Pan-cancer proteogenomics characterization of tumor immunity

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Advances in immunotyping of colorectal cancer

Immunotherapy has transformed treatment for various types of malignancy. However, the benefit of immunotherapy is limited to a minority of patients with mismatch-repair-deficient (dMMR) and microsatellite instability-high (MSI-H) (dMMR-MSI-H) colorectal cancer (CRC). Understanding the complexity and heterogeneity of the tumor immune microenvironment (TIME) and identifying immune-related CRC subtypes will improve antitumor immunotherapy. Here, we review the current status of immunotherapy and typing schemes for CRC. Immune subtypes have been identified based on TIME and prognostic gene signatures that can both partially explain clinical responses to immune checkpoint inhibitors and the prognosis of patients with CRC. Identifying immune subtypes will improve understanding of complex CRC tumor heterogeneity and refine current immunotherapeutic strategies.

Identification of prognostic immune subtypes of lung squamous cell carcinoma by unsupervised consistent clustering

We performed UCC on the expression data of lung squamous cell carcinoma tumor samples to identify the classification of lung squamous cell carcinoma (LUSC) tumor samples, and calculated the levels of different classified immune cells by single-sample gene enrichment analysis (ssGSEA) to obtain a set of immune-related subtype gene tags, which can be used for subtype classification of lung squamous cell carcinoma. TCGA-LUSC and GSE30219 data of lung squamous cell carcinoma were obtained from TCGA and GEO databases. Prognostic-associated subtypes were identified by unsupervised consensus clustering (UCC). Using ssGSEA analysis to calculate the level of immune cells of different subtypes, obtain the connection between subtypes and immunity, identify the gene signatures recognized by subtypes, and verify this group of gene signatures through GSE30219. We effectively identified 2 subtypes that were significantly associated with prognostic survival by UCC, and calculated according to ssGSEA, the 2 subtypes were significantly different at the level of immune cells, followed by introducing a This weighted thinking computes a set of gene signatures that are significantly associated with subtype 1. During validation, this set of gene signatures could efficiently and robustly identify distinct prognostic immune subtypes, demonstrated the validity of this set of gene signatures, as well as 2 subtypes of lung squamous cell carcinoma. We used lung squamous cell carcinoma data from public databases and identified 2 prognostic immunosubtypes of lung squamous cell carcinoma and a set of gene tags that can be used to classify immune subtypes of lung squamous cell carcinoma, which may provide effective evidence for accurate clinical treatment of lung squamous cell carcinoma.

Molecular Evolutionary Landscape of the Immune Microenvironment of Head and Neck Cancer

Head and neck cancer is a highly heterogeneous malignant tumor. Numerous studies have shown that the immune microenvironment of head and neck cancer has a significant impact on its occurrence and development, as well as its prognosis. However, there have been fewer studies related to the accurate immunophenotyping of head and neck cancer. In this study, we used gene expression profile information and clinical information from the TCGA-HNSC cohort (502 samples) and the GSE655858 cohort (270 samples) to identify and independently validate three immune subtypes (Cluster1-Cluster3) with different immune-related molecular profiles and clinical outcomes. Cluster2, which is mainly dominated by B-lymphocyte infiltration, was found to have the best prognosis. In addition, a support vector machine (SVM)-based classifier was constructed, which could accurately classify HNSC based on 19 genes. Furthermore, the results of the prognostic analysis showed activation of antibody-secreting B-lymphocyte function, which showed a good prognostic effect in all three immune subtypes of HNSC. Finally, the immune evolutionary landscape of HNSC was constructed in an attempt to explain the evolutionary pattern of the immune subtypes of HNSC. In summary, we provide a conceptual framework for understanding the tumor immune microenvironment in HNSC and demonstrate the importance of immune infiltration of B lymphocytes in HNSC. Further research is needed to assess the importance of these immunophenotypes in combination drug therapy and to provide a basis for screening appropriate patients for immunotherapy.

Inhibition of FGFR3 upregulates MHC-I and PD-L1 via TLR3/NF-kB pathway in muscle-invasive bladder cancer

BACKGROUND

Improving the potency of immune response is paramount among issues concerning immunotherapy of muscle-invasive bladder cancer (MIBC).

METHODS

On the basis of immune subtypes, we investigated possible molecular mechanisms involved in tumor immune escape in MIBC. According to the 312 immune-related genes, three MIBC immune subtypes were clustered.

RESULTS

Cluster 2 subtype is characterized by FGFR3 mutations and has a better clinical prognosis. However, the expression levels of MHC-I and immune checkpoints genes were the lowest, indicating that this subtype is subject to immune escape and has a low response rate to immunotherapy. Bioinformatics analysis and immunofluorescence staining of clinical samples revealed that the FGFR3 is involved in the immune escape in MIBC. Besides, after FGFR3 knockout with siRNA in RT112 and UMUC14 cells, the TLR3/NF-kB pathway was significantly activated and was accompanied by upregulation of MHC-I and PD-L1 gene expression. Furthermore, the use of TLR3 agonists poly(I:C) can further improve the effect.

CONCLUSION

Together, our results suggest that FGFR3 might involve in immunosuppression by inhibition of NF-kB pathway in BC. Considering that TLR3 agonists are currently approved for clinical treatment as immunoadjuvants, our study might provide more insights for improving the efficacy of immunotherapy in MIBC.

Prediction of risk and overall survival of pancreatic cancer from blood soluble immune checkpoint-related proteins

Background

Immune checkpoint inhibition holds promise as a novel treatment for pancreatic ductal adenocarcinoma (PDAC). The clinical significance of soluble immune checkpoint (ICK) related proteins have not yet fully explored in PDAC.

Methods

We comprehensively profiled 14 soluble ICK-related proteins in plasma in 70 PDAC patients and 70 matched healthy controls. Epidemiological data of all subjects were obtained through structured interviews, and patients' clinical data were retrieved from electronical health records. We evaluated the associations between the biomarkers with the risk of PDAC using unconditional multivariate logistic regression. Consensus clustering (k-means algorithm) with significant biomarkers was performed to identify immune subtypes in PDAC patients. Prediction models for overall survival (OS) in PDAC patients were developed using multivariate Cox proportional hazards regression. Harrell's concordance index (C-index), time-dependent receiver operating characteristic (ROC) curve and calibration curve were utilized to evaluate performance of prediction models. Gene expressions of the identified ICK-related proteins in tumors from TCGA were analyzed to provide insight into underlying mechanisms.

