Characterization of Hypoxia-Related Molecular Subtypes in Clear Cell Renal Cell Carcinoma to Aid Immunotherapy and Targeted Therapy via Multi-Omics Analysis

Identification and validation of prognostic signature genes of bladder cancer by integrating methylation and transcriptomic analysis

Being a frequent malignant tumor of the genitourinary system, Bladder Urothelial Carcinoma (BLCA) has a poor prognosis. This study focused on identifying and validating prognostic biomarkers utilizing methylation, transcriptomics, and clinical data from The Cancer Genome Atlas Bladder Urothelial Carcinoma (TCGA BLCA) cohort. The impact of altered differentially methylated hallmark pathway genes was subjected to clustering analysis to observe changes in the transcriptional landscape on BLCA patients and identify two subtypes of patients from the TCGA BLCA population where Subtype 2 was associated with the worst prognosis with a p-value of 0.00032. Differential expression and enrichment analysis showed that subtype 2 was enriched in immune-responsive and cancer-progressive pathways, whereas subtype 1 was enriched in biosynthetic pathways. Following, regression and network analyses revealed Epidermal Growth Factor Receptor (EGFR), Fos-related antigen 1 (FOSL1), Nuclear Factor Erythroid 2 (NFE2), ADP-ribosylation factor-like protein 4D (ARL4D), SH3 domain containing ring finger 2 (SH3RF2), and Cadherin 3 (CDH3) genes to be the most significant prognostic gene markers. These genes were used to construct a risk model that separated the BLCA patients into high and low-risk groups. The risk model was also validated in an external dataset by performing survival analysis between high and low-risk groups with a p-value < 0.001 and the result showed the high group was significantly associated with poor prognosis compared to the low group. Single-cell analyses revealed the elevated level of these genes in the tumor microenvironment and associated with immune response. High-grade patients also tend to have a high expression of these genes compared to low-grade patients. In conclusion, this research developed a six-gene signature that is pertinent to the prediction of overall survival (OS) and might contribute to the advancement of precision medicine in the management of bladder cancer.

Implications of single-cell immune landscape of tumor microenvironment for the colorectal cancer diagnostics and therapy

Colorectal cancer (CRC) originates from the polyps lining the colon and is among the most common types of cancer. With the increasing popularity of single-cell sequencing technologies, researchers have been able to better understand the immune landscape of colorectal cancer, by analyzing their expression and interactions in detail with the tumor microenvironment (TME) at single-cell level. Since the tumor-immune cell interactions play a critical part in the advancement as well as treatment response in colorectal cancer, the release of inhibitory factors such as T cells are important for recognizing and destroying cancer cells. Such information is vital to identify immunotherapeutic targets for cure and monitoring response to treatments. Therefore, a comprehensive single-cell studies-based overview of key immunogenic agents regulating the TME of CRC is provided in this review. Tumor-associated macrophages can promote tumor growth and resistance to treatment by releasing factors that inhibit the function of other immune cells. Additionally, colorectal cancer cells can express programmed cell death protein 1 and its ligand, which can also inhibit T-cell function. Researchers have found that certain types of immune cells, prominently T cells, natural killer, and dendritic cells, can have a positive impact on the prognosis of colorectal cancer patients. Treatments like immune checkpoint inhibitors and CAR-T therapies that help to release the inhibitory signals from the cancer cells allow the immune cells to function more effectively.

A transcriptome study of p53-pathway related prognostic gene signature set in bladder cancer

p53 pathway is important in tumorigenesis. However, no study has been performed to specifically investigate the role of p53 pathway genes in bladder cancer (BLCA). In this study, transcriptomics data of muscle invasive bladder cancer patients (n = 411) from The Cancer Genome Atlas (TCGA) were investigated. Using the hallmark p53 pathway gene set, the Non-Negative Matrix factorization (NMF) analysis identified two subtypes (C1 and C2). Clinical, survival, and immunological analysis were done to validate distinct characteristics of the subtypes. Pathway enrichment analysis showed the subtype C1 with poor prognosis having enrichment in genes of the immunity related pathways, where C2 subtype with better prognosis being enriched in genes of the steroid synthesis and drug metabolism pathways. A signature gene set consisting of MDGA2, GNLY, GGT2, UGT2B4, DLX1, and DSC1 was created followed by a risk model. Their expressions were analyzed in RNA extracted from the blood and matched tumor tissues of BLCA patients (n = 10). DSC1 had significant difference of expression (p = 0.005) between the blood and tumor tissues in our BLCA samples. Contrary to the usual normal bladder tissue to blood ratio, DLX1 expression was lower (p = 0.02734) in tumor tissues than in blood. Being the first research of p53 pathway related signature gene set in bladder cancer, this study potentially has a substantial impact on the development of biomarkers for BLCA.

