Data Mining of Prognostic Microenvironment-Related Genes in Clear Cell Renal Cell Carcinoma: A Study with TCGA Database

Integrative analysis of LAG3 immune signature and identification of a LAG3-related genes prognostic signature in kidney renal clear cell carcinoma

Immune checkpoint blockade (ICB) therapy has resulted in improved overall survival in kidney renal clear cell carcinoma (KIRC), but most treated patients fail to show durable clinical responses. Lymphocyte activation gene-3 (LAG3) is a novel inhibitory immune checkpoint, but its expression pattern, prognostic value, and immunological role in KIRC remain unknown. In this study, we utilized TCGA_KIRC RNA-sequencing data to analyze the relationship between LAG3 expression and clinical features. The single-cell sequencing data and tissue immunofluorescence are employed to investigate the subcellular localization of LAG3 in KIRC. Kaplan-Meier plotter, TIMER, and TISIDB were used to assess the association between LAG3 expression and prognosis, as well as its correlation with immune-related components. We constructed the LAG3 interaction network by using STRING, GeneMANIA, BioGRID, and HitPredict databases. We found that LAG3 is upregulated and correlates with poor prognostic phenotype in KIRC. LAG3 is predominantly expressed on exhausted CD8+ T cells and shows strong co-expression with PDCD1 in KIRC. Moreover, our findings indicated that LAG3 not only inhibits T cell activation but also potentially regulates cell adhesion in KIRC. In conclusion, our study implies that LAG3 can serve as a potential prognostic biomarker for KIRC. Furthermore, blocking both LAG3 and PDCD1 may alleviate resistance to anti-PDCD1 therapy, providing novel insights for immunotherapy decision-making in KIRC patients.

The intrarenal landscape of T cell receptor repertoire in clear cell renal cell cancer

BACKGROUND

Clear cell renal cell cancer (ccRCC) is accompanied by T-cell infiltration. In this study, we sought to determine the difference in T-cell infiltration and the T-cell receptor (TCR) immune repertoire between ccRCC and peritumour tissue.

METHODS

T-cell infiltration was examined using immunohistochemistry (IHC) and haematoxylin and eosin (HE) staining. The chi-squared test and Pearson correlation analysis were applied to evaluate the relationship between clinical traits and CD3, CD4, and CD8 expression. Immune repertoire sequencing (IR-Seq) was used to describe the profile of the TCR repertoire.

RESULTS

The adjacent tissue showed increased expression of CD3, CD4 and CD8 compared with ccRCC tissue (P_CD3 = 0.033; P_CD4 = 0.014; P_CD8 = 0.004). Indicated CD3⁺ T-cell density in ccRCC tissue was positively correlated with that in peritumour tissue (P = 0.010, r = 0.514), which implied the T cells in peritumour tissue directly infect the number of cells infiltrating in ccRCC tissue. Moreover, there was a positive correlation between Vimentin expression and indicated positive T-cell marker in ccRCC tissue (P_CD3 = 0.035; P_CD4 = 0.020; P_CD8 = 0.027). Advanced stage revealed less CD4⁺ T-cell infiltration in ccRCC tissue (P_CD4 = 0.023). The results from IR-Seq revealed an obvious increase in VJ and VDJ segment usage, as well as higher complementarity-determining region 3 (CDR3) amino acid (aa) clonotypes in ccRCC. The matched antigen recognized by the TCR of ccRCC may be potential targets.

CONCLUSIONS

The current study collectively demonstrates diminished T-cell infiltration and increased CDR3 aa diversity in ccRCC, which may be associated with immunotherapeutic targets for ccRCC patients.