Results

Soluble BTLA, CD28, CD137, GITR and LAG-3 were significantly upregulated in PDAC patients (all q < 0.05), and elevation of each of them was correlated with PDAC increased risk (all p < 0.05). PDAC patients were classified into soluble immune-high and soluble immune-low subtypes, using these 5 biomarkers. Patients in soluble immune-high subtype had significantly poorer OS than those in soluble immune-low subtype (log-rank p = 9.7E-03). The model with clinical variables and soluble immune subtypes had excellent predictive power (C-index = 0.809) for the OS of PDAC patients. Furthermore, the immune subtypes identified with corresponding genes' expression in PDAC tumor samples in TCGA showed an opposite correlation with OS to that of immune subtypes based on blood soluble ICK-related proteins (log-rank p =0.02). The immune-high subtype tumors displayed higher cytolytic activity (CYT) score than immune-low subtype tumors (p < 2E-16).

Conclusion

Five soluble ICK-related proteins were identified to be significantly associated with the risk and prognosis of PDAC. Patients who were classified as soluble immune-low subtype based on these biomarkers had better overall survival than those of the soluble immune-high subtype.

HISMD: A Novel Immune Subtyping System for HNSCC

Immune subtyping is an important way to reveal immune heterogeneity, which may contribute to the diversity of the progression and treatment in head and neck squamous cell carcinoma (HNSCC). However, reported immune subtypes mainly focus on levels of immune infiltration and are mostly based on a mono-omics profile. This study aimed to identify a comprehensive immune subtype for HNSCC via multi-omics clustering and build a novel subtype prediction system for clinical application. Data were obtained from The Cancer Genome Atlas database and our independent multicenter cohort. Multi-omics clustering was performed to identify 3 clusters of 499 patients in The Cancer Genome Atlas based on immune-related gene expression and somatic mutations. The immune characteristics and biological features of the obtained clusters were revealed by bioinformatics, and 3 immune subtypes were identified: 1) adaptive immune activation subtype predominantly enriched in T cells, 2) innate immune activation subtype predominantly enriched in macrophages, and 3) immune desert subtype. Subsequently, the clinical implications of each subtype were analyzed per clinical epidemiology. We found that adaptive immune activation showed better survival outcomes and had a similar response to chemotherapy with innate immune activation, whereas immune desert might be relatively resistant to chemotherapy. Moreover, a subtype prediction system was developed by deep learning with whole slide images and named HISMD: HNSCC Immune Subtypes via Multi-omics and Deep Learning. We endowed HISMD with interpretability through image-based key feature extraction. The clinical implications, biological significances, and predictive stability of HISMD were successfully verified by using our independent multicenter cohort data set. In summary, this study revealed the immune heterogeneity of HNSCC and obtained a novel, highly accurate, and interpretable immune subtyping prediction system. For clinical implementation in the future, additional validation and utility studies are warranted.

Imaging phenotypes from MRI for the prediction of glioma immune subtypes from RNA sequencing: A multicenter study

Tumor subtyping based on its immune landscape may guide precision immunotherapy. The aims of this study were to identify immune subtypes of adult diffuse gliomas with RNA sequencing data, and to noninvasively predict this subtype using a biologically interpretable radiomic signature from MRI. A subtype discovery dataset (n = 210) from a public database and two radiogenomic datasets (n = 130 and 55, respectively) from two local hospitals were included. Brain tumor microenvironment-specific signatures were constructed from RNA sequencing to identify the immune types. A radiomic signature was built from MRI to predict the identified immune subtypes. The pathways underlying the radiomic signature were identified to annotate their biological meanings. The reproducibility of the findings was verified externally in multicenter datasets. Three distinctive immune subtypes were identified, including an inflamed subtype marked by elevated hypoxia-induced immunosuppression, a "cold" subtype that exhibited scarce immune infiltration with downregulated antigen presentation, and an intermediate subtype that showed medium immune infiltration. A 10-feature radiomic signature was developed to predict immune subtypes, achieving an AUC of 0.924 in the validation dataset. The radiomic features correlated with biological functions underpinning immune suppression, which substantiated the hypothesis that molecular changes can be reflected by radiomic features. The immune subtypes, predictive radiomic signature, and radiomics-correlated biological pathways were validated externally. Our data suggest that adult-type diffuse gliomas harbor three distinctive immune subtypes that can be predicted by MRI radiomic features with clear biological significance. The immune subtypes, radiomic signature, and radiogenomic links can be replicated externally.

Corrigendum: Immunogenomic landscape in breast cancer reveals immunotherapeutically relevant gene signatures

[This corrects the article DOI: 10.3389/fimmu.2022.805184.].

Integrated clinical analysis and data mining assessed the impact of NOX4 on the immune microenvironment and prognosis of pancreatic cancer

Background

The tumor microenvironment (TME) of pancreatic cancer is complex. which forms forms a microenvironment with high immunosuppression, ischemia and hypoxia, which promotes tumor proliferation and migration, inhibit the anti-tumor immune response. NOX4 plays an important role in tumor microenvironment and has a significant relationship with the occurrence, development and drug resistance of tumor.

Methods

Firstly, NOX4 expression in pancreatic cancer tissues under different pathological conditions was detected by applying immunohistochemical staining of tissue microarray (TMA). Transcriptome RNA sequencing data and clinical data of 182 pancreatic cancer samples were downloaded and collated from the UCSC xena database. 986 NOX4-related lncRNAs were filtered by Spearman correlation analysis. prognosis-related NOX4-related lncRNAs and NRlncSig Score were finally obtained by univariate and multivariate Cox regression with Least Absolute Shrinkage and Selection Operator (Lasso) analysis in pancreatic cancer patients. we plotted Kaplan -Meier and time-dependent ROC curves (ROC) to assess the validity in predicting the prognosis of pancreatic cancer. The ssGSEA analysis was applied to explore the immune microenvironment of pancreatic cancer patients as well as to discuss the immune cells and immune status separately.

Results

We found that a mature tumor marker, NOX4, play different roles in different clinical subgroups by immunohistochemical analysis and clinical data. Finally, 2 NOX4-related lncRNAs were determined by least absolute shrinkage and selection operator (LASSO) analysis, univariate Cox analysis and multivariate COX analysis. The ROC curve and DCA curve showed that NRS Score had better predictive ability than independent prognosis-related lncRNA and other clinicopathologic indicators. We obtained the relative abundance of 28 infiltrating immune cells by ssGSEA analysis and found a significant positive correlation between the abundance of anti-tumor immune cells and tumor-promoting immune cells in the risk-classified microenvironment. No matter NRS Score or AC092667.2, RP11-349A8.3 was significantly correlated with immune infiltrating cells. Meanwhile, the IC50 of conventional chemotherapeutic agents in high-score group were significantly lower than those in low-score group.

