T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers

A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma

An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8+ T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8+ T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC.

Integrated analysis of single-cell and bulk RNA-sequencing reveals a novel signature based on NK cell marker genes to predict prognosis and immunotherapy response in gastric cancer

Natural killer (NK) cells play essential roles in the tumor development, diagnosis, and prognosis of tumors. In this study, we aimed to establish a reliable signature based on marker genes in NK cells, thus providing a new perspective for assessing immunotherapy and the prognosis of patients with gastric cancer (GC). We analyzed a total of 1560 samples retrieved from the public database. We performed a comprehensive analysis of single-cell RNA-sequencing (scRNA-seq) data of gastric cancer and identified 377 marker genes for NK cells. By performing Cox regression analysis, we established a 12-gene NK cell-associated signature (NKCAS) for the Cancer Genome Atlas (TCGA) cohort, that assigned GC patients into a low-risk group (LRG) or a high-risk group (HRG). In the TCGA cohort, the areas under curve (AUC) value were 0.73, 0.81, and 0.80 at 1, 3, and 5 years. External validation of the predictive ability for the signature was then validated in the Gene Expression Omnibus (GEO) cohorts (GSE84437). The expression levels of signature genes were measured and validated in GC cell lines by real-time PCR. Moreover, NKCAS was identified as an independent prognostic factor by multivariate analysis. We combined this with a variety of clinicopathological characteristics (age, M stage, and tumor grade) to construct a nomogram to predict the survival outcomes of patients. Moreover, the LRG showed higher immune cell infiltration, especially CD8+ T cells and NK cells. The risk score was negatively associated with inflammatory activities. Importantly, analysis of the independent immunotherapy cohort showed that the LRG had a better prognosis and immunotherapy response when compared with the HRG. The identification of NK cell marker genes in this study suggests potential therapeutic targets. Additionally, the developed predictive signatures and nomograms may aid in the clinical management of GC.

Identification of an immune-related signature as a prognostic classifier for patients with early-stage head and neck squamous cell carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) is the most common type and accounts for 90% of all head and neck cancer cases. Despite advances in early diagnosis and treatment strategies-chemotherapy, surgical resection, and radiotherapy-5-year survival remains grim. For patients with early-stage HNSCC, accurately predicting clinical outcomes is challenging. Considering the pivotal role of the immune system in HNSCC, we developed a reliable immune-related gene signature (IRGS) and explored its predictive accuracy in patients with early-stage HNSCC.

Methods

We examined immune gene expression profiles and clinical information from 230 early-stage HNSCC specimens, including 100 cases from The Cancer Genome Atlas (TCGA), 49 cases from the Gene Expression Omnibus (GEO; GSE65858), and 81 cases from an independent clinical cohort. The prognostic signature was constructed using Kaplan-Meier analysis and the least absolute shrinkage and selection operator (LASSO) Cox algorithm. We also explored the IRGS-related biological pathways and immune landscape using bioinformatics analysis.

Results

A nine-immune-gene signature was generated to significantly stratify patients into high and low-risk groups. High risk patients exhibited shorter survival time [hazard ratio (HR) =13.795, 95% confidence interval (CI): 3.275-58.109, P<0.001]. The signature demonstrated robust prognostic ability in the training and validation sets and could independently predict overall survival (OS) and relapse-free survival (RFS). Subsequently, the receiver operating characteristic (ROC) curve and C-index confirmed the signature's predictive accuracy compared to clinical parameters. Additionally, cases classified as low risk showed more immune cell infiltration than high-risk cases.

Conclusions

Our novel IRGS is a reliable and robust classifier for accurate patient stratification and prognostic evaluation. Future studies will attempt to affirm the signature's clinical application to early-stage HNSCC.

Molecular and immunological features of TREM1 and its emergence as a prognostic indicator in glioma

Triggering receptor expressed on myeloid cells 1 (TREM1), which belongs to the Ig-like superfamily expressed on myeloid cells, is reportedly involved in various diseases but has rarely been studied in glioma. In this study, the prognostic value and functional roles of TREM2 in glioma were analyzed. TERM1 was observed to be significantly upregulated in GBM compared to in other grade gliomas and was associated with poor prognosis. Increased TREM1 accompanied distinct mutation and amplification of driver oncogenes. Moreover, gene ontology and KEGG analyses showed that TREM1 might play a role in immunologic biological processes in glioma. TREM1 was also found to be tightly correlated with immune checkpoint molecules. xCell research revealed a link between TREM1 expression and multiple immune cell types, especially monocytes and macrophages. Single-cell analysis and immunofluorescence results showed that macrophages expressed TREM1. In vitro, inhibition of TREM1 signaling could result in a decrease in tumor-promoting effects of monocytes/TAMs. In summary, TREM1 may be a potential independent prognostic factor and immune target, which might provide new avenues to improve the efficacy of immunotherapy in glioma patients.

Identification of the role of MCM6 in bladder cancer prognosis, immunotherapy response, and in vitro experimental investigation using multi-omics analysis

BACKGROUND

The tumor-promoting effects of MCM6 in numerous tumors have been widely revealed, yet its specific role in bladder cancer (BLCA) is still elusive. The objective of this research was to explore the underlying impact of MCM6 on BLCA.

METHODS

Integrating transcriptomic and proteomic data, MCM6 was identified to be strongly correlated with BLCA through weighted gene co-expression network analysis(WGCNA) and venn analyses. Then, the clinical value of MCM6 was validated with public database data. The different molecular/immune characteristics and the benefit of immunotherapy were also found in MCM6-defined subgroups. Additionally, single-cell RNA sequencing (scRNA-seq) data was choose for quantify MCM6 expression in the distinct BLCA cell types. The biological role of MCM6 were evaluated via in vitro functional experiments.

RESULTS

It was testified that the MCM6 could distinguish patients outcome in TCGA and GEO cohorts. Moreover, compared with the MCM6 low-expression group, the MCM6 high-expression group was related to more tumor-promoting related pathways, aggressive phenotypes, and benefit from immunotherapy. Analysis of scRNA-seq data resulted in MCM6 was mainly expressed in BLCA epithelial cells and the proportion of MCM6-expressing tumor epithelial cells is higher than the normal epithelial cells. Moreover, vitro experiments demonstrated that MCM6 knockdown repressed proliferation, cell cycle, migration, and invasion of BLCA cells.

CONCLUSION

This research indicated MCM6 is a promising marker for both prognosis and immunotherapy benefit and could promote the cells proliferation, invasion and migration in BLCA.

Breast cancers with high proliferation and low ER-related signalling have poor prognosis and unique molecular features with implications for therapy

BACKGROUND

Luminal breast cancers with high proliferation (MKShi) and low ER-related signalling (ERSlo) have a poor prognosis. We investigated treatment responses and molecular features of MKShi/ERSlo tumours to inform potential therapies.

METHODS

Gene expression data from patients who received neoadjuvant chemotherapy (NAC) without (MDACC, N = 199) or with pembrolizumab (I-SPY2, N = 40), or endocrine therapy (NET) without (POETIC, N = 172) or with palbociclib (NeoPalAna, N = 32) were analyzed to assess treatment response by MKS/ERS-subgroups. TCGA was used to assess the mutational landscape and biomarkers associated with palbociclib-resistance (Cyclin-E, RBsig, IRPR) and immunotherapy-response (TMB, TILs, T-cell inflamed) by MKS/ERS-subgroups.

RESULTS

Compared to MKShi/ERShi tumours, MKShi/ERSlo tumours had higher pathological response rates to NAC (22% vs 8%, p = 0.06) but a higher recurrence risk (4-year metastasis-free survival 70% vs 94%, p = 0.01). MKShi/ERSlo tumours frequently harboured TP53 (34%) and PIK3CA (33%) mutations, and showed high expression of Cyclin-E, RBsig and IRPR, high TMB and elevated TIL and T-cell inflamed metagene expression. MKShi/ERSlo tumours retained high proliferation after NET with or without palbociclib but had higher pathological complete response rates when pembrolizumab was added to NAC (42% vs 21%, p = 0.07).

CONCLUSIONS

MKShi/ERSlo tumours have dismal outcomes and are enriched in chemotherapy-sensitive but ET- and palbociclib-resistant tumours. Biomarker analysis and clinical data suggest a potential role for immunotherapy in this group.

Prediction of Prognosis and Immunotherapy Response with a Novel Natural Killer Cell Marker Genes Signature in Osteosarcoma

Background: Natural killer (NK) cells are characterized by their antitumor efficacy without previous sensitization, which have attracted attention in tumor immunotherapy. The heterogeneity of osteosarcoma (OS) has hindered therapeutic application of NK cell-based immunotherapy. The authors aimed to construct a novel NK cell-based signature to identify certain OS patients more responsive to immunotherapy. Materials and Methods: A total of eight publicly available datasets derived from patients with OS were enrolled in this study. Single-cell RNA sequencing data obtained from the Gene Expression Omnibus (GEO) database were analyzed to screen NK cell marker genes. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis was used to construct an NK cell-based prognostic signature in the TARGET-OS dataset. The differences in immune cell infiltration, immune system-related metagenes, and immunotherapy response were evaluated among risk subgroups. Furthermore, this prognostic signature was experimentally validated by reverse transcription-quantitative real-time PCR (RT-qPCR). Results: With differentially expressed NK cell marker genes screened out, a five-gene NK cell-based prognostic signature was constructed. The prognostic predictive accuracy of the signature was validated through internal clinical subgroups and external GEO datasets. Low-risk OS patients contained higher abundances of infiltrated immune cells, especially CD8 T cells and naive CD4 T cells, indicating that T cell exhaustion states were present in the high-risk OS patients. As indicated from correlation analysis, immune system-related metagenes displayed a negative correlation with risk scores, suggesting the existence of immunosuppressive microenvironment in OS. In addition, based on responses to immune checkpoint inhibitor therapy in two immunotherapy datasets, the signature helped predict the response of OS patients to anti-programmed cell death protein 1 (PD-1) or anti-programmed cell death ligand 1 (PD-L1) therapy. RT-qPCR results demonstrated the roughly consistent relationship of these five gene expressions with predicting outcomes. Conclusions: The NK cell-based signature is likely to be available for the survival prediction and the evaluation of immunotherapy response of OS patients, which may shed light on subsequent immunotherapy choices for OS patients. In addition, the authors revealed a potential link between immunosuppressive microenvironment and OS.