LAG-3 as a Potent Target for Novel Anticancer Therapies of a Wide Range of Tumors

LAG-3 (Lymphocyte activation gene 3) protein is a checkpoint receptor that interacts with LSEC-tin, Galectin-3 and FGL1. This interaction leads to reduced production of IL-2 and IFN-γ. LAG-3 is widely expressed in different tumor types and modulates the tumor microenvironment through immunosuppressive effects. Differential expression in various tumor types influences patient prognosis, which is often associated with coexpression with immune checkpoint inhibitors, such as TIM-3, PD-1 and CTLA-4. Here, we discuss expression profiles in different tumor types. To date, many clinical trials have been conducted using LAG-3 inhibitors, which can be divided into anti-LAG-3 monoclonal antibodies, anti-LAG-3 bispecifics and soluble LAG-3-Ig fusion proteins. LAG-3 inhibitors supress T-cell proliferation and activation by disallowing for the interaction between LAG-3 to MHC-II. The process enhances anti-tumor immune response. In this paper, we will review the current state of knowledge on the structure, function and expression of LAG-3 in various types of cancer, as well as its correlation with overall prognosis, involvement in cell-based therapies and experimental medicine. We will consider the role of compounds targeting LAG-3 in clinical trials both as monotherapy and in combination, which will provide data relating to the efficacy and safety of proposed drug candidates.

Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma

Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.

Comprehensive Genomic Characterization of Tumor Microenvironment and Relevant Signature in Clear Cell Renal Cell Carcinoma

Purpose

To systematically investigate the characterization of tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC), we performed a comprehensive analysis incorporating genomic alterations, cellular interactions, infiltrating immune cells, and risk signature.

Patients and Methods

Multi-omics data including RNA-seq, single-nucleotide variant (SNV) data, copy number variation (CNV) data, miRNA, and corresponding prognostic data were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database. The CIBERSORT algorithm was utilized to identify prognostic TME subclusters, and TMEscore was further quantified. Moreover, the mutational landscape of TCGA-KIRC was explored. Lastly, TIDE resource was applied to assess the significance of TMEscore in predicting immunotherapeutic benefits.

Results

We analyzed the TME infiltration patterns from 621 ccRCC patients and identified 5 specific TME subclusters associated with clinical outcomes. Then, we found that TMEcluster5 was significantly related to favorable prognosis and enriched memory B-cell infiltration. Accordingly, we depicted the clustering landscape of TMEclusters, TMEscore levels, tumor mutation burden (TMB), tumor grades, purity, and ploidy in all patients. Lastly, TIDE was used to assess the efficiency of immune checkpoint blockers (ICBs) and found that the TMEscore has superior predictive significance to TMB, making it an essential independent prognostic biomarker and drug indicator for clinical use.

Conclusions

Our study depicted the clustering landscape of TMEclusters, TMEscore levels, TMB, tumor grades, purity, and ploidy in total ccRCC patients. The TMEscore was proved to have promising significance for predicting prognosis and ICB responses, in accordance with the goal of developing rationally individualized therapeutic interventions.

Integrated analysis of prognostic immune-related genes in the tumor microenvironment of ovarian cancer

Background

Ovarian cancer (OC) is a major cause of most gynecological cancer deaths, and the rates of incidence and mortality are increasing worldwide. However, factors in the tumor microenvironment (TME) related to OC and certain prognostic markers of OC are still unknown. We aimed to identify biomarkers connected to prognostic immunity based on clinical patients’ data from The Cancer Genome Atlas (TCGA).

Methods

We used the ESTIMATE algorithm to compute the immune and matrix scores of OC patients from TCGA. Next, differentially expressed genes (DEGs) according to the immune and matrix scores were obtained. Subsequently, genes (GZMB, C2orf37, CXCL13, and UBD) connected with prognostic immunity were determined. Moreover, functional enrichment analysis and the protein-protein interaction network showed that these genes were enriched in many biological processes related to immune function. The Tumor Immune Estimation Resource (TIMER) algorithm was also used to analyze the immune prognostic genes according to six immuno-infiltrating cells.

Results

According to high/low immune-scores and matrix-score groups, 682 common genes were identified, within 420 upregulated genes and 262 downregulated genes. Gene ontology (GO) analysis of biological process primarily enriched in T cell activation, regulation of lymphocyte activation and lymphocyte differentiation. OS analysis showed 45 genes (6.6%) were relevant in the final results. The Kaplan-Meier plotter database verified the top 10 genes related to prognosis, but only GZMB, C2orf37, CXCL13 and UBD were related to overall survival (OS).

