The immune infiltration in clear cell Renal Cell Carcinoma and their clinical implications: A study based on TCGA and GEO databases

Exploring necrosis-associated mitochondrial gene signatures: revealing their role in prognosis and immunotherapy of renal clear cell carcinoma

Mitochondrial dysfunction and necrotic apoptosis, pivotal in therapeutic strategies for multiple diseases, lack comprehensive understanding in the context of renal clear cell carcinoma (ccRCC). This study explores their potential as valuable tools for ccRCC prediction, prevention, and personalized medical care. Transcriptomic and clinical datasets were acquired from the Cancer Genome Atlas (TCGA) repository. Mitochondrial and necrosis-associated gene sets were sourced from MitoCarta3.0 and the KEGG Pathway databases, respectively. Six necrosis-related mitochondrial genes (nc-MTGs) with prognostic significance were analyzed and screened, and a prognostic model was constructed. The accuracy of the model was verified using external data (E-MTAB-1980). TISCH was used to explore nc-MTGs at the cellular level. Finally, the expression level of BH3 interacting domain death agonist (BID) in ccRCC cell line was detected by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR), and the effect of BID down-regulation on tumor cell migration was verified by transwell assays and wound-healing experiments. We established and validated a prognostic model for clear cell renal cell carcinoma (ccRCC) utilizing six necrosis-related mitochondrial genes (nc-MTGs), affirming its efficacy in evaluating tumor progression. RT-PCR results showed that BID expression was up-regulated in ccRCC tissues compared with controls and exhibited oncogenic effects. In vitro cell function experiments showed that BID may be an important factor affecting the migration of ccRCC. Our study is the first to elucidate the biological functions and prognostic significance of mitochondrial molecules related to necroptosis, providing a new way to evaluate mitochondrial therapeutics in patients with ccRCC.

The Cellular Stress and Cutaneous Manifestations in Renal Cell Carcinomas-A Narrative Review

The carcinomas originating from the renal cortex are the most aggressive renal malignancies, with a high tendency for metastasis. Understanding the incidence of cutaneous manifestations caused by renal carcinomas is a challenge. In the first part, this article summarizes a series of factors that promote oncogenesis, invasiveness, and the ability of renal cell carcinoma (RCC) to develop secondary cutaneous manifestations. It is postulated that the cellular stress response is one of the leading causes of developing dermatological events induced by cancers located at distant sites. Furthermore, the paper provides an overview of cutaneous complications associated with renal cancer, categorized as malignant manifestations (metastases, synchronous or metachronous cutaneous malignancies associated with renal cancer), non-malignant indirect cutaneous manifestations associated with renal cancer, and treatment consequences. The data presented in this article suggest that recognizing certain cutaneous disorders could assist the physician in the early identification of renal neoplasms and could lead to a better prognosis.

Targeting CXCR4 impaired T regulatory function through PTEN in renal cancer patients

BACKGROUND

Tregs trafficking is controlled by CXCR4. In Renal Cell Carcinoma (RCC), the effect of the new CXCR4 antagonist, R54, was explored in peripheral blood (PB)-Tregs isolated from primary RCC patients.

METHODS

PB-Tregs were isolated from 77 RCC patients and 38 healthy donors (HDs). CFSE-T effector-Tregs suppression assay, IL-35, IFN-γ, IL-10, TGF-β1 secretion, and Nrp-1+Tregs frequency were evaluated. Tregs were characterised for CTLA-4, PD-1, CD40L, PTEN, CD25, TGF-β1, FOXP3, DNMT1 transcriptional profile. PTEN-pAKT signalling was evaluated in the presence of R54 and/or triciribine (TCB), an AKT inhibitor. Methylation of TSDR (Treg-Specific-Demethylated-Region) was conducted.

RESULTS

R54 impaired PB-RCC-Tregs function, reduced Nrp-1+Tregs frequency, the release of IL-35, IL-10, and TGF-β1, while increased IFN-γ Teff-secretion. The CXCR4 ligand, CXCL12, recruited CD25+PTEN+Tregs in RCC while R54 significantly reduced it. IL-2/PMA activates Tregs reducing pAKT+Tregs while R54 increases it. The AKT inhibitor, TCB, prevented the increase in pAKT+Tregs R54-mediated. Moreover, R54 significantly reduced FOXP3-TSDR demethylation with DNMT1 and FOXP3 downregulation.

CONCLUSION

R54 impairs Tregs function in primary RCC patients targeting PTEN/PI3K/AKT pathway, reducing TSDR demethylation and FOXP3 and DNMT1 expression. Thus, CXCR4 targeting is a strategy to inhibit Tregs activity in the RCC tumour microenvironment.