Molecular and clinical characterization of ANG expression in gliomas and its association with tumor-related immune response

Background

Angiogenin (ANG) has been widely reported as a crucial molecular regulator in multiple malignancies. However, its role in gliomagenesis remains unclear. This study aimed to investigate the molecular and clinical characterization of ANG expression at transcriptome level and the association with glioma-related immune response.

Methods

A total of 301 glioma samples with mRNA microarray data (CGGA301) was obtained from the official website of CGGA project for yielding preliminary results, followed by validation in two independent RNAseq datasets, including TCGA with 697 samples and CGGA325 with 325 patients. Moreover, CGGA single-cell RNAseq (scRNAseq) data were analyzed to identify differential and dynamic ANG expression in different cells. Immunohistochemistry was performed to evaluate ANG protein expression across different WHO grades in a tissue microarray (TMA). Figure generation and statistical analysis were conducted using R software.

Results

ANG expression was associated with clinical features, malignant phenotypes, and genomic alterations. Based on significantly correlated genes of ANG, subsequent gene ontology (GO) and gene set enrichment analysis (GSEA) concordantly pointed to the significant association of ANG in immune-related biological processes. Moreover, ANG showed robust correlations with canonical immune checkpoint molecules, including PD1 signaling, CTLA4, TIM3, and B7H3. Gene sets variation analysis (GSVA) found that ANG was particularly associated with activities of macrophages and antigen presentation cells (APCs) in both LGG and GBM across different datasets. Furthermore, the higher-ANG milieu seemed to recruit monocyte-macrophage lineage and dendritic cells into the glioma microenvironment. According to scRNAseq analysis, ANG was mainly expressed by neoplastic cells and tumor-associated macrophages (TAMs) and was correlated with the initiation and progression of tumor cells and the polarization of TAMs. Finally, Kaplan-Meier plots demonstrated that higher expression of ANG was significantly correlated with shorter survival in gliomas. Cox regression analysis further confirmed ANG as an independent predictor of prognosis for gliomas of all three datasets.

Conclusion

ANG is significantly correlated with a range of malignant and aggressive characteristics in gliomas and reveals considerable prognostic value for glioma patients. ANG seems to be primarily associated with immune activities of macrophages and APCs in gliomas. Furthermore, ANG is mainly expressed in neoplastic cells and TAMs and is involved in the initiation and progression of neoplastic cells as well as macrophage polarization.

B-cells and regulatory T-cells in the microenvironment of HER2+ breast cancer are associated with decreased survival: a real-world analysis of women with HER2+ metastatic breast cancer

BACKGROUND

Despite major improvements in treatment of HER2-positive metastatic breast cancer (MBC), only few patients achieve complete remission and remain progression free for a prolonged time. The tumor immune microenvironment plays an important role in the response to treatment in HER2-positive breast cancer and could contain valuable prognostic information. Detailed information on the cancer-immune cell interactions in HER2-positive MBC is however still lacking. By characterizing the tumor immune microenvironment in patients with HER2-positive MBC, we aimed to get a better understanding why overall survival (OS) differs so widely and which alternative treatment approaches may improve outcome.

METHODS

We included all patients with HER2-positive MBC who were treated with trastuzumab-based palliative therapy in the Netherlands Cancer Institute between 2000 and 2014 and for whom pre-treatment tissue from the primary tumor or from metastases was available. Infiltrating immune cells and their spatial relationships to one another and to tumor cells were characterized by immunohistochemistry and multiplex immunofluorescence. We also evaluated immune signatures and other key pathways using next-generation RNA-sequencing data. With nine years median follow-up from initial diagnosis of MBC, we investigated the association between tumor and immune characteristics and outcome.

RESULTS

A total of 124 patients with 147 samples were included and evaluated. The different technologies showed high correlations between each other. T-cells were less prevalent in metastases compared to primary tumors, whereas B-cells and regulatory T-cells (Tregs) were comparable between primary tumors and metastases. Stromal tumor-infiltrating lymphocytes in general were not associated with OS. The infiltration of B-cells and Tregs in the primary tumor was associated with unfavorable OS. Four signatures classifying the extracellular matrix of primary tumors showed differential survival in the population as a whole.

CONCLUSIONS

In a real-world cohort of 124 patients with HER2-positive MBC, B-cells, and Tregs in primary tumors are associated with unfavorable survival. With this paper, we provide a comprehensive insight in the tumor immune microenvironment that could guide further research into development of novel immunomodulatory strategies.

HCK Promotes High-Grade Serous Ovarian Cancer Tumorigenesis through CD44 and NOTCH3 Signaling

High-grade serous ovarian cancer (HGSOC) is a highly aggressive and lethal subtype of ovarian cancer. While most patients initially respond to standard-of-care treatment, the majority will eventually relapse and succumb to their disease. Despite significant advances in our understanding of this disease, the mechanisms that govern the distinctions between HGSOC with good and poor prognosis remain unclear. In this study, we implemented a proteogenomic approach to analyze gene expression, proteomic and phosphoproteomic profiles of HGSOC tumor samples to identify molecular pathways that distinguish HGSOC tumors relative to clinical outcome. Our analyses identify significant upregulation of hematopoietic cell kinase (HCK) expression and signaling in poor prognostic HGSOC patient samples. Analyses of independent gene expression datasets and IHC of patient samples confirmed increased HCK signaling in tumors relative to normal fallopian or ovarian samples and demonstrated aberrant expression in tumor epithelial cells. Consistent with the association between HCK expression and tumor aggressiveness in patient samples, in vitro phenotypic studies showed that HCK can, in part, promote cell proliferation, colony formation, and invasive capacity of cell lines. Mechanistically, HCK mediates these phenotypes, partly through CD44 and NOTCH3-dependent signaling, and inhibiting CD44 or NOTCH3 activity, either genetically or through gamma-secretase inhibitors, can revert HCK-driven phenotypes.

IMPLICATIONS

Collectively, these studies establish that HCK acts as an oncogenic driver of HGSOC through aberrant activation of CD44 and NOTCH3 signaling and identifies this network as a potential therapeutic opportunity in a subset of patients with aggressive and recurrent HGSOC.

Dual Functions of T Lymphocytes in Breast Carcinoma: From Immune Protection to Orchestrating Tumor Progression and Metastasis

Breast cancer (BC) is the most common cancer type in women and the second leading cause of death. Despite recent advances, the mortality rate of BC is still high, highlighting a need to develop new treatment strategies including the modulation of the immune system and immunotherapies. In this regard, understanding the complex function of the involved immune cells and their crosstalk with tumor cells is of great importance. T-cells are recognized as the most important cells in the tumor microenvironment and are divided into several subtypes including helper, cytotoxic, and regulatory T-cells according to their transcription factors, markers, and functions. This article attempts to provide a comprehensive review of the role of T-cell subsets in the prognosis and treatment of patients with BC, and crosstalk between tumor cells and T-cells. The literature overwhelmingly contains controversial findings mainly due to the plasticity of T-cell subsets within the inflammatory conditions and the use of different panels for their phenotyping. However, investigating the role of T-cells in BC immunity depends on a variety of factors including tumor types or subtypes, the stage of the disease, the localization of the cells in the tumor tissue and the presence of different cells or cytokines.

Clinical characterization of EFHD2 (swiprosin-1) in Glioma-associated macrophages and its role in regulation of immunosuppression

Glioblastoma has been extensively studied due to its high mortality and short survival. The evolution mechanism of tumor-associated macrophages (TAMs) to Glioma-associated microglia and macrophages (GAMs) in the tumor microenvironment (TME) remains to be elucidated. The tumor cell-to-cell interaction patterns have not been well defined yet. The EF-Hand Domain Family Member D2 (EFHD2) has been reported to be differentially expressed as an immunomodulatory molecule in a variety of cancers. But large-scale clinical data from multiple ethnic communities have not been used to investigate the role of EFHD2 in glioma. RNA-seq data from 313 or 657 glioma patients from the Chinese Glioma Genome Atlas (CGGA) database and 603 glioma patients from the Cancer Genome Atlas (TCGA) database were analyzed retrospectively. Cell localization was performed using single-cell sequencing data from the CGGA database and the GSE131928 dataset. Mouse glioma cell lines and primary macrophages isolated from Efhd2 knockout mice were co-cultured to validate the immunomodulatory effects of EFHD2 on macrophages and the remodeling of TME of glioblastoma. EFHD2 is enriched in high-grade gliomas, isocitrate dehydrogenase wild-type, and 1p/19q non-co-deficient gliomas. It is a potential biomarker of glioma-proneuronal subtypes and an independent prognostic factor for overall survival in patients with malignant glioblastoma. EFHD2 regulates the monocyte-macrophage system function and positively correlates with immunosuppressive checkpoints. Further experimental data demonstrates that Efhd2 influences the polarization state of GAMs and inhibits the secretion of TGF-β1. In vitro experiments have revealed that macrophages lacking Efhd2 suppress the vitality of two glioma cell lines and decelerate the growth of glioma xenografts. In conclusion, EFHD2 promises to be a key target for TME-related immunotherapy.