Conclusions

GZMB, CXCL13, and UBD may influence prognosis via their effects on the infiltration of immune cells and therefore represent potential targets for OC immunotherapy.

Immunological perspective on the malignant progression of renal clear cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common malignancy affecting the kidneys, accounting for approximately 75% of all kidney tumors. Recently, the impact of immune response, immunotherapy, and immune-related genes (IRGs) on tumor development has received much attention. This study sought to establish a reliable immunological signature and further explore whether this signature has prognostic significance in ccRCC patients.

Methods

Differentially expressed IRGs in 611 patients with diagnosis of ccRCC from The Cancer Genome Atlas (TCGA) were analyzed along with the corresponding survival time and disease clinical data. Survival analysis, selection operator Cox analysis, and minimum absolute shrinkage were applied to establish an IRG prognostic signature (IRGPS). The expression levels of relevant genes were detected by real-time quantitative PCR. A Nomogram was used to explore the possible impact of the IRGPS on the immune system, prognosis, and metastasis, and the associated mechanisms were explored through functional enrichment analysis.

Results

An IRGPS was established based on eight prognostic IRGs and was found to be closely associated with immune levels, metastasis, and prognosis. The IRGPS was determined to be a valid predictor of the efficacy of immune checkpoint inhibitors (ICIs). Three Nomograms based on the IRGPS and other clinical features were developed and could effectively predict prognosis, distant metastasis, and lymph node metastasis in patients with ccRCC.

Conclusions

The IRGPS constructed in this study serves as a tool for assessing immune status, developing individualized treatment regimens, and predicting prognosis in patients with ccRCC.

Prediction and prognostic significance of ALOX12B and PACSIN1 expression in gastric cancer by genome-wide RNA expression and methylation analysis

Background

Stomach adenocarcinoma (STAD) is one of the common gastrointestinal cancers, characterized by late discovery and metastasis. However, research of gene methylation and expression in gastric cancer (GC) metastasis has been quite limited. This study aimed to investigate the altered gene expression patterns between metastasis and non-metastasis samples using high-throughput RNA and methylation profiles from a large number of patients. Another aim was to identify a specific potential metastasis biomarker, with the ability to predict the metastasis possibility and prognosis of patients with STAD.

Methods

In this study, we integrated The Cancer Genome Atlas (TCGA) program STAD datasets, analyzed the RNA expression and DNA methylation data between non-metastasis (M0) and distant metastasis (M1) samples, and evaluated the candidate biomarker in survival and prognosis of GC.

Results

Among all patients enrolled, 329 with M0 and M1 information were positive for RNA analysis, and 353 with M0 and M1 information were positive for methylation analysis. We found 29 upregulated and 200 downregulated genes in RNA level, and 5,046 hypermethylated and 8,563 hypomethylated probes in methylation level. Among these genes, we found high RNA expression level and low DNA methylation level of ALOX12B and PACSIN1 in GC metastasis samples. Patients with high expression of these 2 genes had poor overall survival (OS), progression-free survival (PFS), and post-progression survival (PPS).

Conclusions

The expression levels of ALOX12B and PACSIN1 were higher in the metastasis than non-metastasis group, and participants with high expression of these 2 genes were found to have poor survival. The genes ALOX12B and PACSIN1 are potential biomarkers of metastasis and poor prognosis, especially in early stage GC, and provide additional information for subsequent comprehensive treatment of GC.

Gene expression-based immune infiltration analyses of renal cancer and their associations with survival outcome

BACKGROUND

Renal cancer is a common malignant tumor with an increasing incidence rate.

METHODS

In this study, based on the gene expression profiles, we analyzed the compositions of tumor-infiltrating immune cells (TIICs) in renal cancer and paracancerous samples using CIBERSORT. The proportions of 22 TIICs subsets in 122 paired renal carcinoma and paracancerous samples, and 224 Wilms tumor (WT) samples varied between intragroup and intergroup.