Construction of subtype classifiers and validation of a prognostic risk model based on hypoxia-associated lncRNAs for lung adenocarcinoma

BACKGROUND

Studies have shown that long non-coding RNAs (lncRNAs) are found to be hypoxia-regulated lncRNAs in cancer. Lung adenocarcinoma (LUAD) is the leading cause of cancer death worldwide, and despite early surgical removal, has a poor prognosis and a high recurrence rate. Thus, we aimed to identify subtype classifiers and construct a prognostic risk model using hypoxia-associated long noncoding RNAs (hypolncRNAs) for LUAD.

METHODS

Clinical data of LUAD samples with prognosis information obtained from the Gene Expression Omnibus (GEO), acted as validation dataset, and The Cancer Genome Atlas (TCGA) databases, served as training dataset, were used to screen hypolncRNAs in each dataset by univariate Cox regression analysis; the intersection set was used for subsequent analyses. Unsupervised clustering analysis was performed based on the expression of hypolncRNAs using the 'ConsensuClusterPlus' package. The tumor microenvironment (TME) was compared between LUAD subgroups by analyzing the expression of immune cell infiltration, immune components, stromal components, immune checkpoints, and chemokine secretion. To identify robust prognostically associated hypolncRNAs and construct a risk score model, multivariate Cox regression analysis was performed.

RESULTS

A total of 14 hypolncRNAs were identified. Based on the expression of these hypolncRNAs, patients with LUAD were classified into three hypolncRNA-regulated subtypes. The three subtypes differed significantly in immune cell infiltration, stromal score, specific immune checkpoints, and secretion of chemokines and their receptors. The Tumor Immune Dysfunction and Exclusion (TIDE) scores and myeloid-derived suppressor cell (MDSC) scores were also found to differ significantly among the three hypolncRNA-regulated subtypes. Four of the 14 hypolncRNAs were used to construct a signature to distinguish the overall survival (OS) in TCGA dataset (P<0.0001) and GEO dataset (P=0.0032) and sensitivity to targeted drugs in patients at different risks of LUAD.

CONCLUSIONS

We characterized three regulatory subtypes of hypolncRNAs with different TMEs. We developed a signature based on hypolncRNAs, contributing to the development of personalized therapy and representing a new potential therapeutic target for LUAD.

Molecular differences in renal cell carcinoma between males and females

PURPOSE

The disparity in renal cell carcinoma (RCC) risk and treatment outcome between males and females is well documented, but the underlying molecular mechanisms remain poorly elucidated.

METHODS

We performed a narrative review synthesizing contemporary evidence on sex-specific molecular differences in healthy kidney tissue and RCC.

RESULTS

In healthy kidney tissue, gene expression differs significantly between males and females, including autosomal and sex-chromosome-linked genes. The differences are most prominent for sex-chromosome-linked genes and attributable to Escape from X chromosome-linked inactivation and Y chromosome loss. The frequency distribution of RCC histologies varies between the sexes, particularly for papillary, chromophobe, and translocation RCC. In clear-cell and papillary RCC, sex-specific gene expressions are pronounced, and some of these genes are amenable to pharmacotherapy. However, for many, the impact on tumorigenesis remains poorly understood. In clear-cell RCC, molecular subtypes and gene expression pathways have distinct sex-specific trends, which also apply to the expression of genes implicated in tumor progression.

CONCLUSION

Current evidence suggests meaningful genomic differences between male and female RCC, highlighting the need for sex-specific RCC research and personalized sex-specific treatment approaches.