Identification of Breast Cancer LCK Proto-Oncogene as a Master Regulator of TNBC Neutrophil Enrichment and Polarization

The role of neutrophils in breast cancer shows that the N1 proinflammatory subtype can suppress and attack the tumor. In contrast, the N2 pro-tumor subtype aids the tumor in its survival, progression, and metastasis. Recently, more focus has been directed to the role of innate myeloid cells, specifically neutrophils, in regulating the responses of lymphoid populations both in the progression of cancer and in response to therapy. However, the exact crosstalk between breast cancer cells and neutrophils is poorly understood. In this work, we used in-silico assays to investigate the role of the bidirectional effect of neutrophils on metastatic TNBC. Our reanalysis of publicly available data reveals that most TNBC's classified within the CE2 subtype are leukocyte-poor and have four major cell types in their ecotypes: dendritic cells, macrophages, fibroblasts, and epithelial cells. Further immune deconvolution of these patients revealed that a few cells significantly differed between groups, including macrophages, neutrophils, and T cells. All BC showed lower infiltrating neutrophils compared to healthy surrounding tissue. Treated TNBCs improved the count of infiltrating neutrophils in TNBC. Most TNBC patients have a unique CE2 ecotype, characterized by more basal-like epithelial cells, more neutrophils, and fewer mononuclear lymphocytes (B cells, macrophages M1, T cell CD4+ (non-regulatory), and T cell CD8+ and T regs). This can be related to our finding that CE2 TNBCs are characterized by a lower LCK and higher ERBB2, and their top DEGs are related to leukocyte activation and NFKB pathway.

Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. The dataset used for the research is the Lung Harvard 2 Dataset (LH2) which consists of 150 Adenocarcinoma subjects and 31 Mesothelioma subjects. The paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Nonlinear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.

Comprehensive analysis of the clinical and biological significances of cholesterol metabolism in lower-grade gliomas

BACKGROUND

As a component of membrane lipids and the precursor of oxysterols and steroid hormones, reprogrammed cholesterol metabolism contributes to the initiation and progression of multiple cancers. Thus, we aim to further investigate the significances of cholesterol metabolism in lower-grade gliomas (LGGs).

METHODS

The present study included 413 LGG samples from TCGA RNA-seq dataset (training cohort) and 172 LGG samples from CGGA RNA-seq dataset (validation cohort). The cholesterol metabolism-related signature was identified by the LASSO regression model. Bioinformatics analyses were performed to explore the functional roles of this signature in LGGs. Kaplan-Meier and Cox regression analyses were enrolled to estimate prognostic value of the risk signature.

RESULTS

Our findings suggested that cholesterol metabolism was tightly associated clinicopathologic features and genomic alterations of LGGs. Bioinformatics analyses revealed that cholesterol metabolism played a key role in immunosuppression of LGGs, mainly by promoting macrophages polarization and T cell exhaustion. Kaplan-Meier curve and Cox regression analysis showed that cholesterol metabolism was an independent prognostic indicator for LGG patients. To improve the clinical application value of the risk signature, we also constructed a nomogram model to predict the 1-, 3- and 5-year survival of LGG patients.

CONCLUSION

The cholesterol metabolism was powerful prognostic indicator and could serve as a promising target to enhance personalized treatment of LGGs.

A novel machine learning-based programmed cell death-related clinical diagnostic and prognostic model associated with immune infiltration in endometrial cancer

Background

To explore the underlying mechanism of programmed cell death (PCD)-related genes in patients with endometrial cancer (EC) and establish a prognostic model.

Methods

The RNA sequencing data (RNAseq), single nucleotide variation (SNV) data, and corresponding clinical data were downloaded from TCGA. The prognostic PCD-related genes were screened and subjected to consensus clustering analysis. The two clusters were compared by weighted correlation network analysis (WGCNA), immune infiltration analysis, and other analyses. The least absolute shrinkage and selection operator (LASSO) algorithm was used to construct the PCD-related prognostic model. The biological significance of the PCD-related gene signature was evaluated through various bioinformatics methods.

Results

We identified 43 PCD-related genes that were significantly related to prognoses of EC patients, and classified them into two clusters via consistent clustering analysis. Patients in cluster B had higher tumor purity, higher T stage, and worse prognoses compared to those in cluster A. The latter generally showed higher immune infiltration. A prognostic model was constructed using 11 genes (GZMA, ASNS, GLS, PRKAA2, VLDLR, PRDX6, PSAT1, CDKN2A, SIRT3, TNFRSF1A, LRPPRC), and exhibited good diagnostic performance. Patients with high-risk scores were older, and had higher stage and grade tumors, along with worse prognoses. The frequency of mutations in PCD-related genes was correlated with the risk score. LRPPRC, an adverse prognostic gene in EC, was strongly correlated with proliferation-related genes and multiple PCD-related genes. LRPPRC expression was higher in patients with higher clinical staging and in the deceased patients. In addition, a positive correlation was observed between LRPPRC and infiltration of multiple immune cell types.

Conclusion

We identified a PCD-related gene signature that can predict the prognosis of EC patients and offer potential targets for therapeutic interventions.

Exploration of prognosis and immunometabolism landscapes in ER+ breast cancer based on a novel lipid metabolism-related signature

Introduction

Lipid metabolic reprogramming is gaining attention as a hallmark of cancers. Recent mounting evidence indicates that the malignant behavior of breast cancer (BC) is closely related to lipid metabolism. Here, we focus on the estrogen receptor-positive (ER+) subtype, the most common subgroup of BC, to explore immunometabolism landscapes and prognostic significance according to lipid metabolism-related genes (LMRGs).

Methods

Samples from The Cancer Genome Atlas (TCGA) database were used as training cohort, and samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), Gene Expression Omnibus (GEO) datasets and our cohort were applied for external validation. The survival-related LMRG molecular pattern and signature were constructed by unsupervised consensus clustering and least absolute shrinkage and selection operator (LASSO) analysis. A lipid metabolism-related clinicopathologic nomogram was established. Gene enrichment and pathway analysis were performed to explore the underlying mechanism. Immune landscapes, immunotherapy and chemotherapy response were further explored. Moreover, the relationship between gene expression and clinicopathological features was assessed by immunohistochemistry.

Results

Two LMRG molecular patterns were identified and associated with distinct prognoses and immune cell infiltration. Next, a prognostic signature based on nine survival-related LMRGs was established and validated. The signature was confirmed to be an independent prognostic factor and an optimal nomogram incorporating age and T stage (AUC of 5-year overall survival: 0.778). Pathway enrichment analysis revealed differences in immune activities, lipid biosynthesis and drug metabolism by comparing groups with low- and high-risk scores. Further exploration verified different immune microenvironment profiles, immune checkpoint expression, and sensitivity to immunotherapy and chemotherapy between the two groups. Finally, arachidonate 15-lipoxygenase (ALOX15) was selected as the most prominent differentially expressed gene between the two groups. Its expression was positively related to larger tumor size, more advanced tumor stage and vascular invasion in our cohort (n = 149).

Discussion

This is the first lipid metabolism-based signature with value for prognosis prediction and immunotherapy or chemotherapy guidance for ER+ BC.

The DEAD-box RNA helicase, DDX60, Suppresses immunotherapy and promotes malignant progression of pancreatic cancer

Excessive proliferation, invasion, metastasis, and immune resistance in pancreatic cancer (PC) makes it one of the most lethal malignant tumors. Recently, DDX60 was found to be involved in the development of various tumors and in immunotherapy. Therefore, we aimed to investigate whether DDX60 is a new factor involved in PC immunotherapy. The DDX60 mRNA was screened using transcriptome sequencing (RNA-seq). The Cox and survival analysis of DDX60 was performed using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. In addition, clinical and immune infiltration data in the databases were analyzed and plotted using the R language. Clinical samples and in vitro experiments were used to determine the molecular evolution of DDX60 during PC progression. We found that DDX60 was upregulated in PC tissues (P value = 0.0083) and was associated with poor prognosis and short survival time of patients with PC. Results of Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set variation analyses showed that viral defense, tumor, and immune-related pathways were significantly enriched in samples with high DDX60 expression. The Pearson correlation test demonstrated that DDX60 expression correlated strongly with immune checkpoint and immune system-related metagene clusters. Our results indicated that DDX60 promoted cell proliferation, migration, and invasion and was related to poor prognosis and immune resistance. Therefore, DDX60 may be a promising novel target for PC immunotherapy.

Characterization of the immune environment in pregnancy-associated breast cancer

Pregnancy-associated breast cancer (PrBC) is a rare and clinically challenging condition. Specific immune mechanisms and pathways are involved in maternal-fetal tolerance and tumor-host immunoediting. The comprehension of the molecular processes underpinning this immune synergy in PrBC is needed to improve patients' clinical management. Only a few studies focused on the immune biology of PrBC and attempted to identify bona fide biomarkers. Therefore, clinically actionable information remains extremely puzzling for these patients. In this review article, we discuss the current knowledge on the immune environment of PrBC, in comparison with pregnancy-unrelated breast cancer and in the context of maternal immune changes during pregnancy. A particular emphasis is given to the actual role of potential immune-related biomarkers for PrBC clinical management.

KYNU-related transcriptome profile and clinical outcome from 2994 breast tumors

The Catabolism of tryptophan modulates the immunosuppressive microenvironment in tumors. KYNU (Kynureninase) served as an enzyme involved in amino acid tryptophan catabolism through the kynurenine pathway. The molecular and clinical characteristics of KYNU remain unclear, and the impact of KYNU on the immune response has not been reported until now. We analyzed large-scale transcriptome data and related clinical information on 2994 breast cancer patients to characterize KYNU's role in breast cancer. There was a strong correlation between KYNU expression and major molecular and clinical characteristics, and it was more likely to be overexpressed in patients with higher malignancy subtypes. Inflammatory and immune responses were strongly correlated with KYNU. KYNU was also associated with immune modulators at the pan-cancer level, particularly its potential synergistic role with other immune checkpoints in breast cancer. KYNU expression was linked to the malignancy grade of breast cancer and predicted poorer outcomes. Tryptophan catabolism might play an important role in modulating the tumor immune microenvironment through KYNU. More significantly, KYNU might synergize with CTLA4, PDL2, IDO1, and other immune checkpoints, contributing to the development of combination cancer immunotherapy targeting KYNU and other checkpoints. As far as we are aware, this is the biggest and most thorough study describing KYNU's role in breast cancer.