RESULTS

After analyzed the difference of TIICs composition between renal cancer and paired paracancerous samples, we found that M0 macrophages and CD8 T cells were significantly elevated, while naive B cells were significantly decreased in renal cancer samples compared with paracancerous samples. Survival analysis showed that high overall TIICs proportion, the low proportion of resting mast cells and the high proportion of activated memory CD4 T cells were associated with poor prognosis of renal cancer patients. In addition, 3 clusters were identified by hierarchical clustering analysis, and they presented a distinct prognosis. Cluster 1 had superior survival outcomes, while cluster 2 had an inferior survival outcome.

CONCLUSIONS

Our study indicated that overall TIICs proportion, certain TIICs subset proportion, including resting mast cells and activated memory CD4 T cells, and distinct cluster patterns were associated with the prognosis of renal cancer, which was significant for the clinical surveillance and treatment of renal cancer.

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

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

Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein‐protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan‐Meier plot demonstrated that low‐immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. ‐, P < .001). A total of 540 genes were screened as DEGs with 539 up‐regulated genes and 1 down‐regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine‐cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4,MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment‐related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.

Identification of P2RY13 as an immune-related prognostic biomarker in lung adenocarcinoma: A public database-based retrospective study

Background

Lung adenocarcinoma (LUAD) is the leading histological subtype of non-small cell lung cancer (NSCLC).

Methods

In the present study, the gene matrixes of LUAD were downloaded from The Cancer Genome Atlas to infer immune and stromal scores with the ‘Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data’ (ESTIMATE) algorithm and identified immune-related differentially expressed genes (DEGs) between the high- and low-stromal/immune score groups. Next, all DEGs were subjected to univariate Cox regression and survival analyses to screen out prognostic biomarkers in the tumor microenvironment (TME), and were validated in the Gene Expression Omnibus database. Single-sample gene set enrichment analysis (ssGSEA) was performed to assess the level of tumor-infiltrating immune cells (TIICs) and immune functions, and GSEA was used to identified pathways altered by prognostic biomarkers.

Results

Survival analysis showed that LUAD in the high-immune and stromal score group had a better clinical prognosis. A total of 303 immune-related DEGs were detected. Univariate Cox regression and survival analyses revealed that P2Y purinoceptor 13 (P2RY13) was a favorable factor for the prognosis of LUAD. ssGSEA and Spearman correlation analysis demonstrated that P2RY13 was highly correlated with various TIICs and immune functions. Several immune-associated pathways were enriched between the high- and low-expression P2RY13 groups.

Conclusion

P2RY13 may be a potential prognostic indicator and is highly associated with the TME in LUAD. However, further experimental studies are required to validate the present findings.

An Exploration of the Tumor Microenvironment Identified a Novel Five-Gene Model for Predicting Outcomes in Bladder Cancer

Bladder cancer (BC) is one of the top ten most common cancer types globally, accounting for approximately 7% of all male malignancies. In the last few decades, cancer research has focused on identifying oncogenes and tumor suppressors. Recent studies have revealed that the interplay between tumor cells and the tumor microenvironment (TME) plays an important role in the initiation and development of cancer. However, the current knowledge regarding its effect on BC is scarce. This study aims to explore how the TME influences the development of BC. We focused on immune and stromal components, which represent the major components of TME. We found that the proportion of immune and stromal components within the TME was associated with the prognosis of BC. Furthermore, based on the scores of immune and stromal components, 811 TME-related differentially expressed genes were identified. Three subclasses with distinct biological features were divided based on these TME-genes. Finally, five prognostic genes were identified and used to develop a prognostic prediction model for BC patients based on TME-related genes. Additionally, we validated the prognostic value of the five-gene model using three independent cohorts. By further analyzing features based on the five-gene signature, higher CD8+ T cells, higher tumor mutational burden, and higher chemosensitivity were found in the low-risk group, which presented a better prognosis. In conclusion, our exploration comprehensively analyzed the TME and identified TME-related prognostic genes for BC, providing new insights into potential therapeutic targets.