Conclusion

As a mature tumor marker, NOX4-related lncRNAs provide new research strategies for prognostic evaluation, molecular mechanism and clinical treatment of pancreatic cancer.

CEBPB is associated with active tumor immune environment and favorable prognosis of metastatic skin cutaneous melanoma

Metastatic skin cutaneous melanoma (SKCM) is a common malignancy that accounts for low morbidity but high mortality of skin cancer. SKCM is characterized by high lymphocytic infiltration, whereas the states of infiltrated cells are variable in patients leading to a heterogeneous prognosis and hindering appropriate clinical decisions. It is therefore urgent to identify markers associated with lymphocytic infiltration, cellular conditions, and the prognosis of SKCM. In this study, we report that CEBPB, a transcriptional factor, is mainly expressed in macrophages in metastatic SKCM and associated with an active tumor immune environment and a favorable prognosis through integrated analysis of single-cell and bulk RNA-seq datasets. High CEBPB expression is significantly associated with active inflammation and immune response pathways in both macrophages and bulk SKCM tumor tissues. A signature based on CEBPB-associated genes that are specifically expressed in macrophages could robustly and prognostically separate different metastatic SKCM patients. In addition, the associations between the metastatic SKCM tumor signature and microenvironment with respect to T-cell recruitment and state, inflammation response, angiogenesis, and so on were also determined. In conclusion, we present here the first report on CEBPB in tumor immune environment and prognosis regulation in metastatic SKCM and construct a reliable signature, which should provide a useful biomarker for stratification of the patient's prognosis and therapeutic selection.

Identification of immune subtypes to guide immunotherapy and targeted therapy in clear cell renal cell carcinoma

Accumulating pieces of evidence suggested that immunotypes may indicate the overall immune landscape in the tumor microenvironment, which were closely related to therapeutic response. The purpose of this study was to classify and define the immune subtypes of clear cell renal cell carcinoma (ccRCC), so as to authenticate the potential immune subtypes that respond to immunotherapy. Transcriptome expression profile and mutation profile data of ccRCC, as well as clinical characteristics used in this study were obtained from TCGA database. There were significant differences in the infiltration of immune cells, immune checkpoints, and antigens between ccRCC and para-cancerous tissues. According to immune components, patients with ccRCC were divided into three immune subtypes, with different clinical and molecular characteristics. Compared with other subtypes, IS2 showed cold immune phenotype, and was associated with better survival. IS1 represented complex immune populations and was associated with poor overall survival (OS) and progression free survival (PFS). Further analysis indicated that expression of immune checkpoints also differed among the three subtypes, and was abnormally up-regulated in IS3. Pathway enrichment analysis indicated that the mTOR signaling pathway was abnormally enriched in IS3, while the TGF_BETA, ANGIOGENESIS and receptor tyrosine kinase signaling pathways were abnormally enriched in IS2. Furthermore, there was an abnormal enrichment of the epithelial-to-mesenchymal transition (EMT) signaling pathway in IS1, which may be associated with a higher rate of metastasis. Finally, SCG2 was screened as a specific antigen of ccRCC, which was not only related to poor prognosis, but also significantly associated with immune cells and immune checkpoints. In conclusion, the immune subtypes of ccRCC may provide new insights into the tumor biology and the precise clinical management of this disease.

Identification of Tumor Antigens and Immune Subtypes of Malignant Mesothelioma for mRNA Vaccine Development

BACKGROUND

mRNA-based cancer vaccines have been considered a promising anticancer therapeutic approach against various cancers, yet their efficacy for malignant mesothelioma (MESO) is still not clear. The present study is designed to identify MESO antigens that have the potential for mRNA vaccine development, and to determine the immune subtypes for the selection of suitable patients.

METHODS

A total of 87 MESO datasets were used for the retrieval of RNA sequencing and clinical data from The Cancer Genome Atlas (TCGA) databases. The possible antigens were identified by a survival and a genome analysis. The samples were divided into two immune subtypes by the application of a consensus clustering algorithm. The functional annotation was also carried out by using the DAVID program. Furthermore, the characterization of each immune subtype related to the immune microenvironment was integrated by an immunogenomic analysis. A protein-protein interaction network was established to categorize the hub genes.

RESULTS

The five tumor antigens were identified in MESO. FAM134B, ALDH3A2, SAV1, and RORC were correlated with superior prognoses and the infiltration of antigen-presenting cells (APCs), while FN1 was associated with poor survival and the infiltration of APCs. Two immune subtypes were identified; TM2 exhibited significantly improved survival and was more likely to benefit from vaccination compared with TM1. TM1 was associated with a relatively quiet microenvironment, high tumor mutation burden, and enriched DNA damage repair pathways. The immune checkpoints and immunogenic cell death modulators were also differentially expressed between two subtypes. Finally, FN1 was identified to be the hub gene.

CONCLUSIONS

FAM134B, ALDH3A2, SAV1, RORC, and FN1 are considered as possible and effective mRNA anti-MESO antigens for the development of an mRNA vaccine, and TM2 patients are the most suitable for vaccination.

Identification and Verification of Immune Subtype-Related lncRNAs in Clear Cell Renal Cell Carcinoma

Background

According to clinical study results, immune checkpoint blockade (ICB) treatment enhances the survival outcome of patients with clear cell renal cell carcinoma (ccRCC). Previous research has divided ccRCC patients into immune subtypes with distinct ICB response rates. However, the study on the association between lncRNAs and ccRCC immune subtypes is lacking.

Methods

Differentially expressed lncRNAs/mRNAs between two major immune subgroups were calculated. A weighted gene co-expression network analysis (WGCNA) was conducted to establish the lncRNA-mRNA co-expression network and select the key lncRNAs. Then, prognostic lncRNAs were selected from the network by the bioinformatics method. Next, the risk-score was estimated by lncRNA expression and their coefficients. Finally, a nomogram based on lncRNAs and clinical parameters was created to predict the prognosis of ccRCC.

Results

LncRNAs and mRNAs associated with ccRCC immune subtypes were identified. The lncRNAs and mRNAs from a gene module closely linked to the immune subtype were used to construct a network. The KEGG pathways enriched in the network were related to immune system activation processes. These 8 lncRNAs (AL365361.1, LINC01934, AC090152.1, PCED1B-AS1, LINC00426, AC007728.2, AC243829.4, and LINC00158) were found to be positively correlated with immune cells of the tumor microenvironment. The C-index of the nomogram was 0.777, and the calibration curve data suggests that the nomogram has a high degree of discriminating capacity.

Conclusion

In summary, we discovered core lncRNAs linked with immune subtypes and created corresponding lncRNA–mRNA networks. These lncRNAs are anticipated to have predictive significance for ccRCC and may provide insight into novel biomarkers for the disease.

