adenocarcinoma. A nomogram and corrected curves were constructed. Finally, the expression and function of these signature genes were explored by qRT-PCR, immunohistochemistry (IHC) and proliferation, migration and invasion assays.

Results

The 173 samples were divided into 3 categories (C1, C2, and C3) and a 3-gene signature model (ALOX5, ALOX12, and CISD1) was constructed. The prognostic model showed good independent prognostic ability in PAAD. In the GSE62452 external validation set, the molecular model also showed good risk prediction. KM-curve analysis showed that there were significant differences between the high and low-risk groups, samples with a high-risk score had a worse prognosis. The predictive efficiency of the 3-gene signature-based nomogram was significantly better than that of traditional clinical features. For comparison with other models, that our model, with a reasonable number of genes, yields a more effective result. The results obtained with qPCR and IHC assays showed that ALOX5 was highly expressed, whether ALOX12 and CISD1 were expressed at low levels in tissue samples. Finally, function assays results suggested that ALOX5 may be an oncogene and ALOX12 and CISD1 may be tumor suppressor genes.

Conclusions

We present a novel prognostic molecular model for PAAD based on ferroptosis-related genes, which serves as a potentially effective tool for prognostic differentiation in pancreatic cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02431-8.

Development of a Gene Risk Signature for Patients of Pancreatic Cancer

BACKGROUND

Pancreatic cancer is a highly malignant solid tumor with a high lethality rate, but there is a lack of clinical biomarkers that can assess patient prognosis to optimize treatment.

METHODS

Gene-expression datasets of pancreatic cancer tissues and normal pancreatic tissues were obtained from the GEO database, and differentially expressed genes analysis and WGCNA analysis were performed after merging and normalizing the datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were used to screen the prognosis-related genes in the modules with the strongest association with pancreatic cancer and construct risk signatures. The performance of the risk signature was subsequently validated by Kaplan-Meier curves, receiver operating characteristic (ROC), and univariate and multivariate Cox analyses.

RESULT

A three-gene risk signature containing CDKN2A, BRCA1, and UBL3 was established. Based on KM curves, ROC curves, and univariate and multivariate Cox regression analyses in the TRAIN cohort and TEST cohort, it was suggested that the three-gene risk signature had better performance in predicting overall survival.

CONCLUSION

This study identifies a three-gene risk signature, constructs a nomogram that can be used to predict pancreatic cancer prognosis, and identifies pathways that may be associated with pancreatic cancer prognosis.

Pyroptosis regulators exert crucial functions in prognosis, progression and immune microenvironment of pancreatic adenocarcinoma: a bioinformatic and in vitro research

Pyroptosis is an inflammatory programmed cell death, showing potentials to be a novel anti-cancer approach. However, the roles of pyroptosis-related (PR) genes (PRGs) in pancreatic adenocarcinoma (PAAD) remain elusive. In the present study, we constructed a novel PR risk signature through the lasso regression analysis. The risk signature was greatly conducive to PAAD prognostic assessment. PR risk score was identified as an independent prognostic factor and could distinguish the prognostic differences of most clinical subgroups. Meanwhile, it could improve the traditional prognostic models based on TNM-staging. Next, its prognostic value was also tested in five validation cohorts. Using CIBERSORT, ESTIMATE, and ssGSEA algorithms, the effects of PR risk signature on tumor immune microenvironment (TIM) were explored. High PR risk suppressed antitumor immune through decreasing the infiltrating levels of CD8 T and NK cells. The genomic information and histological expression of risk PRGs were uncovered by USCA and HPA databases. Somatic mutation, methylation alteration, and homozygous CNV of eight PRGs barely occurred in PAAD samples. As for therapeutic correlation, PR risk score may not predict the efficacy of PD-1/L1 inhibitors and was weakly associated with multiple drug susceptibilities. Finally, the biofunctions of toll like receptor 3 (TLR3) in pancreatic cancer (PC) cells were investigated through qPCR, MTT, colony formation, and Transwell assays. Overexpression of TLR3 could promote the proliferation, migration, and invasion of PC cells. In conclusion, PRGs play crucial roles in prognosis, progression, and immune microenvironment of PAAD. TLR3 is expected to be a promising therapeutic target.

Establishment of a Prognostic Signature of Stromal/Immune-Related Genes for Gastric Adenocarcinoma Based on ESTIMATE Algorithm

Different subtypes of gastric cancer differentially respond to immune checkpoint inhibitors (ICI). This study aimed to investigate whether the Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm is related to the classification and prognosis of gastric cancer and to establish an ESTIMATE-based gene signature to predict the prognosis for patients. The immune/stromal scores of 388 gastric cancer patients from TCGA were used in this analysis. The upregulated differentially expressed genes (DEGs) in patients with high stromal/immune scores were identified. The immune-related hub DEGs were selected based on protein-protein interaction (PPI) analysis. The prognostic values of the hub DEGs were evaluated in the TCGA dataset and validated in the GSE15460 dataset using the Kaplan-Meier curves. A prognostic signature was built using the hub DEGs by Cox proportional hazards model, and the accuracy was assessed using receiver operating characteristic (ROC) analysis. Different subtypes of gastric cancer had significantly different immune/stromal scores. High stromal scores but not immune scores were significantly associated with short overall survivals of TCGA patients. Nine hub DEGs were identified in PPI analysisThe expression of these hub DEG negatively correlated with the overall survival in the TCGA cohort, which was validated in the GSE15460 cohort. A 9-gene prognostic signature was constructed. The risk factor of patients was calculated by this signature. High-risk patients had significantly shorter overall survival than low-risk patients. ROC analysis showed that the prognostic model accurately identified high-risk individuals within different time frames. We established an effective 9-gene-based risk signature to predict the prognosis of gastric cancer patients, providing guidance for prognostic stratification.

Discovery of Cancer-Specific and Independent Prognostic Gene Subsets of the Slit-Robo Family Using TCGA-PANCAN Datasets

The Slit-Robo family of axon guidance molecules works in concert, playing important roles in organ development and cancer. Expressions of individual Slit-Robo genes have been used in calculating univariable hazard ratios (HR_uni) for predicting cancer prognosis in the literature. However, Slit-Robo members do not act independently; hence, hazard ratios from multivariable Cox regression (HR_multi) on the whole gene set can further lead to identification of cancer-specific, novel, and independent prognostic gene pairs or modules. Herein, we obtained mRNA expressions of the Slit-Robo family consisting of four Robos (ROBO1/2/3/4) and three Slits (SLIT1/2/3), along with four types of survival outcome across cancers found in the Cancer Genome Atlas (TCGA). We used cluster heat maps to visualize closely associated pairs/modules of prognostic genes across 33 different cancers. We found a smaller number of significant genes in HR_multi than in HR_uni, suggesting that the former analysis was less redundant. High ROBO4 expression emerged as relatively protective within the family, in both types of HR analyses. Multivariable Cox regression, on the other hand, revealed significantly more HR signatures containing Slit-Robo pairs acting in opposing directions than those containing Slit-Slit or Robo-Robo pairs for disease-specific survival. Furthermore, we discovered, through the online app SmulTCan's lasso regression, Slit-Robo gene subsets that significantly differentiated between high- versus low-risk prognosis patient groups, particularly for renal cancers and low-grade glioma. The statistical pipeline reported herein can help test independent and significant pairs/modules within a codependent gene family for cancer prognostication, and thus should also prove useful in personalized/precision medicine research.