Identification and Validation of a Tumor Microenvironment-Related Gene Signature for Prognostic Prediction in Advanced-Stage Non-Small-Cell Lung Cancer

The development of immunotherapy has greatly changed the advanced-stage non-small-cell lung cancer (NSCLC) treatment landscape. The complexity and heterogeneity of tumor microenvironment (TME) lead to discrepant immunotherapy effects among patients at the same pathologic stages. This study is aimed at exploring potential biomarkers of immunotherapy and accurately predicting the prognosis for advanced NSCLC patients. RNA-seq data and clinical information on stage III/IV NSCLC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). In TCGA-NSCLC with stage III/IV (n = 192), immune scores and stromal scores were calculated by using the ESTIMATE algorithms. Univariate, LASSO, and multivariate Cox regression analyses were performed to screen prognostic TME-related genes (TMERGs) and constructed a gene signature risk score model. It was validated in external dataset including GSE41271 (n = 91) and GSE81089 (n = 36). Additionally, a nomogram incorporating TMERG signature risk score and clinical characteristics was established. Further, we accessed the proportion of 22 types of tumor-infiltrating immune cells (TIIC) from the CIBERSORT website and analyzed the difference between two risk groups. OS of patients with high immune/stromal scores were higher (log-rank P = 0.044/log-rank P = 0.048). Multivariate Cox regression identified six prognostic TMERGs, including CD200, CHI3L2, CNTN1, CTSL, FYB1, and SLC52A1. We developed a six-gene risk score model, which was validated as an independent prognostic factor for OS (HR: 3.32, 95% CI: 2.16-5.09). Time-ROC curves showed useful discrimination for TCGA-NSCLC cohort (1-, 2-, and 3-year AUCs were 0.718, 0.761, and 0.750). The predictive robustness was validated in the external dataset. The C-index and 1-, 2-, and 3-year AUCs of nomogram were the largest, which demonstrated the nomogram had the greatest predictive accuracy and effectiveness and could be used for clinical guidance. Besides, the increased infiltration of T cells regulatory (Tregs) and macrophages M2 in the high-risk group suggested that chronic inflammation may reduce survival probability in patients with advanced NSCLC. We conducted a comprehensive analysis of the tumor microenvironment and identified the TMERG signature, which could predict prognosis accurately and provide a reference for the personalized immunotherapy for advanced NSCLC patients.

Identification of FPR3 as a Unique Biomarker for Targeted Therapy in the Immune Microenvironment of Breast Cancer

Although research into immunotherapy is growing, its use in the treatment of breast cancer remains limited. Thus, identification and evaluation of prognostic biomarkers of tissue microenvironments will reveal new immune-based therapeutic strategies for breast cancer. Using an in silico bioinformatic approach, we investigated the tumor microenvironmental and genetic factors related to breast cancer. We calculated the Immune score, Stromal score, Estimate score, Tumor purity, TMB (Tumor mutation burden), and MATH (Mutant-allele tumor heterogeneity) of Breast cancer patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and Maftools. Significant correlations between Immune/Stromal scores with breast cancer subtypes and tumor stages were established. Importantly, we found that the Immune score, but not the Stromal score, was significantly related to the patient's prognosis. Weighted correlation network analysis (WGCNA) identified a pattern of gene function associated with Immune score, and that almost all of these genes (388 genes) are significantly upregulated in the higher Immune score group. Protein-protein interaction (PPI) network analysis revealed the enrichment of immune checkpoint genes, predicting a good prognosis for breast cancer. Among all the upregulated genes, FPR3, a G protein-coupled receptor essential for neutrophil activation, is the sole factor that predicts poor prognosis. Gene set enrichment analysis analysis showed FRP3 upregulation synergizes with the activation of many pathways involved in carcinogenesis. In summary, this study identified FPR3 as a key immune-related biomarker predicting a poor prognosis for breast cancer, revealing it as a promising intervention target for immunotherapy.

IGLL5 is correlated with tumor-infiltrating immune cells in clear cell renal cell carcinoma

Renal cell carcinomas (RCCs) account for about 90% of renal tumors, and their major histological subtype is ccRCC (clear cell RCC). Increasing evidence has indicated that the tumor microenvironment plays a significant role in the occurrence and development of ccRCC. In this study, we used ESTIMATE and CIBERSORT computational methods to calculate the proportion of immune and stromal components and the rate of TICs (tumor‐infiltrating immune cells) in 539 ccRCC samples from The Cancer Genome Atlas database. By examining the intersection of the differentially expressed genes obtained by the protein–protein interaction network and Cox regression analysis, we identified only one overlapping gene: IGLL5 (immunoglobulin lambda‐like polypeptide 5). We report that IGLL5 expression is correlated with TICs. Furthermore, our immunoinfiltration analyses revealed that three types of TIC are positively correlated with IGLL5 expression. IGLL5 may have potential as a prognostic biomarker of ccRCC.