Hepcidin as a prognostic biomarker in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common malignancy of urologic neoplasms. Hepcidin is a pivotal modulator of iron metabolism involved in human cancers; however, the biological significance of hepcidin in ccRCC remains to be fully understood. Therefore, in this study, we evaluated the expression profiles of hepcidin in ccRCC from several public databases and found that hepcidin expression was upregulated in ccRCC, which was further validated in ccRCC cell lines, clinical samples, and tissue microarray (TMA) quantitative real-time PCR and immunohistochemistry. In addition, we found that the expression level of hepcidin was correlated with the age, T stage and pathologic stage of patients. Furthermore, hepcidin promoter methylation was significantly associated with the worse poor clinical parameters of ccRCC patients, and hepcidin was an independent prognostic factor. Mechanistically, enrichment analysis revealed that hepcidin participated in the immune-related and metabolism-related pathways. Hepcidin was positively correlated with not only immune infiltration and immune checkpoints but also tumor mutation burden and cytotoxic T lymphocyte. Finally, we validated the positive correlation of hepcidin with the marker of macrophage (CD68) in the TMA. Our findings provide insights into understanding the function and its underlying mechanism of hepcidin in ccRCC and suggest that hepcidin might serve as a potential predictive biomarker of response to immunotherapy and the prognosis of patients with ccRCC.

Development of a novel hypoxia-immune–related LncRNA risk signature for predicting the prognosis and immunotherapy response of colorectal cancer

Background

Colorectal cancer (CRC) is one of the most common digestive system tumors worldwide. Hypoxia and immunity are closely related in CRC; however, the role of hypoxia-immune–related lncRNAs in CRC prognosis is unknown.

Methods

Data used in the current study were sourced from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) databases. CRC patients were divided into low- and high-hypoxia groups using the single-sample gene set enrichment analysis (ssGSEA) algorithm and into low- and high-immune groups using the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm. Differentially expressed lncRNAs (DElncRNAs) between low- and high-hypoxia groups, low- and high-immune groups, and tumor and control samples were identified using the limma package. Hypoxia-immune–related lncRNAs were obtained by intersecting these DElncRNAs. A hypoxia-immune–related lncRNA risk signature was developed using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. The tumor microenvironments in the low- and high-risk groups were evaluated using ssGSEA, ESTIMATE, and the expression of immune checkpoints. The therapeutic response in the two groups was assessed using TIDE, IPS, and IC50. A ceRNA network based on signature lncRNAs was constructed. Finally, we used RT-qPCR to verify the expression of hypoxia-immune–related lncRNA signatures in normal and cancer tissues.

Results

Using differential expression analysis, and univariate Cox and LASSO regression analyses, ZNF667-AS1, LINC01354, LINC00996, DANCR, CECR7, and LINC01116 were selected to construct a hypoxia-immune–related lncRNA signature. The performance of the risk signature in predicting CRC prognosis was validated in internal and external datasets, as evidenced by receiver operating characteristic curves. In addition, we observed significant differences in the tumor microenvironment and immunotherapy response between low- and high-risk groups and constructed a CECR7–miRNA–mRNA regulatory network in CRC. Furthermore, RT-qPCR results confirmed that the expression patterns of the six lncRNA signatures were consistent with those in TCGA-CRC cohort.

Conclusion

Our study identified six hypoxia-immune–related lncRNAs for predicting CRC survival and sensitivity to immunotherapy. These findings may enrich our understanding of CRC and help improve CRC treatment. However, large-scale long-term follow-up studies are required for verification.

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 Survival Risk and Immune-Related Characteristics of Kidney Renal Clear Cell Carcinoma

Background

Immunity exerts momentous functions in the progression and treatment of kidney renal clear cell carcinoma (KIRC). A better understanding of the relationship between KIRC and immunity may make a great contribution to evaluating the prognosis and immune-related therapeutic response of KIRC.

Methods

A series of information such as RNA sequence, clinical data, and tumor mutation burden (TMB) of KIRC patients were downloaded through The Cancer Genome Atlas (TCGA). Next, combining the survival information and gene expression data of TCGA and Gene Expression Omnibus (GEO), we established an immune gene-related prognosis model (IGRPM) and analyzed it. Then we constructed a nomogram which was convenient for clinicians to judge the prognosis of KIRC. Last but not the least, the expressions of some genes used to construct IGRPM in early KIRC, and adjacent normal tissues were verified through real-time fluorescence quantitative polymerase chain reaction (RT-qPCR). Perl (strawberry-perl-5.30.0.1-64bit), R software (4.0.3), and GraphPad Prism 7 were used to process the relevant data.