Adenylate kinase 2 is a biomarker related to the prognosis of glioma and the immune microenvironment

BACKGROUND

Among the brain and the other central nervous system, gliomas are the most prevalent malignant primary tumors. Adenylate kinase 2 (AK2) is generally thought to be crucial for energy metabolism and signal transduction. Several disorders are correlated with its aberrant expression. However, it is unclear what functions AK2 might have in gliomas.

METHODS

We investigated the relationship between AK2 expression and clinicopathological features of glioma patients using information obtained from public databases and patient tissue microarrays. AK2 knockdown glioma cell lines were constructed to explore how AK2 affects glioma progress. The association between AK2 and the immune microenvironment in gliomas was evaluated by multiple methods.

RESULTS

AK2 expression was higher in glioma samples than in normal brain tissues. Older patients and those with higher-grade, IDH-wildtype, 1p/19q codeletion-free, and MGMT-unmethylated tumors had higher levels of AK2 expression, linking to poor outcomes. Thus, gliomas with high AK2 expression have a worse prognosis. GO and KEGG analyses demonstrated that AK2 was relevant to cell division and DNA replication. Downregulation of AK2 suppresses cell proliferation, migration, and colony formation of glioma cell lines in vitro. AK2 expression was positively connected to the inhibitory immune checkpoints, also correlating with immune infiltration degree.

CONCLUSIONS

In this study, AK2 may be a potential biological target for more precise molecular therapy of gliomas, since its high expression is associated with worse outcomes and a more malignant immune microenvironment.

Novel methylation-related long non-coding RNA clinical outcome prediction method: the clinical phenotype and immune infiltration research in low-grade gliomas

Background

Recent studies have suggested that long non-coding RNAs (lncRNAs) may play crucial role in low-grade glioma; however, the underlying mechanisms linking them to epigenetic methylation remain unclear.

Methods

We downloaded expression level data for regulators associated with N1 methyladenosine (m1A), 5-methyladenine (m5C), and N6 methyladenosine (m6A) (M1A/M5C/M6A) methylation from the Cancer Genome Atlas-low-grade glioma (TCGA-LGG) database. We identified the expression patterns of lncRNAs, and selected methylation-related lncRNAs using Pearson correlation coefficient>0.4. Non-negative matrix dimensionality reduction was then used to determine the expression patterns of the methylation-associated lncRNAs. We constructed a weighted gene co-expression network analysis (WGCNA) network to explore the co-expression networks between the two expression patterns. Functional enrichment of the co-expression network was performed to identify biological differences between the expression patterns of different lncRNAs. We also constructed prognostic networks based on the methylation presence in lncRNAs in low-grade gliomas.

Results

We identified 44 regulators by literature review. Using a correlation coefficient greater than 0.4, we identified 2330 lncRNAs, among which 108 lncRNAs with independent prognostic values were further screened using univariate Cox regression at P< 0.05. Functional enrichment of the co-expression networks revealed that regulation of trans-synaptic signaling, modulation of chemical synaptic transmission, calmodulin binding, and SNARE binding were mostly enriched in the blue module. The calcium and CA2 signaling pathways were associated with different methylation-related long non-coding chains. Using the Least Absolute Shrinkage Selector Operator (LASSO) regression analysis, we analyzed a prognostic model containing four lncRNAs. The model's risk score was 1.12 *AC012063 + 0.74 * AC022382 + 0.32 * AL049712 + 0.16 * GSEC. Gene set variation analysis (GSVA) revealed significant differences in mismatch repair, cell cycle, WNT signaling pathway, NOTCH signaling pathway, Complement and Cascades, and cancer pathways at different GSEC expression levels. Thus, these results suggest that GSEC may be involved in the proliferation and invasion of low-grade glioma, making it a prognostic risk factor for low-grade glioma.

Conclusion

Our analysis identified methylation-related lncRNAs in low-grade gliomas, providing a foundation for further research on lncRNA methylation. We found that GSEC could serve as a candidate methylation marker and a prognostic risk factor for overall survival in low-grade glioma patients. These findings shed light on the underlying mechanisms of low-grade glioma development and may facilitate the development of new treatment strategies.

Single-Cell Sequencing-Based Validation of T Cell-Associated Diagnostic Model Genes and Drug Response in Crohn's Disease

Crohn's disease is a highly heterogeneous autoimmune disease with a unique inflammatory phenotype of T cells at the lesion site. We aim to further explore the diagnosis of Crohn's disease and drug prediction of T cell marker gene expression. We obtained single-cell expression profile data from 22 CDs or normal samples and performed cell annotation and cellular communication analysis. Through the intersection of T cell marker genes, differential genes, and WGCNA results, we identified T cell-specific key genes and their immune landscapes and potential pathogenesis, and validated them across multiple datasets and patient tissue samples. We also explored the differentiation characteristics of genes by pseudo-temporal analysis and assessed their diagnostic performance and drug sensitivity by molecular docking. Finally, we extended this study to the prognosis of IBD-associated colon cancer. TNF-centered 5-gene diagnostic model not only has excellent diagnostic efficacy, but is also closely associated with KRAS, P53, and IL6/JAK/STAT3 pathways and physiological processes, such as EMT, coagulation, and apoptosis. In addition, this diagnostic model may have potential synergistic immunotherapeutic effects, with positive correlations with immune checkpoints such as CTLA4, CD86, PDCD1LG2, and CD40. Molecular docking demonstrated that BIRC3 and ANXA1 have strong binding properties to Azathioprine and Glucoocorticoid. Furthermore, the 5-gene model may suggest antagonism to IFX and prognosis for colon cancer associated with inflammatory bowel disease. Single-cell sequencing targeting T cell-related features in patients with Crohn's disease may aid in new diagnostic decisions, as well as the initial exploration of high-potential therapies.

Development and Validation of a Prognostic Risk Model Based on Nature Killer Cells for Serous Ovarian Cancer

Nature killer (NK) cells are increasingly considered important in tumor microenvironment, but their role in predicting the prognosis of ovarian cancer has not been revealed. This study aimed to develop a prognostic risk model for ovarian cancer based on NK cells. Firstly, differentially expressed genes (DEGs) of NK cells were found by single-cell RNA-sequencing dataset analysis. Based on six NK-cell DEGs identified by univariable, Lasso and multivariable Cox regression analyses, a prognostic risk model for serous ovarian cancer was developed in the TCGA cohort. This model was then validated in three external cohorts, and evaluated as an independent prognostic factor by multivariable Cox regression analysis together with clinical characteristics. With the investigation of the underlying mechanism, a relation between a higher risk score of this model and more immune activities in tumor microenvironment was revealed. Furthermore, a detailed inspection of infiltrated immunocytes indicated that not only quantity, but also the functional state of these immunocytes might affect prognostic risk. Additionally, the potential of this model to predict immunotherapeutic response was exhibited by evaluating the functional state of cytotoxic T lymphocytes. To conclude, this study introduced a novel prognostic risk model based on NK-cell DEGs, which might provide assistance for the personalized management of serous ovarian cancer patients.

Regulatory Role of Fatty Acid Metabolism-Related Long Noncoding RNA in Prostate Cancer: A Computational Biology Study Analysis

In elderly men, prostate cancer is a leading cause of death. Tumor cells require more energy to progress than normal cells, and this energy is mainly dependent on the large amount of ATP support generated by lipid metabolism. Therefore, in this study, we focused on long noncoding RNAs related to lipid metabolism in prostate cancer to discover the biological mechanisms of lipid metabolism regulation. The TCGA-PRAD cohort was used in this study for computational biology analysis. In lipid metabolism biological pathways, 1959 long noncoding RNAs were identified by Pearson correlation coefficient analysis of protein-coding genes, then univariate regression with P values fewer than 0.05. We further identified 784 lncRNAs that were lipid metabolism-related lncRNAs considered to have prognostic value for disease-free survival. Subsequently, we constructed two lncRNA expression patterns of lipid metabolism based on these lncRNAs by nonnegative matrix dimensionality reduction. These two expression patterns showed significant differences in disease-free survival curves for those diagnosed with prostate cancer. We found significant differences in mRNA surveillance pathway and mRNA processing between C1 and C2 groups based on the WGCNA method to explore the biological characteristics of these two expression patterns. Finally, we constructed a disease-free survival (PFS) model based on these lncRNAs. The results identified lncRNAs involved in lipid metabolism and revealed differences in their expression patterns. Additionally, the results offer candidate ideas and approaches concerning the precision treatment of prostate cancer by studying lipid metabolism by candidate long noncoding RNAs.

Ferroptosis heterogeneity in triple-negative breast cancer reveals an innovative immunotherapy combination strategy

Treatment of triple-negative breast cancer (TNBC) remains challenging. Deciphering the orchestration of metabolic pathways in regulating ferroptosis will provide new insights into TNBC therapeutic strategies. Here, we integrated the multiomics data of our large TNBC cohort (n = 465) to develop the ferroptosis atlas. We discovered that TNBCs had heterogeneous phenotypes in ferroptosis-related metabolites and metabolic pathways. The luminal androgen receptor (LAR) subtype of TNBC was characterized by the upregulation of oxidized phosphatidylethanolamines and glutathione metabolism (especially GPX4), which allowed the utilization of GPX4 inhibitors to induce ferroptosis. Furthermore, we verified that GPX4 inhibition not only induced tumor ferroptosis but also enhanced antitumor immunity. The combination of GPX4 inhibitors and anti-PD1 possessed greater therapeutic efficacy than monotherapy. Clinically, higher GPX4 expression correlated with lower cytolytic scores and worse prognosis in immunotherapy cohorts. Collectively, this study demonstrated the ferroptosis landscape of TNBC and revealed an innovative immunotherapy combination strategy for refractory LAR tumors.