Overexpression of chemokine receptor lymphotactin receptor 1 has prognostic value in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is an aggressive subtype of renal cell carcinoma. X‐C motif chemokine receptor 1 (XCR1) exerts important roles in tumor progression; however, its role in ccRCC is unclear.

Methods

We utilized publicly available data from The Cancer Genome Atlas (TCGA) to assess the role of XCR1 in ccRCC and validated the results in 36 samples from patients with ccRCC who underwent curative resection in Xinqiao Hospital Chongqing. XCR1 overexpression was identified in ccRCC, which was confirmed by qRT‐PCR assay and immunohistochemical staining of ccRCC samples.

Results

For the TCGA and clinical data, Kaplan–Meier survival analysis revealed that higher XCR1 expression in ccRCC was related to longer overall survival. Cox regression analysis suggested that XCR1 is an independent risk factor for ccRCC. GSEA analysis suggested that XCR1 is associated with the JAK/STAT signaling pathway. XCR1 knockdown by small interfering RNA (siRNA) significantly increased ccRCC cell proliferation and migration, and decreased cell apoptosis.

Conclusion

We found higher XCR1 expression in ccRCC compared with that in normal tissues is related to longer overall survival in patients with ccRCC. XCR1 knockdown significantly increased RCC cells proliferation and migration, and decreased apoptosis. XCR1 might be used as a prognostic biomarker in ccRCC in the future.

Construction of a 13-Gene Signature as a Novel Prognostic Marker for Patients with Clear Cell Renal Cell Carcinoma and the Role of XCR1 in Cell Proliferation

Objective

The tumor microenvironment plays a key role in regulating tumor progression. This research aimed to develop the biomarker related to tumor microenvironment in clear cell renal cell carcinoma (ccRCC).

Methods

The ESTIMATE algorithm was used to evaluate the immune score of ccRCC cases from The Cancer Genome Atlas (TCGA). Differentially expressed genes between high and low immune scores were identified and a 13-gene signature was constructed by the LASSO Cox regression model to predict overall survival (OS) for ccRCC cases in TCGA or International Cancer Genome Consortium (ICGC) project. The immune cell fractions were calculated by the TIMER algorithm. Cell viability and gene expression were determined by CCK-8 and qRT-PCR, respectively.

Results

The OS of patients with high immune scores was worse than that of patients with low immune scores. The OS between ccRCC patients from TCGA or ICGC cohort in high- and low-risk groups stratified by the gene signature was significantly different. Subgroup analysis also showed a robust prognostic ability of the gene signature. Multivariate Cox regression analysis demonstrated that this gene signature was an independent prognostic factor. The nomogram that integrated the gene signature and three clinicopathological risk factors had a favorably predictive ability in predicting 3, 5 and 10 year survival. Moreover, the high-risk group had a significantly higher abundance of B cell, T cell, CD4, neutrophil and DC infiltration. Among 13 genes, X-C motif chemokine receptor1 (XCR1) was upregulated in ccRCC cells and exerted an inhibitory effect on cell proliferation.

Conclusion

This study constructs a 13-gene signature as a novel prognostic marker to predict the survival of ccRCC patients and XCR1 may serve as a therapeutic target.

Mining TCGA database for prognostic genes in head and neck squamous cell carcinoma microenvironment

Background/purpose

Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignant tumors. The aim of this study was to elucidate the effect of tumor microenvironment-related genes on the prognosis of HNSCC and to obtain tumor microenvironment-related genes that can predict poor prognosis in HNSCC patients.