Results

The single-sample gene set enrichment analysis (ssGSEA) showed that there were significant differences in StromalScore, ImmuneScore, ESTIMATEScore, TumorPurity, 22 kinds of human immune cells infiltration, and HLA genes expression between high immunity group (Immunity_H) and low immunity group (Immunity_L). The Immunity_H expressed more immune-related genes and enriched more immune-related functions than the Immunity_L. In addition, compared with the low-risk group, the high-risk group had worse survival outcome and higher TMB. Combining IGRPM-based risk characteristic and TMB, we found that low-TMB + low-risk was the most beneficial to the survival outcome of KIRC patients. The risk characteristic based on IGRPM could be used as an independent prognostic factor for KIRC, and the nomogram constructed for evaluating the prognosis of KIRC showed excellent predictive potential. The RT-qPCR results suggested that not all the genes used to construct IGRPM showed differential expression in early KIRC compared with adjacent normal tissues, but all these genes had significant influence on the prognosis of KIRC.

Conclusion

These comprehensive immune assessments and survival predictions, integrating multiple aspects of data and clinical information, can provide additional value to the current Tumor Node Metastasis staging system for risk stratification of KIRC and may facilitate the development of KIRC immunotherapy.

A Whole Exon Screening-Based Score Model Predicts Prognosis and Immune Checkpoint Inhibitor Therapy Effects in Low-Grade Glioma

Objective

This study aims to identify prognostic factors for low-grade glioma (LGG) via different machine learning methods in the whole genome and to predict patient prognoses based on these factors. We verified the results through in vitro experiments to further screen new potential therapeutic targets.

Method

A total of 940 glioma patients from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) were included in this study. Two different feature extraction algorithms – LASSO and Random Forest (RF) – were used to jointly screen genes significantly related to the prognosis of patients. The risk signature was constructed based on these screening genes, and the K-M curve and ROC curve evaluated it. Furthermore, we discussed the differences between the high- and low-risk groups distinguished by the signature in detail, including differential gene expression (DEG), single-nucleotide polymorphism (SNP), copy number variation (CNV), immune infiltration, and immune checkpoint. Finally, we identified the function of a novel molecule, METTL7B, which was highly correlated with PD-L1 expression on tumor cell, as verified by in vitro experiments.

Results

We constructed an accurate prediction model based on seven genes (AUC at 1, 3, 5 years= 0.91, 0.85, 0.74). Further analysis showed that extracellular matrix remodeling and cytokine and chemokine release were activated in the high-risk group. The proportion of multiple immune cell infiltration was upregulated, especially macrophages, accompanied by the high expression of most immune checkpoints. According to the in vitro experiment, we preliminarily speculate that METTL7B affects the stability of PD-L1 mRNA by participating in the modification of m6A.

Conclusion

The seven gene signatures we constructed can predict the prognosis of patients and identify the potential benefits of immune checkpoint inhibitors (ICI) therapy for LGG. More importantly, METTL7B, one of the risk genes, is a crucial molecule that regulates PD-L1 and could be used as a new potential therapeutic target.

Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma

Purpose

The present study aimed to establish a hypoxia related genes model to predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data accessed from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database.

Methods

Patients’ data were downloaded from the TCGA and ICGC databases, and hypoxia related genes were accessed from the Molecular Signatures Database. The differentially expressed genes were evaluated and then the differential expressions hypoxia genes were screened. The TCGA cohort was randomly divided into a discovery TCGA cohort and a validation TCGA cohort. The discovery TCGA cohort was used for constructing the hypoxia genes risk model through Lasso regression, univariate and multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were used to assess the reliability and sensitivity of our model. Then, we established a nomogram to predict the probable one-, three-, and five-year overall survival rates. Lastly, the Tumor Immune Dysfunction and Exclusion (TIDE) score of patients was calculated.

Results

We established a six hypoxia-related gene prognostic model of KIRC patients in the TCGA database and validated in the ICGC database. The patients with high riskscore present poorer prognosis than those with low riskscore in the three TCGA cohorts and ICGC cohort. ROC curves show our six-gene model with a robust predictive capability in these four cohorts. In addition, we constructed a nomogram for KIRC patients in the TCGA database. Finally, the high risk-group had a high TIDE score than the patients with low riskscore.