Polo-like kinase 1 is related with malignant characteristics and inhibits macrophages infiltration in glioma

Background

Tumor immune microenvironment (TIM) plays a critical role in tumorigenesis and progression. Recently, therapies based on modulating TIM have made great breakthroughs in cancer treatment. Polo-like kinase 1 (PLK1) is a crucial regulatory factor of the cell cycle process and its dysregulations often cause various pathological processes including tumorigenesis. However, the detailed mechanisms surrounding the regulation of PLK1 on glioma immune microenvironment remain undefined.

Methods

Public databases and online datasets were used to extract data of PLK1 expression, clinical features, genetic alterations, and biological functions. The EdU, flow cytometry, and macrophage infiltration assays as well as xenograft animal experiments were performed to determine the relationship between PLK1 and glioma immune microenvironment in vivo and in vitro.

Results

PLK1 is always highly expressed in multiple cancers especially in glioma. Univariable and Multivariate proportional hazard Cox analysis showed that PLK1 was a prognostic biomarker for glioma. Simultaneously, highly expressed PLK1 is significantly related to prognosis, histological and genetic features in glioma by analyzing public databases. In addition, the enrichment analysis suggested that PLK1 might related to "immune response", "cell cycle", "DNA replication", and "mismatch repair" in glioma. Immune infiltration analysis demonstrated that highly expressed PLK1 inhibited M1 macrophages infiltration to glioblastoma immune microenvironment by Quantiseq and Xcell databases and negatively related to some chemokines and marker genes of M1 macrophages in glioblastoma. Subsequent experiments confirmed that PLK1 knockdown inhibited the proliferation of glioma cells but increased the M1 macrophages infiltration and polarization. Furthermore, in glioma xenograft mouse models, we showed that inhibiting PLK1 blocked tumor proliferation and increased the M1 macrophages infiltration. Finally, PLK1 methylation analysis and lncRNA-miRNA network revealed the potential mechanism of abnormal PLK1 expression in glioma.

Conclusions

PLK1 inhibits M1 macrophages infiltration into glioma immune microenvironment and is a potential biomarker for glioma.

Integrated molecular analyses of an interferon-γ based subtype with regard to outcome, immune characteristics, and immunotherapy in bladder cancer and experimental verification

This study attempted to explore the role of interferon-γ related genes (IRGs) in the prognosis and immunotherapy of bladder cancer (BC). Based on data downloaded from public databases, molecular subtypes with different IRG expression patterns were determined via nonnegative matrix factorization clustering. On the basis of IRGs, interferon-γ related gene signature (IRGS) was developed through Cox regression analyses. We identified that two molecular subgroups with different outcome and immune profiles. It was proved that IRGS possessed prediction efficiency for BC prognosis. Compared with low IRGS group, high IRGS group was related to less anti-cancer immune cells infiltration, less tumor mutation burden score, more cancer stem cell index, and less benefit from immunotherapy. Differential expression of six model genes (IRF5, LATS2, MTHFD2, VAMP8, HLA-G and PTPN6) was validated between paired tissues by RT-qPCR. This study presents a prognostic model, which could serve as an indicator for the benefit of BC immunotherapy.

A novel immune checkpoints-based signature to predict prognosis and response to immunotherapy in lung adenocarcinoma

Background

Except for B7-CD28 family members, more novel immune checkpoints are being discovered. They are closely associated with tumor immune microenvironment and regulate the function of many immune cells. Various cancer therapeutic studies targeting these novel immune checkpoints are currently in full swing. However, studies concerning novel immune checkpoints phenotypes and clinical significance in lung adenocarcinoma (LUAD) are still limited.

Methods

We enrolled 1883 LUAD cases from nine different cohorts. The samples from The Cancer Genome Atlas (TCGA) were used as a training set, whereas seven microarray data cohorts and an independent cohort with 102 qPCR data were used for validation. The immune profiles and potential mechanism of the system were also explored.

Results

After univariate Cox proportional hazards regression and stepwise multivariable Cox analysis, a novel immune checkpoints-based system (LTA, CD160, and CD40LG) were identified from the training set, which significantly stratified patients into high- and low-risk groups with different survivals. Furthermore, this system has been well validated in different clinical subgroups and multiple validation cohorts. It also acted as an independent prognostic factor for patients with LAUD in different cohorts. Further exploration suggested that high-risk patients exhibited distinctive immune cells infiltration and suffered an immunosuppressive state. Additionally, this system is closely linked to various classical immunotherapy biomarkers.

Conclusion

we constructed a novel immune checkpoints-based system for LUAD, which predicts prognosis and immunotherapeutic implications. We believe that these findings will not only aid in clinical management but will also shed some light on screening appropriate patients for immunotherapy.

Landscape of the intratumroal microenvironment in bladder cancer: Implications for prognosis and immunotherapy

Introduction

This study aims to present the landscape of the intratumoral microenvironment and by which establish a classification system that can be used to predict the prognosis of bladder cancer patients and their response to anti-PD-L1 immunotherapy.

Methods

The expression profiles of 1554 bladder cancer cases were downloaded from seven public datasets. Single-sample gene set enrichment analysis (ssGSEA), univariate Cox regression analysis, and meta-analysis were employed to establish the bladder cancer immune prognostic index (BCIPI). Extensive analyses were executed to investigate the association between BCIPI and overall survival, tumor-infiltrated immunocytes, immunotherapeutic response, mutation load, etc.

Results

The results obtained from seven independent cohorts and meta-analyses suggested that the BCIPI is an effective classification system for estimating bladder cancer patients' overall survival. Patients in the BCIPI-High subgroup revealed different immunophenotypic outcomes from those in the BCIPI-Low subgroup regarding tumor-infiltrated immunocytes and mutated genes. Subsequent analysis suggested that patients in the BCIPI-High subgroup were more sensitive to anti-PD-L1 immunotherapy than those in the BCIPI-Low subgroup.

Conclusions

The newly established BCIPI is a valuable tool for predicting overall survival outcomes and immunotherapeutic responses in patients with bladder cancer.

Novel T-cell signature based on cell pair algorithm predicts survival and immunotherapy response for patients with bladder urothelial carcinoma

Background

T-cell–T-cell interactions play important roles in the regulation of T-cells’ cytotoxic function, further impacting the anti-tumor efficacy of immunotherapy. There is a lack of comprehensive studies of T-cell types in bladder urothelial carcinoma (BLCA) and T-cell-related signatures for predicting prognosis and monitoring immunotherapy efficacy.

Methods

More than 3,400 BLCA patients were collected and used in the present study. The ssGSEA algorithm was applied to calculate the infiltration level of 19 T-cell types. A cell pair algorithm was applied to construct a T-cell-related prognostic index (TCRPI). Survival analysis was performed to measure the survival difference across TCRPI-risk groups. Spearman’s correlation analysis was used for relevance assessment. The Wilcox test was used to measure the expression level difference.

Results

Nineteen T-cell types were collected; 171 T-cell pairs (TCPs) were established, of which 26 were picked out by the least absolute shrinkage and selection operator (LASSO) analysis. Based on these TCPs, the TCRPI was constructed and validated to play crucial roles in survival stratification and the dynamic monitoring of immunotherapy effects. We also explored several candidate drugs targeting TCRPI. A composite TCRPI and clinical prognostic index (CTCPI) was then constructed, which achieved a more accurate estimation of BLCA’s survival and was therefore a better choice for prognosis prediction in BLCA.

Conclusions

All in all, we constructed and validated TCRPI based on cell pair algorithms in this study, which might put forward some new insights to increase the survival estimation and clinical response to immune therapy for individual BLCA patients and contribute to the personalized precision immunotherapy strategy of BLCA.

Association of body mass index and inflammatory dietary pattern with breast cancer pathologic and genomic immunophenotype in the nurses' health study

BACKGROUND

Breast tumor immune infiltration is clearly associated with improved treatment response and outcomes in breast cancer. However, modifiable patient factors associated with breast cancer immune infiltrates are poorly understood. The Nurses' Health Study (NHS) offers a unique cohort to study immune gene expression in tumor and adjacent normal breast tissue, immune cell-specific immunohistochemistry (IHC), and patient exposures. We evaluated the association of body mass index (BMI) change since age 18, physical activity, and the empirical dietary inflammatory pattern (EDIP) score, all implicated in systemic inflammation, with immune cell-specific expression scores.

METHODS

This population-based, prospective observational study evaluated 882 NHS and NHSII participants diagnosed with invasive breast cancer with detailed exposure and gene expression data. Of these, 262 women (training cohort) had breast tumor IHC for four classic immune cell markers (CD8, CD4, CD20, and CD163). Four immune cell-specific scores were derived via lasso regression using 105 published immune expression signatures' association with IHC. In the remaining 620 patient evaluation cohort, we evaluated association of each immune cell-specific score as outcomes, with BMI change since age 18, physical activity, and EDIP score as predictors, using multivariable-adjusted linear regression.

RESULTS

Among women with paired expression/IHC data from breast tumor tissue, we identified robust correlation between novel immune cell-specific expression scores and IHC. BMI change since age 18 was positively associated with CD4+ (β = 0.16; p = 0.009), and CD163 novel immune scores (β = 0.14; p = 0.04) in multivariable analyses. In other words, for each 10 unit (kg/m2) increase in BMI, the percentage of cells positive for CD4 and CD163 increased 1.6% and 1.4%, respectively. Neither physical activity nor EDIP was significantly associated with any immune cell-specific expression score in multivariable analyses.

CONCLUSIONS

BMI change since age 18 was positively associated with novel CD4+ and CD163+ cell scores in breast cancer, supporting further study of the effect of modifiable factors like weight gain on the immune microenvironment.

Multi-omics integration reveals a six-malignant cell maker gene signature for predicting prognosis in high-risk neuroblastoma

Background

Neuroblastoma is the most common extracranial solid tumor of childhood, arising from the sympathetic nervous system. High-risk neuroblastoma (HRNB) remains a major therapeutic challenge with low survival rates despite the intensification of therapy. This study aimed to develop a malignant-cell marker gene signature (MMGS) that might serve as a prognostic indicator in HRNB patients.