PTK6: An emerging biomarker for prognosis and immunotherapeutic response in clear cell renal carcinoma (KIRC)

Kidney renal clear cell carcinoma (KIRC), one of the most prevalent form of kidney carcinoma, is highly aggressive cancer known for significant immune infiltration and high mortality rates. The absence of sensitivity to traditional therapy has spurred the search for new treatments. Protein Tyrosine Kinase 6 (PTK6) is implicated in promoting cancer growth, spread, and metastasis. Our review of The Cancer Genome Atlas database revealed PTK6 overexpression in KIRC, though its specific role in this cancer type was unclear. We investigated PTK6's cancer-promoting roles in KIRC using the database and confirmed our findings with patient-derived tissues. Our analysis showed that elevated PTK6 expression is linked to worse outcomes and higher levels of immune infiltration. It also correlates positively with neo-antigens (NEO) and DNA ploidy changes in KIRC. This research delves into PTK6's role in KIRC development, suggesting PTK6 as a possible biomarker for prognosis and treatment in KIRC.

The Expression and Prognostic Value of Co-stimulatory Molecules in Clear Cell Renal Cell Carcinoma (CcRcc)

BACKGROUND

Renal cell carcinoma (RCC) was one of the most common malignant cancers in the urinary system. Clear cell carcinoma (ccRCC) is the most common pathological type, accounting for approximately 80% of RCC. The lack of accurate and effective prognosis prediction methods has been a weak link in ccRCC treatment. Co-stimulatory molecules played the main role in increasing anti-tumor immune response, which determined the prognosis of patients. Therefore, the main objective of the present study was to explore the prognostic value of co-stimulatory molecules genes in ccRCC patients.

METHODS

The TCGA database was used to get gene expression and clinical characteristics of patients with ccRCC. A total of 60 co-stimulatory molecule genes were also obtained from TCGAccRCC, including 13 genes of the B7/ CD28 co-stimulatory molecules family and 47 genes of the TNF family. In the TCGA cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to generate a multigene signature. R and Perl programming languages were used for data processing and drawing. Real-time PCR was used to verify the expression of differentially expressed genes.

RESULTS

The study's initial dataset included 539 ccRCC samples and 72 normal samples. The 13 samples have been eliminated. According to FDR<0.05, there were differences in the expression of 55 co-stimulatory molecule genes in ccRCC and normal tissues. LASSO Cox regression analysis results indicated that 13 risk genes were optimally used to construct a prognostic model of ccRCC. The patients were divided into a high-risk group and a low-risk group. Those in the high-risk group had significantly lower OS (Overall Survival rate) than patients in the low-risk group. Receiver operating characteristic (ROC) curve analysis confirmed the predictive value of the prognosis model of ccRCC (AUC>0.7). There are substantial differences in immune cell infiltration between high and low-risk groups. Functional analysis revealed that immune-related pathways were enriched, and immune status was different between the two risk groups. Real-time PCR results for genes were consistent with TCGA DEGs.

CONCLUSION

By stratifying patients with all independent risk factors, the prognostic score model developed in this study may improve the accuracy of prognosis prediction for patients with ccRCC.

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model

OBJECTIVES

Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC).

METHODS

In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression.

RESULTS

A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group.

CONCLUSION

The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

P2RY13 is a prognostic biomarker and associated with immune infiltrates in renal clear cell carcinoma: A comprehensive bioinformatic study

Background and Aims

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of cancer with a high incidence globally. This study aimed to investigate the role of P2RY13 in the progression of ccRCC and elucidate its mechanism of action.

Methods

Gene Expression Omnibus and The Cancer Genome Atlas databases were used to extract gene expression profiles of ccRCC. These profiles were annotated and visualized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, as well as Gene Set Enrichment Analysis (GSEA). The STRING database was used to establish a protein-protein interaction network and to analyze the functional similarity. The GEPIA2 database was used to predict survival associated with hub genes. Meanwhile, the TIMER2.0 database was used to assess immune cell infiltration and its link with the hub genes. Immunohistochemistry (IHC) was used to determine the difference between ccRCC and adjacent normal tissue.

Results

We identified 272 differentially expressed genes (DEGs). GO and KEGG analyses suggested that DEGs were primarily involved in lymphocyte activation, inflammatory response, immunological effector mechanism pathways. By cytohubba, the 20 highest-scoring hub genes were screened to identify critical genes in the protein-protein interaction network linked with ccRCC. Resting dendritic cells, CD8 T cells, and activated mast cells all showed a significant positive correlation with these hub genes. Moreover, a higher immune score was associated with increased prognostic risk scores, which in turn correlated with a poorer prognosis. IHC revealed that P2RY13 was expressed at higher levels in ccRCC compared to para-cancer tissues.

Conclusion

Identifying the DEGs will aid in the understanding of the causes and molecular mechanisms involved in ccRCC. P2RY13 may play a pivotal role in the progression and prognosis of ccRCC, potentially driving carcinogenesis though immune system mechanisms.