Discovering a Four-Gene Prognostic Model Based on Single-Cell Data and Gene Expression Data of Pancreatic Adenocarcinoma

Background

Pancreatic cancer has a 5-year overall survival lower than 8%. Pancreatic adenocarcinoma (PAAD) is the most common type. This study attempted to explore novel molecular subtypes and a prognostic model through analyzing tumor microenvironment (TME).

Materials and Methods

Single-cell RNA sequencing (scRNA-seq) data and expression profiles from public databases were downloaded. Three PAAD samples with single-cell data and 566 samples with gene expression data were included. Seurat was used to identify cell subsets. SVA merged and removed batch effects from multichip datasets. CIBERSORT was used to evaluate the components of different cells in transcriptome, ConsensusClusterPlus was used to identify molecular subtypes, and gene set enrichment analysis was used for functional enrichment analysis. LASSO Cox was performed to construct dimensionality reduction and prognosis model.

Results

Memory B cells (MBCs) were identified to be significantly with PAAD prognosis. Two immune subtypes (IS1 and IS2) with distinct overall survival were constructed. Forty-one DEGs were identified between IS1 and IS2. Four prognostic genes (ANLN, ARNTL2, SERPINB5, and DKK1) were screened to develop a prognostic model. The model was effective in classifying samples into high-risk and low-risk groups with distinct prognosis. Three subgroups of MBCs were identified, where MBC_0 and MBC_1 were differentially distributed between IS1 and IS2, high-risk and low-risk groups.

Conclusions

MBCs were closely involved in PAAD progression, especially MBC_0 and MBC_1 subgroups. The four-gene prognostic model was predictive of overall survival and could guide immunotherapy for patients with PAAD.

Comprehensive Analysis of Programmed Cell Death Signature in the Prognosis, Tumor Microenvironment and Drug Sensitivity in Lung Adenocarcinoma

Programmed cell death (PCD) is a process that regulates the homeostasis of cells in the body, and it plays an important role in tumor immunity. However, the expression profile and clinical characteristics of PCD-related genes remain unclear. In this study, we comprehensively analysed the PCD genes with the tumor microenvironment (TME), drug sensitivity, immunothearapy response, and evaluated their prognostic value through systematic bioinformatics methods.We identified 125 PCD-related regulatory factors, which were expressed differently in lung adenocarcinoma (LUAD) and normal lung tissues. 32 PCD related prognostic genes associated with LUAD were identified by univariate Cox analysis. 23 PCD-related gene signature was constructed, and all LUAD patients in the Cancer Genome Atlas (TCGA) dataset were stratified as low-risk or high-risk groups according to the risk score. This signature had a powerful prognostic value, which was validated in three independent data sets and clinical subtypes. Additionally, it has unique properties in TME. Further analysis showed that different risk groups have different immune cell infiltration, immune inflammation profile, immune pathways, and immune subtypes. In addition, the low-risk group had a better immunotherapy response with higher levels of multiple immune checkpoints and lower Tumor immune dysfunction and exclusion (TIDE) score, while the high-risk group was sensitive to multiple chemotherapeutic drugs because of its lower IC50. In short, this is the first model to predict the prognosis and immunological status of LUAD patients based on PCD-related genes. It may be used as a predictor of immunotherapy response to achieve customized treatment of LUAD.

Subtyping for pancreatic cancer precision therapy

The cases of pancreatic cancer and associated deaths are increasing consistently and have become a global health concern. Prevalent intratumoral and intertumoral heterogeneity in pancreatic cancer has been revealed as an important cause of its poor prognosis. However, few precision management strategies have been formulated to treat this complex disease. There is growing evidence supporting the significance of subtyping pancreatic tumors on the basis of their molecular characteristics for improving the accuracy of clinical decision-making on treatment. Here, we summarize the current approaches to classification of pancreatic cancer, and highlight the feasibility and potential defects of their application in precision therapy.

Immune-Associated Gene Signatures and Subtypes to Predict the Progression of Atherosclerotic Plaques Based on Machine Learning

Objective: Experimental and clinical evidence suggests that atherosclerosis is a chronic inflammatory disease. Our study was conducted for uncovering the roles of immune-associated genes during atherosclerotic plaque progression.

Methods: Gene expression profiling of GSE28829, GSE43292, GSE41571, and GSE120521 datasets was retrieved from the GEO database. Three machine learning algorithms, least absolute shrinkage, and selection operator (LASSO), random forest, and support vector machine–recursive feature elimination (SVM-RFE) were utilized for screening characteristic genes among atherosclerotic plaque progression- and immune-associated genes. ROC curves were generated for estimating the diagnostic efficacy. Immune cell infiltrations were estimated via ssGSEA, and immune checkpoints were quantified. CMap analysis was implemented to screen potential small-molecule compounds. Atherosclerotic plaque specimens were classified using a consensus clustering approach.

Results: Seven characteristic genes (TNFSF13B, CCL5, CCL19, ITGAL, CD14, GZMB, and BTK) were identified, which enabled the prediction of progression of atherosclerotic plaques. Higher immune cell infiltrations and immune checkpoint expressions were found in advanced-stage than in early-stage atherosclerotic plaques and were positively linked to characteristic genes. Patients could clinically benefit from the characteristic gene-based nomogram. Several small molecular compounds were predicted based on the characteristic genes. Two subtypes, namely, C1 immune subtype and C2 non-immune subtype, were classified across atherosclerotic plaques. The characteristic genes presented higher expression in C1 than in C2 subtypes.

Conclusion: Our findings provide several promising atherosclerotic plaque progression- and immune-associated genes as well as immune subtypes, which might enable to assist the design of more accurately tailored cardiovascular immunotherapy.

Prognostic value and immune characteristics of RUNX gene family in human cancers: a pan-cancer analysis

Background: Runt-related transcription factors (RUNX) are involved in numerous fundamental biological processes and play crucial parts in tumorigenesis and metastasis both directly and indirectly. However, the pan-cancer evidence of the RUNX gene family is not available.

Methods: In this study, we analyzed the potential association between RUNX gene family expression and patient’s prognosis, immune cell infiltration, drug response, and genetic mutation data across different types of tumors using based on The Cancer Genome Atlas, Gene Expression Omnibus, and Oncomine database.