Methods

Multi-omics datasets, including mRNA expression (single-cell and bulk), DNA methylation, and clinical information of HRNB patients, were used to identify prognostic malignant cell marker genes. MMGS was established by univariate Cox analysis, LASSO, and stepwise multivariable Cox regression analysis. Kaplan–Meier (KM) curve and time-dependent receiver operating characteristic curve (tROC) were used to evaluate the prognostic value and performance of MMGS, respectively. MMGS further verified its reliability and accuracy in the independent validation set. Finally, the characteristics of functional enrichment, tumor immune features, and inflammatory activity between different MMGS risk groups were also investigated.

Results

We constructed a prognostic model consisting of six malignant cell maker genes (MAPT, C1QTNF4, MEG3, NPW, RAMP1, and CDT1), which stratified patients into ultra-high-risk (UHR) and common-high-risk (CHR) group. Patients in the UHR group had significantly worse overall survival (OS) than those in the CHR group. MMGS was verified as an independent predictor for the OS of HRNB patients. The area under the curve (AUC) values of MMGS at 1-, 3-, and 5-year were 0.78, 0.693, and 0.618, respectively. Notably, functional enrichment, tumor immune features, and inflammatory activity analyses preliminarily indicated that the poor prognosis in the UHR group might result from the dysregulation of the metabolic process and immunosuppressive microenvironment.

Conclusion

This study established a novel six-malignant cell maker gene prognostic model that can be used to predict the prognosis of HRNB patients, which may provide new insight for the treatment and personalized monitoring of HRNB patients.

scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes

A major challenge in single-cell biology is identifying cell-type-specific gene functions, which may substantially improve precision medicine. Differential expression analysis of genes is a popular, yet insufficient approach, and complementary methods that associate function with cell type are required. Here, we describe scHumanNet (https://github.com/netbiolab/scHumanNet), a single-cell network analysis platform for resolving cellular heterogeneity across gene functions in humans. Based on cell-type-specific gene networks (CGNs) constructed under the guidance of the HumanNet reference interactome, scHumanNet displayed higher functional relevance to the cellular context than CGNs built by other methods on single-cell transcriptome data. Cellular deconvolution of gene signatures based on network compactness across cell types revealed breast cancer prognostic markers associated with T cells. scHumanNet could also prioritize genes associated with particular cell types using CGN centrality and identified the differential hubness of CGNs between disease and healthy conditions. We demonstrated the usefulness of scHumanNet by uncovering T-cell-specific functional effects of GITR, a prognostic gene for breast cancer, and functional defects in autism spectrum disorder genes specific for inhibitory neurons. These results suggest that scHumanNet will advance our understanding of cell-type specificity across human disease genes.

Comprehensive characterization of TNFSF14/LIGHT with implications in prognosis and immunotherapy of human gliomas

Glioblastoma multiforme (GBM) is a common central neural system malignant tumor among adults. Alongside its microscopic spread, immunosuppression in the tumor microenvironment also induces its refractoriness, which makes immunotherapy for GBM particularly important. Unfortunately, traditional immune checkpoint inhibitors (ICIs) often show limited therapeutic effects in GBM clinical trials, and new therapeutic strategies or targets are urgently needed. TNFSF14/LIGHT is a novel immune checkpoint molecule that plays essential roles in both innate and acquired immunity. Despite recent advances in our understanding of the function of TNFSF14/LIGHT in a variety of cancer types, the clinical and immunological importance of TNFSF14/LIGHT in human gliomas has not been fully explained. Here, we employed a comprehensive in silico analysis with publicly available data to analyze the molecular and immune characteristics of TNFSF14/LIGHT to explore its feasibility as an immunotherapy target. Totally, 2215 glioma cases were enrolled in the current study. Immunohistochemistry staining based on patient tissues (n = 34) was performed for the validation. TNFSF14/LIGHT was expressed higher in higher-WHO-grade gliomas and mesenchymal subtypes, and it was sensitive as a prognostic marker in GBM and low-grade glioma (LGG). A nomogram prognostic model was established based on TNFSF14/LIGHT expression together with other risk factors. Additionally, Gene Ontology and pathway analysis revealed that TNFSF14/LIGHT participated in T-cell activities and inflammatory processes. Moreover, analysis based on the structure and interactions of TNFSF14/LIGHT revealed its mutation sites in tumors as well as crucial interacting proteins. Analysis of IMvigor210 indicated the role of TNFSF14/LIGHT in immunotherapy. Altogether, our results reveal an underlying role of TNFSF14/LIGHT as an immunotherapy target in GBM.

Characterization of sialylation-related long noncoding RNAs to develop a novel signature for predicting prognosis, immune landscape, and chemotherapy response in colorectal cancer

Aberrant sialylation plays a key biological role in tumorigenesis and metastasis, including tumor cell survival and invasion, immune evasion, angiogenesis, and resistance to therapy. It has been proposed as a possible cancer biomarker and a potential therapeutic target of tumors. Nevertheless, the prognostic significance and biological features of sialylation-related long noncoding RNAs (lncRNAs) in colorectal cancer (CRC) remain unclear. This study aimed to develop a novel sialylation-related lncRNA signature to accurately evaluate the prognosis of patients with CRC and explore the potential molecular mechanisms of the sialylation-related lncRNAs. Here, we identified sialylation-related lncRNAs using the Pearson correlation analysis on The Cancer Genome Atlas (TCGA) dataset. Univariate and stepwise multivariable Cox analysis were used to establish a signature based on seven sialylation-related lncRNAs in the TCGA dataset, and the risk model was validated in the Gene Expression Omnibus dataset. Kaplan-Meier curve analysis revealed that CRC patients in the low-risk subgroup had a better survival outcome than those in the high-risk subgroup in the training set, testing set, and overall set. Multivariate analysis demonstrated that the sialylation-related lncRNA signature was an independent prognostic factor for overall survival, progression-free survival, and disease-specific survival prediction. The sialylation lncRNA signature-based nomogram exhibited a robust prognostic performance. Furthermore, enrichment analysis showed that cancer hallmarks and oncogenic signaling were enriched in the high-risk group, while inflammatory responses and immune-related pathways were enriched in the low-risk group. The comprehensive analysis suggested that low-risk patients had higher activity of immune response pathways, greater immune cell infiltration, and higher expression of immune stimulators. In addition, we determined the sialylation level in normal colonic cells and CRC cell lines by flow cytometry combined with immunofluorescence, and verified the expression levels of seven lncRNAs using real-time quantitative polymerase chain reaction. Finally, combined drug sensitivity analysis using the Genomics of Drug Sensitivity in Cancer, Cancer Therapeutics Response Portal, and Profiling Relative Inhibition Simultaneously in Mixtures indicated that the sialylation-related lncRNA signature could serve as a potential predictor for chemosensitivity. Collectively, this is the first sialylation lncRNA-based signature for predicting the prognosis, immune landscape, and chemotherapeutic response in CRC, and may provide vital guidance to facilitate risk stratification and optimize individualized therapy for CRC patients.

Transcriptome profile and clinical characterization of ICOS expression in gliomas

Inducible co-stimulator (ICOS), an immune costimulatory molecule, has been found to play an essential role across various malignancies. This study investigated the transcriptome profile and clinical characterization of ICOS in gliomas. Clinical information and transcriptome data of 301 glioma samples were downloaded from the Chinese Glioma Genome Atlas (CGGA) dataset for analysis (CGGA301 cohort). Furthermore, the results were validated in 697 samples with RNAseq data from the TCGA glioma dataset and 325 gliomas with RNAseq data from the CGGA325 dataset. Immunohistochemistry was performed to evaluate ICOS protein expression across different WHO grades in a tissue microarray (TMA). In addition, single-cell sequencing data from CGGA and GSE 163108 datasets were used to analyze the ICOS expression across different cell types. Statistical analyses and figure production were performed with R-language. We found that ICOS was significantly upregulated in higher-grade, IDH wild type, and mesenchymal subtype of gliomas. Functional enrichment analyses revealed that ICOS was mainly involved in glioma-related immune response. Moreover, ICOS showed a robust correlation with other immune checkpoints, including the PD1/PD-L1/PD-L2 pathway, CTLA4, ICOSL (ICOS ligand), and IDO1. Subsequent Tumor Immune Dysfunction and Exclusion (TIDE) analysis revealed that GBM patients with higher ICOS expression seemed to be more sensitive to ICB therapy. Furthermore, based on seven clusters of metagenes, GSVA identified that ICOS was tightly associated with HCK, LCK, MHC-I, MHC-II, STAT1, and interferon, especially with LCK, suggesting a strong correlation between ICOS and T-cell activity in gliomas. In cell lineage analysis, Higher-ICOS gliomas tended to recruit dendritic cells, monocytes, and macrophages into the tumor microenvironment. Single-cell sequencing analysis indicated that ICOS was highly expressed by regulatory T cells (Tregs), especially in mature Tregs. Finally, patients with higher ICOS had shortened survival. ICOS was an independent prognosticator for glioma patients. In conclusion, higher ICOS is correlated with more malignancy of gliomas and is significantly associated with Treg activity among glioma-related immune responses. Moreover, ICOS could contribute as an independent prognostic factor for gliomas. Our study highlights the role of ICOS in glioma and may facilitate therapeutic strategies targeting ICOS for glioma.

Constructing a Novel Prognostic Signature Based on TGF-β Signaling for Personalized Treatment in Pancreatic Adenocarcinoma

Background

Pancreatic adenocarcinoma (PAAD) shows significantly high mortality. Transforming growth factor-beta (TGF-β) signaling plays an important role in tumorigenesis and development. A prognostic model was conducted using transforming growth factor-beta (TGF-β) signaling for predicting PAAD prognosis and guiding personalized therapies.

Methods

Datasets were grouped into test and training sets. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) were applied and introduced for identifying prognostic genes associated with TGF-β. Risk score of each sample was calculated by the prognostic model. The difference in survival, clinical information, mutations, pathways, and chemotherapy and immunotherapy sensitivities between high-risk and low-risk groups was analyzed.