Materials and methods

The ESTIMATE algorithm was applied to the HNSCC transcriptomic data downloaded from the TCGA (The cancer genome atlas), and then the samples were divided into two groups: high and low immune scoring groups, and high and low basal scoring groups to screen for differentially expressed genes (DEGs) associated with poor patient outcomes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to explore the potential functions of DEGs, and then to explore the potential prognostic value of individual DEGs. The results of survival analysis between DEGs and overall survival (OS) to explore tumor microenvironment-related genes relevant to the prognosis of HNSCC patients.

Results

Fifty-nine tumor microenvironment-related genes were screened for association of OS with HNSCC (P < 0.05). The GO and KEGG enrichment analysis showed that the selected DEGs may mediate immune response, extracellular matrix, and immunoglobulin binding via neutrophil activation in HNSCC. Six of these DEGs, GIMAP6, SELL, TIFAB, KCNA3, P2RY8 and CCR4 were most significantly associated with OS (P < 0.001).

Conclusion

We identified six tumor microenvironment-related genes that were significantly associated with poor prognosis in HNSCC. These genes may inspire researchers to discover new targets and approaches for HNSCC treatment.

Prognostic value and immune cell infiltration of hypoxic phenotype-related gene signatures in glioblastoma microenvironment

Glioblastoma (GBM) is a malignant intracranial tumour with the highest proportion and lethality. It is characterized by invasiveness and heterogeneity. However, the currently available therapies are not curative. As an essential environmental cue that maintains glioma stem cells, hypoxia is considered the cause of tumour resistance to chemotherapy and radiation. Growing evidence shows that immunotherapy focusing on the tumour microenvironment is an effective treatment for GBM; however, the current clinicopathological features cannot predict the response to immunotherapy and provide accurate guidance for immunotherapy. Based on the ESTIMATE algorithm, GBM cases of The Cancer Genome Atlas (TCGA) data set were classified into high- and low-immune/stromal score groups, and a four-gene tumour environment-related model was constructed. This model exhibited good efficiency at forecasting short- and long-term prognosis and could also act as an independent prognostic biomarker. Additionally, this model and four of its genes (CLECL5A, SERPING1, CHI3L1 and C1R) were found to be associated with immune cell infiltration, and further study demonstrated that these four genes might drive the hypoxic phenotype of perinecrotic GBM, which affects hypoxia-induced glioma stemness. Therefore, these might be important candidates for immunotherapy of GBM and deserve further exploration.

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Ovarian cancer (OC) is the deadliest gynecological cancer in women. Immune cell infiltration has a critical role in regulating carcinogenesis and prognosis in OC. To identify prognostic genes relevant to the tumor microenvironment in OC, we investigated the association between OC and gene expression profiles. Results obtained with the ESTIMATE R tool showed that immune score and stromal score were correlated with lymphatic invasion, and high immune score predicted a favorable prognosis. A total of 342 common differentially expressed genes were identified according to the two scores; these genes were mainly involved in immune response, extracellular region, and serine-type endopeptidase activity. Three immune-related prognostic genes were selected by univariate and multivariate Cox regression analysis. We further established a prognostic model and validated the prognostic value of three hub genes in different databases; our results showed that this model could accurately predict survival and evaluate prognosis independent of clinical characteristics. Three hub genes have prognostic value in OC. TIMER analysis revealed that the three genes were correlated with different immune cells. Low levels of macrophage infiltration and high levels of CD4+ T cell infiltration were associated with favorable survival outcomes. Arm-level gain of GYPC was correlated with neutrophils and dendritic cells. These findings indicate that CXCR4, GYPC, and MMP12 modulate prognosis via effects on the infiltration of immune cells. Thus, these genes represent potential targets for immune therapy in OC.

Comprehensive analysis of prognostic tumor microenvironment-related genes in osteosarcoma patients

Background

Tumor microenvironment (TME) plays an important role in malignant tumors. Our study aimed to investigate the effect of the TME and related genes in osteosarcoma patients.

Methods

Gene expression profiles and clinical data of osteosarcoma patients were downloaded from the TARGET dataset. ESTIMATE algorithm was used to quantify the immune score. Then, the association between immune score and prognosis was studied. Afterward, a differential analysis was performed based on the high- and low-immune scores to determine TME-related genes. Additionally, Cox analyses were performed to construct two prognostic signatures for overall survival (OS) and disease-free survival (DFS), respectively. Two datasets obtained from the GEO database were used to validate signatures.