Results: The results showed that the expression of the RUNX gene family varied among different cancer types, revealing its heterogeneity in cancers and that expression of RUNX2 was lower than that of RUNX1 and RUNX3 across all cancer types. RUNX gene family gene expression was related to prognosis in several cancers. Furthermore, our study revealed a clear association between RUNX gene family expression and ESTIMATE score, RNA stemness, and DNA stemness scores. Compared with RUNX1 and RUNX2, RUNX3 showed relatively low levels of genetic alterations. RUNX gene family genes had clear associations with immune infiltrate subtypes, and their expression was positively related to immune checkpoint genes and drug sensitivity in most cases. Two immunotherapy cohorts confirm that the expression of RUNX was correlated with the clinical response of immunotherapy.

Conclusions: These findings will help to elucidate the potential oncogenic roles of RUNX gene family genes in different types of cancer and it can function as a prognostic marker in various malignant tumors.

Immunogenomic Landscape in Breast Cancer Reveals Immunotherapeutically Relevant Gene Signatures

Breast cancer is characterized by some types of heterogeneity, high aggressive behaviour, and low immunotherapeutic efficiency. Detailed immune stratification is a prerequisite for interpreting resistance to treatment and escape from immune control. Hence, the immune landscape of breast cancer needs further understanding. We systematically clustered breast cancer into six immune subtypes based on the mRNA expression patterns of immune signatures and comprehensively depicted their characteristics. The immunotherapeutic benefit score (ITBscore) was validated to be a superior predictor of the response to immunotherapy in cohorts from various datasets. Six distinct immune subtypes related to divergences in biological functions, signatures of immune or stromal cells, extent of the adaptive immune response, genomic events, and clinical prognostication were identified. These six subtypes were characterized as immunologically quiet, chemokine dominant, lymphocyte depleted, wounding dominant, innate immune dominant, and IFN-γ dominant and exhibited features of the tumor microenvironment (TME). The high ITBscore subgroup, characterized by a high proportion of M1 macrophages:M2 macrophages, an activated inflammatory response, and increased mutational burden (such as mutations in TP53, CDH1 and CENPE), indicated better immunotherapeutic benefits. A low proportion of tumor-infiltrating lymphocytes (TILs) and an inadequate response to immune treatment were associated with the low ITBscore subgroup, which was also associated with poor survival. Analyses of four cohorts treated with immune checkpoint inhibitors (ICIs) suggested that patients with a high ITBscore received significant therapeutic advantages and clinical benefits. Our work may facilitate the understanding of immune phenotypes in shaping different TME landscapes and guide precision immuno-oncology and immunotherapy strategies.

Recent advances in primary resistance mechanisms against immune checkpoint inhibitors

PURPOSE OF REVIEW

The resistance of immune checkpoint inhibitors (ICIs) has become an obstacle to further improve the survival of patients with advanced cancer. This review provides an overview of recent advances in primary resistance mechanisms of ICIs.

RECENT FINDINGS

With the improvement of study approach, new characteristics and trends have emerged in the classification of tumor immune subtypes. The effects of germline genetic on tumor microenvironment and the efficacy of immunotherapy have been further studied. Exosomal programmed death-ligand 1 (PD-L1) is an increasing focus of research in primary resistance mechanisms of ICIs. In addition to antibiotics and steroids, the influence of other concomitant medications on the efficacy of ICIs has recently gained more attention.

SUMMARY

Exploring the resistance mechanisms of ICIs is one of the great challenges in the field of tumor immunotherapy. Continued work to understand the resistance mechanism of ICIs is ongoing.

M2 subtype tumor associated macrophages (M2-TAMs) infiltration predicts poor response rate of immune checkpoint inhibitors treatment for prostate cancer

BACKGROUND

Prostate cancer (PCa) is poor response to the immunotherapy for its high heterogeneity of immune microenvironment. In this study, we aim to introduce a new immune subtype for PCa involving M2 tumour associated macrophages (M2-TAMs).

METHODS

Three hundred and sixty-two PCa patients and matched normal prostate tissues were selected from the Cancer Genome Atlas and Gene Expression Omnibus databases. Patients' immune infiltration characters were then analyzed based on the gene expressions. The immune subtypes were identified by the method of unsupervised hierarchical clustering. Finally, the relationship between the M2-TAMs infiltration and anti-programmed death-ligand-1 (PD-L1) therapy was investigated in the IMvigor210 cohort.

RESULTS

PCa expressed lower immune-related genes levels compared with the adjacent normal tissues. Based on the proved immunosuppressive mechanisms in PCa, tumour patients were classified into three independent subclasses with high infiltrated cytolytic activity (CYT), M2-TAMs and regulatory T cell (Tregs), respectively. Among these subtypes, M2-TAMs infiltration subtype showed the worst clinicopathological features and prognosis compared with the other two subtypes. The results of the IMvigor210 cohort demonstrated poor response of anti-PD-L1 therapy for patients with high M2-TAMs infiltration.

CONCLUSION

Prostate tumours involved in significant immunosuppression, and high infiltration of M2-TAMs can be applied to predict the effect of anti-PD-L1 therapy.Key MessagesPCa patients can be classified into three immunotypes of high infiltrated CYT, M2-TAMS, and Tregs according to the immunosuppressive mechanisms.High M2-TAMs infiltration subtype reflected the worst clinical characters, immune infiltration, and lowest expression of immune checkpoint inhibitors among the three subclasses in PCa.High M2-TAMs infiltration predicts the low response rate of anti-PD-L1 therapy.

Identification of Methylation Immune Subtypes and Establishment of a Prognostic Signature for Gliomas Using Immune-Related Genes

DNA methylation patterns are essential in understanding carcinogenesis. However, the relationship between DNA methylation and the immune process has not been clearly established—this study aimed at elucidating the interaction between glioma and DNA methylation, consolidating glioma classification and prognosis. A total of 2,483 immune-related genes and 24,556 corresponding immune-related methylation probes were identified. From the Cancer Genome Atlas (TCGA) glioma cohort, a total of 683 methylation samples were stratified into two different clusters using unsupervised clustering, and eight types of other cancer samples from the TCGA database were shown to exhibit excellent distributions. A total of 3,562 differentially methylated probes (DMPs) were selected and used for machine learning. A five-probe signature was established to evaluate the prognosis of glioma as well as the potential benefits of radiotherapy and Procarbazine, CCNU, Vincristine (PCV) treatment. Other prognostic clinical models, such as nomogram and decision tree, were also evaluated. Our findings confirmed the interactions between immune-related methylation patterns and glioma. This novel approach for cancer molecular characterization and prognosis should be validated in further studies.

Immune Classification and Immune Landscape Analysis of Triple-Negative Breast Cancer

Background: To classify triple-negative breast cancer (TNBC) immunotyping using the public database, analyze the differences between subtypes in terms of clinical characteristics and explore the role and clinical significance of immune subtypes in TNBC immunotherapy.