Results

Based on TGF-β signaling, this work built a 7-gene prognostic model showing robustness in sample classification into low-risk and high-risk groups with differential prognoses. Oncogenic pathways like glycolysis, Notch signaling, and hypoxia were noticeably enriched in the group with high risk. Interferon and STAT1 were positively associated with risk score. Importantly, the low-risk group may develop a more favorable response to both chemotherapy and immunotherapy. The current work highlighted the significant function of TGF-β signaling in PAAD development and described the potential cross-links with other oncogenic pathways.

Conclusion

Notably, the prognostic signature can act as a predictor of prognosis, but as a biomarker for optimizing personalized therapies in clinical practice.

Prostate Cancer Tumor Stroma: Responsibility in Tumor Biology, Diagnosis and Treatment

Simple Summary

The crosstalk between prostate stroma and its epithelium is essential to tissue homeostasis. Likewise, reciprocal signaling between tumor cells and the stromal compartment is required in tumor progression to facilitate or stimulate key processes such as cell proliferation and invasion. The aim of the present work was to review the current state of knowledge on the significance of tumor stroma in the genesis, progression and therapeutic response of prostate carcinoma. Additionally, we addressed the future therapeutic opportunities.

Abstract

Prostate cancer (PCa) is a common cancer among males globally, and its occurrence is growing worldwide. Clinical decisions about the combination of therapies are becoming highly relevant. However, this is a heterogeneous disease, ranging widely in prognosis. Therefore, new approaches are needed based on tumor biology, from which further prognostic assessments can be established and complementary strategies can be identified. The knowledge of both the morphological structure and functional biology of the PCa stroma compartment can provide new diagnostic, prognostic or therapeutic possibilities. In the present review, we analyzed the aspects related to the tumor stromal component (both acellular and cellular) in PCa, their influence on tumor behavior and the therapeutic response and their consideration as a new therapeutic target.

S100A10 Is a New Prognostic Biomarker Related to the Malignant Molecular Features and Immunosuppression Process of Adult Gliomas

OBJECTIVE

Previous studies have demonstrated the role of S100A10 in the progression of several tumors; however, few studies have investigated its immunological characteristics in adult gliomas. In this study, we systematically explored its biological features and clinical significance in adult gliomas.

METHODS

Altogether, 325 glioma cases from the Chinese Glioma Genome Atlas and 699 glioma cases from The Cancer Genome Atlas were included as the training and validation cohorts. R software was used for data analysis and mapping using the RNA sequencing data from these cases. One-way analysis of variance and Student's t-test were used to assess the differences between the groups. Differences were considered statistically significant at P < 0.05.

RESULTS

We found that S100A10 was remarkably highly expressed in high-grade glioma, isocitrate dehydrogenase wild type, 1p19q noncodeletion type, O⁶-methylguanine-DNA methyltransferase promoter unmethylation type, and mesenchymal-like molecular subtype. S100A10 specifically and sensitively indicates the mesenchymal-like molecular subtype. Upregulated S100A10 levels were independently correlated with poor survival. S100A10-related biological processes in gliomas mainly concentrate on immunoreaction and inflammatory response. We then proved that S100A10 was positively related to most inflammatory metagenes, except IgG, including HCK, LCK, MHC II, STAT1, and interferon. More importantly, the levels of glioma-infiltrating immune cells were positively associated with the expression of S100A10, especially in tumor-related macrophages, regulatory T cells, and myeloid-derived suppressor cells.

CONCLUSIONS

S100A10 is closely related to malignant pathological subtypes, worse prognosis, and immunosuppressive immune cell infiltration in adult gliomas, making it a promising biomarker and potential target in the diagnosis, treatment, and prognostic assessment of gliomas.

Prognostic significance of programmed death-1 and programmed death ligand-1 proteins in breast cancer

In numerous studies related to tumor prognosis, programmed death-ligand 1 (PD-L1) has been identified as a biomarker. This work aimed to determine the prognostic importance of PD-L1 in breast cancer. We searched electronic databases such as PubMed, Google scholar, home pages of publishing groups, medical, clinical, and pharmaceutical sciences journals, as well as other relevant sources to discover the importance of PD-1 and PD-L1 expression in breast cancer therapies and also recurrence. The keywords used in this search were autoimmunity, programmed cell death, PD-L1 or PD-1, and breast cancer. Our inclusion criteria included studies showing the synergy between the expression of PD-L1 and PD-1 in primary breast cancers as prognostic markers and this research was limited to humans only. We included review articles, original research, letters to the editor, case reports, and short communications in our study, published in English. We focused our work on PD-L1 mRNA expression in breast cancer cell lines. PD-L1 expression has been decisively demonstrated to be a high-risk factor for breast cancer with a bad prognosis.

Molecular and clinical features of a potential immunotherapy target ELK3 in glioma

Glioma represents the most prevalent malignant primary brain cancer, and its treatment remains a tremendous challenge. Novel and efficient molecular targets are therefore required for improving diagnosis, survival prediction, and treatment outcomes. Additionally, some studies have shown that immunity is highly associated with glioma progression. Our study aimed to investigate the clinicopathological features, prognostic significance, and immunotherapeutic targetability of ELK3, a member of the erythroblast transformation-specific transcription factor family, in glioma using bioinformatics analyses. ELK3 transcript levels in glioma tissues were evaluated using the Gene Expression Omnibus and The Cancer Genome Atlas databases. Clinical and transcriptomic data of The Cancer Genome Atlas glioma patients were analyzed to identify the molecular and clinical characterizations of ELK3. The prognostic significance of ELK3 was assessed using Cox regression and Kaplan-Meier analysis. The biological pathways related to ELK3 expression were identified by gene set enrichment analysis. The relationships between ELK3 and inflammatory responses, immune cell infiltration, and immune checkpoints were explored using canonical correlation analysis and gene set variation analysis. ELK3 was upregulated in gliomas, and its high expression was correlated with advanced clinicopathologic features and unfavorable prognosis. Gene set enrichment analysis revealed that several immune-related pathways were tightly linked to high ELK3 expression. gene set variation analysis and correlograms demonstrated that ELK3 was robustly associated with inflammatory and immune responses. Correlation analyses indicated that ELK3 was positively associated with infiltrating immune cells and synergistic with several immune checkpoints. ELK3 may serve as a novel marker of poor prognosis and a potential immunotherapeutic target in glioma.

Can granulysin provide prognostic value in primary breast cancer?

BACKGROUND

Granulysin (GNLY) is a cytolytic and proinflammatory molecule which also acts as an immune alarmin. The multifunctional nature of this molecule has made it challenging to define its full potential as a biomarker in breast cancer.

AIM

To evaluate the prognostic value of intratumoral GNLY in primary breast cancer patients and its association with established clinicopathological parameters.

PATIENTS AND METHODS

The study included 69 node-negative breast cancer patients with known clinicopathological parameters, all of whom had not received any prior hormonal or chemotherapeutic systemic therapy that would interfere with the course of disease. The median follow-up period was 144 months. Steroid hormone receptor status was determined by ligand-binding assay and HER2 status by chromogenic in situ hybridisation (CISH). Intratumoral GNLY mRNA levels were determined by RT-qPCR. Prognostic performance was evaluated by the receiver operating characteristic (ROC), Cox proportional hazards regression and Kaplan-Meier analysis. Classification of patients into GNLY^low and GNLY^high subgroups was performed by the use of the outcome-oriented cut-off point categorisation approach.

RESULTS

There was a significant difference between GNLY values of patients without any recurrences and those with local or distant recurrences (Mann-Whitney test, p = 0.05 and p = 0.02, respectively). None of the tested parameters showed prognostic significance for local and distant recurrences when combined. When distant metastases and local recurrences were separated as events, the best prognostic performance was observed for GNLY as compared with any clinicopathological parameter (AUC=0.24 and p = 0.04 for local events; AUC=0.71 and p = 0.03 for distant events). Local recurrence incidence was 0% for the GNLY^high subgroup and 19% for the GNLY^low subgroup; however distant recurrence incidence was 24% for the GNLY^high subgroup but only 3% for the GNLY^low subgroup (Kaplan-Meier analysis). A significant positive correlation was found between intratumoral ER and GNLY levels, and a significant negative correlation between tumour grade and GNLY levels.

CONCLUSION

High levels of granulysin prognosticate low risk of local recurrence but a high risk of distant metastasis in primary, untreated, breast cancer patients.

A B7-CD28 Family-Based Signature Demonstrates Significantly Different Prognosis and Immunological Characteristics in Diffuse Gliomas

The B7-CD28 gene family plays a crucial role in modulating immune functions and has served as potential targets for immunotherapeutic strategies. Therefore, we systematically analyzed B7-CD28 family gene expression profiles and constructed a B7-CD28 family-based prognostic signature to predict survival and immune host status in diffuse gliomas. The TCGA dataset was used as a training cohort, and three CGGA datasets (mRNAseq_325, mRNAseq_693 and mRNA-array) were employed as validation cohorts to intensify the findings that we have revealed in TCGA dataset. Ultimately, we developed a B7-CD28 family-based signature that consisted of CD276, CD274, PDCD1LG2 and CD80 using LASSO Cox analysis. This gene signature was validated to have significant prognostic value, and could be used as a biomarker to distinguish pathological grade and IDH mutation status in diffuse glioma. Additionally, we found that the gene signature was significantly related to intensity of immune response and immune cell population, as well as several other important immune checkpoint genes, holding a great potential to be a predictive immune marker for immunotherapy and tumor microenvironment. Finally, a B7-CD28 family-based nomogram was established to predict patient life expectancy contributing to facilitate personalizing therapy for tumor sufferers. In summary, this is the first mathematical model based on this gene family with the aim of providing novel insights into immunotherapy for diffuse glioma.