Results

Eighty-five patients were included in our research. The survival analysis indicated that patients with higher immune score have a favorable OS and DFS. Moreover, 769 genes were determined as TME-related genes. The unsupervised clustering analysis revealed two clusters were significantly related to immune score and T cells CD4 memory fraction. In addition, two signatures were generated based on three and two TME-related genes, respectively. Both two signatures can significantly divide patients into low- and high-risk groups and were validated in two GEO datasets. Afterward, the risk score and metastatic status were identified as independent prognostic factors for both OS and DFS and two nomograms were generated. The C-indexes of OS nomogram and DFS nomogram were 0.791 and 0.711, respectively.

Conclusion

TME was associated with the prognosis of osteosarcoma patients. Prognostic models based on TME-related genes can effectively predict OS and DFS of osteosarcoma patients.

An immune scores-based nomogram for predicting overall survival in patients with clear cell renal cell carcinoma

The role of immune cell infiltration in the prognosis of clear cell renal cell carcinoma (ccRCC) has received increasing attention. However, immune scores have not yet been introduced into routine clinical practice of ccRCC patients. The principal objective of our research was to study the correlation between immune scores and overall survival (OS) of ccRCC.In this study, Cox regression analyses were used to identify risk factors associated with OS of ccRCC based on the Cancer Genome Atlas datasets. Furthermore, an integrated nomogram combining immune scores and clinicopathologic factors was built for predicting 3- and 5-year OS of ccRCC patients. The receiver operating characteristic curve, concordance index, and calibration curves were used for the evaluation of our nomogram. Also, Kaplan-Meier (KM) survival analysis of immune scores, stromal scores, and different clinicopathological factors was performed.A total of 514 patients were divided into the low- or high-immune scores group. KM and multivariate Cox regression analyses demonstrated that ccRCC patients with high-immune scores had significantly poor OS compared with those with low-immune scores. Calibration curves showed good consistency between the predicted OS and the actual OS probability. Areas under the receiver operating characteristic curves for 3- and 5-year OS were 0.816 and 0.769, and the concordance index was 0.775, indicating that our nomogram had good accuracy for predicting OS of ccRCC patients. Additionally, KM analysis showed that older age, later T stage, distant metastasis, advanced tumor lymph node metastasis stage, higher tumor grade, left site, and low stromal scores were associated with worse OS in ccRCC patients.High-immune scores show a significant correlation with unsatisfactory prognosis in ccRCC patients. Furthermore, the immune scores-based nomogram may be helpful in predicting ccRCC prognosis.

Prognostic Value of Immune-Related Genes in the Tumor Microenvironment of Bladder Cancer

The tumor microenvironment (TME) is a complex system that plays an important role in tumor development and progression, but the current knowledge about its effect on bladder cancer (BC) is scarce. In this study, we performed a comprehensive analysis of the relationship between the TME and gene expression profiles to identify prognostic biomarkers for BC. The ESTIMATE algorithm was used to calculate immune and stromal scores of BC patients who were obtained from the Gene Expression Omnibus database. We found that the immune and stromal scores were associated with clinical characteristics and the prognosis of BC patients. Based on these scores, 104 immune-related differentially expressed genes were identified. Further, functional enrichment analysis revealed that these genes were mainly involved in the immune-related biological processes and signaling pathways. Three prognostic genes were then identified and used to establish a risk prediction model using Cox regression analyses. Kaplan–Meier survival analysis showed that the expression levels of COL1A1, COMP, and SERPINE2 significantly correlated with cancer-specific survival and overall survival of BC patients. Additionally, we validated the prognostic values of these genes using two independent cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases. Finally, the relationships between the three prognostic genes and several immune cells were evaluated using Tumor Immune Estimation Resource, indicating that the expression levels of COL1A1, COMP, and SERPINE2 correlated positively with the tumor infiltration levels of CD4⁺ T cells and macrophages. In conclusion, the current study comprehensively analyzed the TME and presented immune-related prognostic genes for BC, providing new insights into immunotherapeutic strategies for BC patients.