Methods: We downloaded TNBC data from the cBioPortal and GEO databases. The immune genes were grouped to obtain immune gene modules and annotate their biological functions. Log-rank tests and Cox regression were used to evaluate the prognosis of immune subtypes (IS). Drug sensitivity analysis was also performed for the differences among immune subtypes in immunotherapy and chemotherapy. In addition, dimension reduction analysis based on graph learning was utilized to reveal the internal structure of the immune system and visualize the distribution of patients.

Results: Significant differences in prognosis were observed between subtypes (IS1, IS2, and IS3), with the best in IS3 and the worst in IS1. The sensitivity of IS3 to immunotherapy and chemotherapy was better than the other two subtypes. In addition, Immune landscape analysis found the intra-class heterogeneity of immune subtypes and further classified IS3 subtypes (IS3A and IS3B). Immune-related genes were divided into seven functional modules (The turquoise module has the worst prognosis). Five hub genes (RASSF5, CD8A, ICOS, IRF8, and CD247) were screened out as the final characteristic genes related to poor prognosis by low expression.

Conclusions: The immune subtypes of TNBC were significantly different in prognosis, gene mutation, immune infiltration, drug sensitivity, and heterogeneity. We validated the independent role of immune subtypes in tumor progression and immunotherapy for TNBC. This study provides a new perspective for personalized immunotherapy and the prognosis evaluation of TNBC patients in the future.

The Landscape of Immune Cells Indicates Prognosis and Applicability of Checkpoint Therapy in Hepatocellular Carcinoma

Background

Tumor-infiltrating immune cells are important components of tumor microenvironment (TME), and their composition reflects the confrontation between host immune system and tumor cells. However, the relationship between the composition of infiltrating immune cells, prognosis, and the applicability of anti-PD-1/PD-L1 therapy in hepatocellular carcinoma (HCC) needs systematic examination.

Methods

Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) was applied to evaluate the infiltration of immune cells based on The Cancer Genome Atlas (TCGA) liver hepatocellular carcinoma (LIHC) cohort. Diagnostic and prognostic models were constructed based on immune cells, and the models were validated by two external cohorts. The relationship between immune cells and PD-L1 was evaluated by Spearman correlation, and the finding was validated in our in-house HCC sample.

Result

Patients in TCGA LIHC cohort were classified into six subtypes with different prognosis based on the proportion of tumor-infiltrating immune cells simulated via CIBERSORT. Among 22 types of immune cells, intratumoral PD-L1 mRNA level exhibited linear relationship with the fraction of five types of immune cells (M1 macrophages, plasma cells, CD8⁺ T cells, resting mast cells, and regulatory T cells), and M1 macrophages showed the strongest relevance (R = 0.26, p < 0.001). Immunohistochemistry of our in-house HCC specimens verified this conclusion. Moreover, intratumoral mRNA levels of M1 macrophage-associated cytokines were positively correlated with PD-L1 level.

Conclusions

Our study demonstrated that the prognosis of HCC patients was associated with the pattern of infiltrating immune cells in TME, and macrophage-associated cytokines might be a potential non-invasive marker for predicting the PD-L1 level for HCC patients.

Recognition of Tumor-Associated Antigens and Immune Subtypes in Glioma for mRNA Vaccine Development

Background

Although mRNA vaccines have been efficient for combating a variety of tumors, their effectiveness against glioma remains unclear. There is growing evidence that immunophenotyping can reflect the comprehensive immune status and microenvironment of the tumor, which correlates closely with treatment response and vaccination potency. The purpose of this research was to screen for effective antigens in glioma that could be used for developing mRNA vaccines and to further differentiate the immune subtypes of glioma to create an selection criteria for suitable patients for vaccination.

Methods

Gene expression profiles and clinical data of 698 glioma samples were extracted from The Cancer Genome Atlas, and RNA_seq data of 1018 glioma samples was gathered from Chinese Glioma Genome Atlas. Gene Expression Profiling Interactive Analysis was used to determine differential expression genes and prognostic markers, cBioPortal software was used to verify gene alterations, and Tumor Immune Estimation Resource was used to investigate the relationships among genes and immune infiltrating cells. Consistency clustering was applied for consistent matrix construction and data aggregation, Gene oncology enrichment was performed for functional annotation, and a graph learning-based dimensionality reduction method was applied to describe the subtypes of immunity.

Results

Four overexpressed and mutated tumor antigens associated with poor prognosis and infiltration of antigen presenting cells were identified in glioma, including TP53, IDH1, C3, and TCF12. Besides, four immune subtypes of glioma (IS1-IS4) and 10 immune gene modules were identified consistently in the TCGA data. The immune subtypes had diverse molecular, cellular, and clinical features. IS1 and IS4 displayed an immune-activating phenotype and were associated with worse survival than the other two subtypes, while IS2 and IS3 had lower levels of tumor immune infiltration. Immunogenic cell death regulators and immune checkpoints were also diversely expressed in the four immune subtypes.

Conclusion

TP53, IDH1, C3, and TCF12 are effective antigens for the development of anti-glioma mRNA vaccines. We found four stable and repeatable immune subtypes of human glioma, the classification of the immune subtypes of glioma may play a crucial role in the predicting mRNA vaccine outcome.

A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype

Background

Immune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB.

Methods

Open datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB.

Results

A total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/.

Conclusion

TNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed.

Pan-Cancer Analysis of the Associations of TGFBI Expression With Prognosis and Immune Characteristics

Transforming growth factor-beta-induced (TGFBI) protein has important roles in tumor growth, metastasis, and immunity. However, there is currently no pan-cancer evidence regarding TGFBI. In this study, we conducted a pan-cancer analysis of TGFBI mRNA and protein expression and prognoses of various cancer types using public databases. We also investigated the associations of TGFBI expression with tumor microenvironment (TME) components, immune cell infiltration, tumor mutational burden (TMB), and microsatellite instability (MSI), along with the TGFBI genetic alteration types. The results showed that TGFBI expression varied among different cancer types, and it was positively or negatively related to prognosis in various cancers. TGFBI expression was also significantly correlated with TME components, TMB, MSI, immune cell infiltration, and immunoinhibitory and immunostimulatory gene subsets. These findings indicate that TGFBI participates in various immune responses and it may function as a prognostic marker in various cancers. The findings may be useful for developing immunotherapies that target TGFBI.