CISH Expression Is Associated with Metastasis-Free Interval in Triple-Negative Breast Cancer and Refines the Prognostic Value of PDL1 Expression

Simple Summary

CISH is a member of the suppressors of cytokine signaling (SOCS) family of proteins and an important negative regulator of T cells and NK cells signaling and function. In this study, analyzing 1936 triple-negative breast cancer (TNBC) clinical samples, we highlighted correlation between CISH expression and tumor features. We demonstrated that high CISH upregulation was associated with better metastasis-free interval, especially when PDL1 was also upregulated. Moreover, we showed that the two-gene model (CISH and PDL1) provided more prognostic information than each gene alone and maintained its prognostic value in multivariate analysis. Such prognostic synergy between CISH and PDL1 expressions might reinforce the therapeutic relevance of co-targeting TNBC by a combination of CISH inhibition with an immune checkpoint inhibitor blocking the PD1/PDL1 axis.

Abstract

Strategies are being explored to increase the efficiency of immune checkpoint inhibitors (ICIs) targeting PD1/PDL1 in triple-negative breast cancer (TNBC), including combination with therapies inhibiting intracellular immune checkpoints such as CISH (Cytokine-induced SH2 protein). Correlation between CISH expression and TNBC features is unknown. We retrospectively analyzed CISH expression in 1936 clinical TNBC samples and searched for correlations with clinical variables, including metastasis-free interval (MFI). Among TNBCs, 44% were identified as “CISH-up” and 56% “CISH-down”. High expression was associated with pathological axillary lymph node involvement, more adjuvant chemotherapy, and Lehmann’s immunomodulatory and luminal AR subtypes. The “CISH-up” class showed longer 5-year MFI (72%) than the “CISH-down” class (60%; p = 2.8 × 10⁻²). CISH upregulation was associated with activation of IFNα and IFNγ pathways, antitumor cytotoxic immune response, and signatures predictive for ICI response. When CISH and PDL1 were upregulated together, the 5-year MFI was 81% versus 52% when not upregulated (p = 6.21 × 10⁻⁶). The two-gene model provided more prognostic information than each gene alone and maintained its prognostic value in multivariate analysis. CISH expression is associated with longer MFI in TNBC and refines the prognostic value of PDL1 expression. Such observation might reinforce the therapeutic relevance of combining CISH inhibition with an anti-PD1/PDL1 ICI.

Identification and Validation of a Novel Signature Based on NK Cell Marker Genes to Predict Prognosis and Immunotherapy Response in Lung Adenocarcinoma by Integrated Analysis of Single-Cell and Bulk RNA-Sequencing

Natural killer (NK) cells, the effectors of the innate immune system, have a remarkable influence on cancer prognosis and immunotherapy. In this study, a total of 1,816 samples from nine independent cohorts in public datasets were enrolled. We first conducted a comprehensive analysis of single-cell RNA-sequencing data of lung adenocarcinoma (LUAD) from the Gene Expression Omnibus (GEO) database and determined 189 NK cell marker genes. Subsequently, we developed a seven-gene prognostic signature based on NK cell marker genes in the TCGA LUAD cohort, which stratified patients into high-risk and low-risk groups. The predictive power of the signature was well verified in different clinical subgroups and GEO cohorts. With a multivariate analysis, the signature was identified as an independent prognostic factor. Low-risk patients had higher immune cell infiltration states, especially CD8⁺ T cells and follicular helper T cells. There existed a negative association between inflammatory activities and risk score, and the richness and diversity of the T-cell receptor (TCR) repertoire was higher in the low-risk groups. Importantly, analysis of an independent immunotherapy cohort (IMvigor210) revealed that low-risk patients had better immunotherapy responses and prognosis than high-risk patients. Collectively, our study developed a novel signature based on NK cell marker genes, which had a potent capability to predict the prognosis and immunotherapy response of LUAD patients.

Molecular and Clinical Characterization of CD80 Expression via Large-Scale Analysis in Breast Cancer

Cancer immunotherapy is emerging as a novel promising therapy option for cancer patients. Despite the critical role of CD80 in the regulation of immune responses, the expression and biological functions of CD80 in breast cancer remain unknown. In this study, we aimed to investigate the role of CD80 both clinically and molecularly in breast cancer at a transcriptome level. Herein, we first analyzed the transcriptome profile and relevant clinical information derived from a total of 1090 breast cancer patients recorded in The Cancer Genome Atlas database and then validated this in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database (n = 1904). We revealed the associations of CD80 and the main molecular and clinical characteristics of breast cancer. The gene ontology analysis and Gene Set Variation Analysis of the CD80-related genes revealed that CD80 was closely correlated with immune responses and inflammatory activities in breast cancer. Moreover, the CD80 expression showed a remarkable positive correlation with several infiltrated immune cell populations. In summary, the CD80 expression was closely correlated with the malignancy of breast cancer, and our findings suggest that CD80 might be a promising target for immunotherapeutic strategies. To the best of our knowledge, this is the first integrative study characterizing the role of the CD80 expression in breast cancer via large-scale analyses.

A risk score model based on TGF-β pathway-related genes predicts survival, tumor microenvironment and immunotherapy for liver hepatocellular carcinoma

BACKGROUND

Transforming growth factor-beta (TGF-β) signal is an important pathway involved in all stages of liver hepatocellular carcinoma (LIHC) initiation and progression. Therefore, targeting TGF- β pathway may be a potential therapeutic strategy for LIHC. Prediction of patients' tumor cells response requires effective biomarkers.

METHODS

From 54 TGF-β-related genes, this research determined the genes showing the greatest relation to LIHC prognosis, and developed a risk score model with 8 TGF-β-related genes. The model divided LIHC patients from different datasets and platforms into low- and high-risk groups. Multivariate Cox regression analysis confirmed that the model was an independent prognostic factor for LIHC. The differences in genetic mutation, immune cell infiltration, biological pathway, response to immunotherapy or chemotherapy, and tumor microenvironment in LIHC samples showing different risks were analyzed.

RESULTS

Compared with low-risk group, in the training set and test set, high-risk group showed shorter survival, lower stromal score and higher M0 macrophages scores, regulatory T cells (Tregs), helper follicular T cells. Moreover, high-risk samples showed higher sensitivity to cisplatin, imatinib, sorafenib and salubrinal and pyrimethamine. High-risk group demonstrated a significantly higher Tumor Immune Dysfunction and Exclusion (TIDE) score, but would significantly benefit less from taking immunotherapy and was less likely to respond to immune checkpoint inhibitors.

CONCLUSIONS

In general, this work provided a risk scoring model based on 8 TGF-β pathway-related genes, which might be a new potential tool for predicting LIHC.

Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies

Using pre-treatment gene expression, protein/phosphoprotein, and clinical data from the I-SPY2 neoadjuvant platform trial (NCT01042379), we create alternative breast cancer subtypes incorporating tumor biology beyond clinical hormone receptor (HR) and human epidermal growth factor receptor-2 (HER2) status to better predict drug responses. We assess the predictive performance of mechanism-of-action biomarkers from ∼990 patients treated with 10 regimens targeting diverse biology. We explore >11 subtyping schemas and identify treatment-subtype pairs maximizing the pathologic complete response (pCR) rate over the population. The best performing schemas incorporate Immune, DNA repair, and HER2/Luminal phenotypes. Subsequent treatment allocation increases the overall pCR rate to 63% from 51% using HR/HER2-based treatment selection. pCR gains from reclassification and improved patient selection are highest in HR+ subsets (>15%). As new treatments are introduced, the subtyping schema determines the minimum response needed to show efficacy. This data platform provides an unprecedented resource and supports the usage of response-based subtypes to guide future treatment prioritization.

Overall Survival Signature of 5-Methylcytosine Regulators Related Long Non-Coding RNA in Hepatocellular Carcinoma

Purpose

Studies reported that 5-methylcytosine (m5C) RNA transferase alters tumor progression; however, studies of m5C-related lncRNA remain lacking. This article intends to study the lncRNA modified by m5C RNA transferase in hepatocellular carcinoma using a combination of computational biology and basic experiments.

Method

We identified 13 m5C RNA transferase-related genes and selected long non-coding RNAs with a Pearson correlation coefficient greater than 0.4. Univariate Cox regression analysis was used to screen m5C RNA transferase lncRNA related to survival phenotype. We divided TCGA-LIHC into two types of m5C RNA using non-negative matrix decomposition. According to WGCNA, the co-expression models of two lncRNA regulation modes were constructed to analyze the characteristic biological processes of the two m5C RNA transferase-related lncRNA gene models. Then, a predictive model of m5C RNA transferase lncRNA was using LASSO regression. Finally, we used cell experiments, transwell experiments, and clone formation experiments to test the relationship between SNHG4 and tumor cell proliferation in Hep-G2 and Hep-3b cells line.

Results

We identified 436 m5C RNA transferase-related lncRNAs. Using univariate Cox regression analysis, 43 prognostic-related lncRNAs were determined according to P < 0.001. We divided TCGA-LIHC into two regulation modes of m5C RNA transferase using non-negative matrix factorization. The two regulation modes showed significant differences in overall and disease-free survival. We used LASSO to construct m5c-related lncRNA prognostic signature. Thus, a predictive m5C-lncRNA model was established using four lncRNAs: AC026412.3, AC010969.2, SNHG4, and AP003392.5. The score calculated by the m5C-lncRNA model significantly correlated with the overall survival of hepatocellular carcinoma. The receiver operating characteristic curve and decision curve analysis verified the accuracy of the predictive model. We observed a more robust immune response in the high-risk score group. The transwell experiments and clone formation experiments suggested that m5C RNA transferase-related lncRNA SNHG4 promotes the proliferation and migration of Hep-G2 and Hep-3b cells line.

Conclusion

Two lncRNA expression patterns regulated by m5C RNA transferase were identified. The difference between the two expression patterns and the survival phenotype in the biological process was pointed out. A 5-methylcytosine RNA methyltransferases-related lncRNA overall survival signature was constructed. These results provide some understanding of the influence of m5C transferase on hepatocellular carcinoma. The prediction model of m5C transferase lncRNA has potential clinical value in managing hepatocellular carcinoma.