Conclusions

We established a six hypoxia-related gene risk model for independent prediction of the prognosis of KIRC patients was established and constructed a robust nomogram. The different riskscores might be a biomarker for immunotherapy strategy.

Distinct Hypoxia-Related Gene Profiling Characterizes Clinicopathological Features and Immune Status of Mismatch Repair-Deficient Colon Cancer

Despite dramatic responses to immune checkpoint inhibitors (ICIs) in patients with colon cancer (CC) harboring deficient mismatch repair (dMMR), more than half of these patients ultimately progress and experience primary or secondary drug resistance. There is no useful biomarker that is currently validated to accurately predict this resistance or stratify patients who may benefit from ICI-based immunotherapy. As hypoxic and acidic tumor microenvironment would greatly impair tumor-suppressing functions of tumor-infiltrating lymphocytes (TILs), we sought to explore distinct immunological phenotypes by analysis of the intratumoral hypoxia state using a well-established gene signature. Based on the Gene Expression Omnibus (GEO) (n = 88) and The Cancer Genome Atlas (TCGA) (n = 49) databases of patients with CC, we found that dMMR CC patients could be separated into normoxia subgroup (NS) and hypoxia subgroup (HS) with different levels of expression of hypoxia-related genes (lower in NS group and higher in HS group) using NMF package. Tumoral parenchyma in the HS group had a relatively lower level of immune cell infiltration, particularly CD8+ T cells and M1 macrophages than the NS group, and coincided with higher expression of immune checkpoint molecules and C-X-C motif chemokines, which might be associated with ICI resistance and prognosis. Furthermore, three genes, namely, MT1E, MT2A, and MAFF, were identified to be differentially expressed between NS and HS groups in both GEO and TCGA cohorts. Based on these genes, a prognostic model with stable and valuable predicting ability has been built for clinical application. In conclusion, the varying tumor-immune microenvironment (TIME) classified by hypoxia-related genes might be closely associated with different therapeutic responses of ICIs and prognosis of dMMR CC patients.

Identification of a Hypoxia-Related Molecular Classification and Hypoxic Tumor Microenvironment Signature for Predicting the Prognosis of Patients with Triple-Negative Breast Cancer

Purpose

The hypoxic tumor microenvironment was reported to be involved in different tumorigenesis mechanisms of triple-negative breast cancer (TNBC), such as invasion, immune evasion, chemoresistance, and metastasis. However, a systematic analysis of the prognostic prediction models based on multiple hypoxia-related genes (HRGs) has not been established in TNBC before in the literature. We aimed to develop and verify a hypoxia gene signature for prognostic prediction in TNBC patients.

Methods

The RNA sequencing profiles and clinical data of TNBC patients were generated from the TCGA, GSE103091, and METABRIC databases. The TNBC-specific differential HRGs (dHRGs) were obtained from differential expression analysis of hypoxia cultured TNBC cell lines compared with normoxic cell lines from the GEO database. Non-negative matrix factorization (NMF) method was then performed on the TNBC patients using the dHRGs to explore a novel molecular classification on the basis of the dHRG expression patterns. Prognosis-associated dHRGs were identified by univariate and multivariate Cox regression analysis to establish the prognostic risk score model.

Results

Based on the expressions of 205 dHRGs, all the patients in the TCGA training cohort were categorized into two subgroups, and the patients in Cluster 1 demonstrated worse OS than those in Cluster 2, which was validated in two independent cohorts. Additionally, the effects of somatic copy number variation (SCNV), somatic single nucleotide variation (SSNV), and methylation level on the expressions of dHRGs were also analyzed. Then, we performed Cox regression analyses to construct an HRG-based risk score model (3-gene dHRG signature), which could reliably discriminate the overall survival (OS) of high-risk and low-risk patients in TCGA, GSE103091, METABRIC, and BMCHH (qRT-PCR) cohorts.

Conclusions

In this study, a robust predictive signature was developed for patients with TNBC, indicating that the 3-gene dHRG model might serve as a potential prognostic biomarker for TNBC.