Identification of Immune-Related Subtypes and Characterization of Tumor Microenvironment Infiltration in Bladder Cancer

Tumors are closely related to the tumor microenvironment (TME). The complex interaction between tumor cells and the TME plays an indisputable role in tumor development. Tumor cells can affect the TME, promote tumor angiogenesis and induce immune tolerance by releasing cell signaling molecules. Immune cell infiltration (ICI) in the TME can affect the prognosis of patients with bladder cancer. However, the pattern of ICI of the TME in bladder cancer has not yet been elucidated. Herein, we identified three distinct ICI subtypes based on the TME immune infiltration pattern of 584 bladder cancer patients using the ESTIMATE and CIBERSORT algorithms. Then, we identified three gene clusters based on the differentially expressed genes (DEGs) between the three ICI subtypes. In addition, the ICI score was determined using single sample gene set enrichment analysis (ssGSEA). The results suggested that patients in the high ICI score subgroup had a favorable prognosis and higher expression of checkpoint-related and immune activity-related genes. The high ICI score subgroup was also linked to increased tumor mutation burden (TMB) and neoantigen burden. A cohort treated with anti-PD-L1 immunotherapy confirmed the therapeutic advantage and clinical benefit of patients with higher ICI scores. In the end, our study also shows that the ICI score represents an effective prognostic predictor for evaluating the response to immunotherapy. In conclusion, our study deepened the understanding of the TME, and it provides new ideas for improving patients' response to immunotherapy and promoting individualized tumor immunotherapy in the future.

Classification of Osteosarcoma Based on Immunogenomic Profiling

Accumulating evidence has supported that osteosarcoma is heterogeneous, and several subtypes have been identified based on genomic profiling. Immunotherapy is revolutionizing cancer treatment and is a promising therapeutic strategy. In contrast, few studies have identified osteosarcoma classification based on immune biosignatures, which offer the optimal stratification of individuals befitting immunotherapy. Here, we classified osteosarcoma into two clusters: immunity high and immunity low using the single-sample gene-set enrichment analysis and unsupervised hierarchical clustering. Immunity_H subtype was associated with high immune cells infiltration, a favorable prognosis, benefit to immunotherapy, high human leukocyte antigen gene expression, and activated immune signal pathway indicating an immune-hot phenotype. On the contrary, the Immunity_L subtype was correlated with low immune cell infiltration, poor prognosis, and cancer-related pathway, indicating an immune-cold phenotype. We also identified TYROBP as a key immunoregulatory gene associated with CD8⁺ T cell infiltration by multiplex immunohistochemistry. Finally, we established an immune-related prognostic model that predicted the survival time of osteosarcoma. In conclusion, we established a new classification system of osteosarcoma based on immune signatures and identified TYROBP as a key immunoregulatory gene. This stratification had significant clinical outcomes for estimating prognosis, as well as the immunotherapy of osteosarcoma patients.

A novel prognostic mRNA/miRNA signature for esophageal cancer and its immune landscape in cancer progression

Mounting evidence shows that MicroRNAs (miRNAs) and their target genes are aberrantly expressed in many cancers and are linked to tumor occurrence and progression, especially in esophageal cancer (EC). This study purposed to explore new biomarkers related to the prognosis of EC and to uncover their potential mechanisms in promoting tumor progression. We identified 162 differentially expressed miRNAs and 4555 differentially expressed mRNAs in EC. Then, a risk model involving three miRNAs (miR‐4521, miR‐3682‐3p, and miR‐1269a) was designed to predict prognosis in EC patients. Furthermore, 7 target genes (Rho GTPase‐activating protein 24, Chromobox 3, Contactin‐associated protein 2, ELOVL fatty acid elongase 5, LIF receptor subunit alpha, transmembrane protein 44, and transmembrane protein 67) were selected for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to reveal their potential mechanisms in promoting EC progression. After a series of correlation analyses, miRNA target genes were found to be significantly positively or negatively associated with immune infiltration, tumor microenvironment, cancer stemness properties, and tumor mutation burden at different degrees in EC. To further elucidate the role of miRNA signature in cancer progression, we performed a pan‐cancer analysis to determine whether these genes exert similar effects on other tumors. Interestingly, the miRNA target genes altered expression on tumor immunity; however, pan‐cancer progression was the same as that of EC. Thus, we explored the immune landscape of the miRNA signature and its target genes in EC and pan‐cancer. These findings demonstrated the versatility and effectiveness of our model in various cancers and provided a new direction for cancer management.

Indoleamine 2, 3-Dioxygenase 1 and CD8 Expression Profiling Revealed an Immunological Subtype of Colon Cancer With a Poor Prognosis

Background

The Immunoscore method, based on the distribution of the quantification of cytotoxic and memory T cells, provides an indicator of tumor recurrence for colon cancer. However, recent evidence has suggested that immune checkpoint expression represents a surrogate measure of tumor-infiltrating T cell exhaustion, and therefore may serve as a more accurate prognostic biomarker for colon cancer. Indoleamine 2, 3-dioxygenase 1 (IDO1), a potent immunosuppressive molecule, has been strongly associated with T-cell infiltration, but it lacks universal prognostic significance among all of the cancer subtypes. Our aim was to elucidate the prognostic significance of the combination of IDO1 and CD8A expression in colon cancer.

Methods

Gene expression and clinical survival data were analyzed using The Cancer Genome Atlas (TCGA) data set and validated using NCBI Gene Expression Omnibus (NCBI-GEO) cohort. Hierarchical clustering, functional enrichment analyses, and immune infiltration analysis were applied to evaluate the distinctive immune statuses in colon cancer risk subgroups stratified by IDO1 and CD8A expression. Moreover, Multivariate Cox regression analysis and Receiver Operating Characteristic (ROC) analyses were conducted to determine the prognostic value of IDO1/CD8A stratification. The IDO1/CD8A classifier may be suitable for use in the prediction of cancer development. It was validated via an in vivo murine model.

Results

The stratification analysis demonstrated that the colon cancer subtype with the CD8A^highIDO1^high* tumor resulted in the worst survival despite high levels of CD8 infiltrates. Its poor prognosis was associated with high levels of immune response, checkpoint genes, and Th1/IFN-γ gene signatures, regardless of CMS classification. Moreover, the IDO1/CD8A stratification was identified as an independent prognostic factor of overall survival (OS) and a useful predictive biomarker in colon cancer. In vivo data revealed the CD8A^highIDO1^high group showed strong correlations with late-stage metastasis of colon carcinoma cells and upregulation of immune checkpoints.

Conclusions

The findings indicate that the proposed IDO1/CD8A stratification has exact and independent prognostic implications beyond CD8 T cell alone and CMS classification. As a result, it may represent a promising tool for risk stratification in colon cancer and improve the development of immunotherapies for patients with colon cancer in the future.

A Pan-Cancer Study of Epidermal Growth Factor-Like Domains 6/7/8 as Therapeutic Targets in Cancer

With highly homologous epidermal growth factor (EGF)-like (EGFL) domains, the members of the EGFL family play crucial roles in growth, invasion, and metastasis of tumors and are closely associated with the apoptosis of tumor cells and tumor angiogenesis. Furthermore, their contribution to immunoreaction and tumor microenvironment is highly known. In this study, a comprehensive analysis of EGFL6, -7, and -8 was performed on the basis of their expression profiles and their relationship with the rate of patient survival. Through a pan-cancer study, their effects were correlated with immune subtypes, tumor microenvironment, and drug resistance. Using The Cancer Genome Atlas pan-cancer data, expression profiles of EGFL6, -7, and -8, and their association with the patient survival rate and tumor microenvironment were analyzed in 33 types of cancers. The expression of the EGFL family was different in different cancer types, revealing the heterogeneity among cancers. The results showed that the expression of EGFL8 was lower than EGFL6 and EGFL7 among all cancer types, wherein EGFL7 had the highest expression. The univariate Cox proportional hazard regression model showed that EGFL6 and EGFL7 were the risk factors to predict poor prognosis of cancers. Survival analysis was then used to verify the relationship between gene expression and patient survival. Furthermore, EGFL6, EGFL7, and EGFL8 genes revealed a clear association with immune infiltrate subtypes; they were also related to the infiltration level of stromal cells and immune cells with different degrees. Moreover, they were negatively correlated with the characteristics of cancer stem cells measured by DNAs and RNAs. In addition, EGFL6, -7, and -8 were more likely to contribute to the resistance of cancer cells. Our systematic analysis of EGFL gene expression and their correlation with immune infiltration, tumor microenvironment, and prognosis of cancer patients emphasized the necessity of studying each EGFL member as a separate entity within each particular type of cancer. Simultaneously, EGFL6, -7, and -8 signals were verified as promising targets for cancer therapies, although further laboratory validation is still required.

Molecular subtyping of hepatocellular carcinoma: A step toward precision medicine

Hepatocellular carcinoma (HCC) is one of the most prevalent and fatal digestive tumors. Treatment for this disease has been constraint by heterogeneity of this group of tumors, which has greatly limited the progress in personalized therapy. Although existing studies have revealed the genetic and epigenetic blueprints that drive HCCs, many of the molecular mechanisms that lead to HCCs remain elusive. Recent advances in techniques for studying functional genomics, such as genome sequencing and transcriptomic analyses, have led to the discovery of molecular mechanisms that participate in the initiation and evolution of HCC. Integrative multi-omics analyses have identified several molecular subtypes of HCC associated with specific molecular characteristics and clinical outcomes. Deciphering similar molecular features among highly heterogeneous HCC patients is a prerequisite to implementation of personalized therapeutics. This review summarizes the current research progresses in precision therapy on the backbone of molecular subtypes of HCC.

A Molecular Subtype Model for Liver HBV-Related Hepatocellular Carcinoma Patients Based on Immune-Related Genes

Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with a very poor prognosis. Immunotyping is of great significance for predicting HCC outcomes and guiding immunotherapy. Therefore, we sought to establish a reliable prognostic model for HBV-related HCC based on immune scores. We identified immune-related modules of The Cancer Genome Atlas LIHC and GSE14520 data sets through weighted gene co-expression network analysis and evaluated HCC through a non-negative matrix factorization algorithm. Through further bioinformatics analyses, we identified causes for prognostic differences between subtypes. The results illustrate a significant difference in prognosis based on immunotypes, which may stem from metabolic disorders and increased tumor invasion associated with the high expression of genes related to stem cell characteristics. In conclusion, we identified a novel HBV-related HCC immune subtype and determined its immunological characteristics, which provides ideas for further individualized immunotherapy research.

Novel immune subtypes of lung adenocarcinoma identified through bioinformatic analysis

The magnitude of the immune response is closely associated with clinical outcome in patients with cancer. However, finding potential therapeutic targets for lung cancer in the immune system remains challenging. Here, we constructed a vital immune‐prognosis genes (VIPGs) based cluster of lung adenocarcinoma (LUAD) from IMMPORT databases and The Cancer Genome Atlas. A transcription factor regulatory network for the VIPGs was also established. The tumor microenvironment of LUAD was analyzed using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm and single‐sample Gene Set Enrichment Analysis. The immune checkpoints and genomic alterations were explored in the different immune clusters. We identified 15 VIPGs for patients with LUAD and clustered the patients into low‐immunity and high‐immunity subtypes. The immune score, stromal score and ESTIMATE score were significantly higher in the high‐immunity subtype, whereas tumor purity was higher in the low‐immunity subtype. In addition, the immune checkpoints cytotoxic T lymphocyte associate protein‐4(CTLA4), programmed cell death protein‐1 and programmed death‐ligand were elevated in the low‐immunity subtype. The genomic results also showed that the tumor mutation burden was higher in the high‐immunity subtype. Finally, Gene Set Enrichment Analysis showed that several immune‐related gene sets, including interleukin‐2/STAT5 signaling, inflammatory response, interleukin‐6/Janus kinase(JAK)/signal transducer and activator of transcription 3 (STAT3) signaling, interferon‐gamma response and allograft rejection, were elevated in the high‐immunity subtype. Finally, high‐immunity patients exhibited greater overall and disease‐specific survival outcome compared with low‐immunity patients (log rank P = 0.013 and P = 0.0097). Altogether, here we have identified 15 immune‐prognosis genes and a potential immune subtype for patients with LUAD, which may provide new insights into the prognosis and treatment of LUAD.

Immunological analyses reveal an immune subtype of uveal melanoma with a poor prognosis

Uveal melanoma is an aggressive intraocular malignancy that often exhibits low immunogenicity. Metastatic uveal melanoma samples frequently exhibit monosomy 3 or BAP1 deficiency. In this study, we used bioinformatic methods to investigate the immune infiltration of uveal melanoma samples in public datasets. We first performed Gene Set Enrichment/Variation Analyses to detect immunological pathways that are altered in tumors with monosomy 3 or BAP1 deficiency. We then conducted an unsupervised clustering analysis to identify distinct immunologic molecular subtypes of uveal melanoma. We used CIBERSORT and ESTIMATE with RNA-seq data from The Cancer Genome Atlas and the GSE22138 microarray dataset to determine the sample-level immune subpopulations and immune scores of uveal melanoma samples. The Kaplan-Meier method and log-rank test were used to assess the prognostic value of particular immune cells and genes in uveal melanoma samples. Through these approaches, we discovered uveal melanoma-specific immunologic features, which may provide new insights into the tumor microenvironment and enhance the development of immunotherapies in the future.