Construction of a prognostic risk score model based on the ARHGAP family to predict the survival of osteosarcoma

BACKGROUND

Osteosarcoma (OS) is the most common primary malignancy of bone tumors. More and more ARHGAP family genes have been confirmed are to the occurrence, development, and invasion of tumors. However, its significance in osteosarcoma remains unclear. In this study, we aimed to identify the relationship between ARHGAP family genes and prognosis in patients with OS.

METHODS

OS samples were retrieved from the TCGA and GEO databases. We then performed LASSO regression analysis and multivariate COX regression analysis to select ARHGAP family genes to construct a risk prognosis model. We then validated this prognostic model. We utilized ESTIMATE and CIBERSORT algorithms to calculate the stroma and immune scores of samples, as well as the proportions of tumor infiltrating immune cells (TICs). Finally, we conducted in vivo and in vitro experiments to investigate the effect of ARHGAP28 on osteosarcoma.

RESULTS

We selected five genes to construct a risk prognosis model. Patients were divided into high- and low-risk groups and the survival time of the high-risk group was lower than that of the low-risk group. The high-risk group in the prognosis model constructed had relatively poor immune function. GSEA and ssGSEA showed that the low-risk group had abundant immune pathway infiltration. The overexpression of ARHGAP28 can inhibit the proliferation, migration, and invasion of osteosarcoma cells and tumor growth in mice, and IHC showed that overexpression of ARHGAP28 could inhibit the proliferation of tumor cells.

CONCLUSION

We constructed a risk prognostic model based on five ARHGAP family genes, which can predict the overall survival of patients with osteosarcoma, to better assist us in clinical decision-making and individualized treatment.

HAGLR, A Long Non-coding RNA of Potential Tumor Suppressive Function in Clear Cell Renal Cell Carcinoma: Diagnostic and Prognostic Implications

Research works suggested the role of long non-coding RNAs (lncRNAs) in pathogenesis of clear cell renal cell carcinoma (ccRCC). lncRNA HAGLR is studied in several malignancies, but not in ccRCC. From The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset, we analyzed molecular alterations of HAGLR and constructed a competitive endogenous RNA (ceRNA) network with related miRNAs and mRNAs. Gene Ontology analysis was done to identify important pathways enriched with HAGLR recovered mRNAs. Clinical importance of HAGLR and related mRNAs was assessed and, the impact of selected mRNA-encoding genes on tumor immune infiltration was studied using TIMER. HAGLR expression was reduced in ccRCC than in normal kidneys, and correlated significantly with gene promoter methylation. Low HAGLR level in tumors showed diagnostic potency, and was associated with clinicopathological parameters (stage/grade/metastasis) and poor patient survival. The HAGLR-associated ceRNA network constituted 13 miRNAs and 23 mRNAs differentially expressed in the TCGA-KIRC dataset. From HAGLR recovered mRNA-encoding genes, we developed a 5-gene (PAQR5, ARHGAP24, HABP4, PDLIM5, and RPS6KA2) prognostic signature in the training dataset and validated it in testing as well as entire datasets. The expression level of signature genes showed negative correlation with tumor infiltration of immune cells having adverse impact on ccRCC prognosis and also with tumor derived chemokines facilitating the infiltration. In conclusion, HAGLR seemed to play a tumor suppressive role in ccRCC. HAGLR and associated gene signature may have implementation in improving existing prognostic measure and developing effective immunotherapeutic strategies for ccRCC.

Development and validation of a combined hypoxia- and metabolism-related prognostic signature to predict clinical prognosis and immunotherapy responses in clear cell renal cell carcinoma

Background

Hypoxia and metabolism are closely correlated with the progression of cancer. We aimed to construct a combined hypoxia- and metabolism-related genes (HMRGs) prognostic signature to predict survival and immunotherapy responses in patients with clear cell renal cell carcinoma (ccRCC).

Methods

The RNA-seq profiles and clinical data of ccRCC were acquired from the TCGA and the ArrayExpress (E-MTAB-1980) databases. Least absolute shrinkage and selection operator (LASSO) and univariate and multivariate Cox regression analyses were applied to establish a prognostic signature. The E-MTAB-1980 cohort was selected for validation. The effectiveness and reliability of the signature were further evaluated by Kaplan-Meier (K-M) survival and time-dependent receiver operating characteristic (ROC) curves. Further analyses, including functional enrichment, ssGSEA algorithm, CIBERSORT algorithm, and expression of immune checkpoints, were explored to investigate immune status and immunotherapy responses.

Results

We constructed a prognostic eight-gene signature with IRF6, TEK, PLCB2, ABCB1, TGFA, COL4A5, PLOD2, and TUBB6. Patients were divided into high-risk and low-risk groups based on the medium-risk score. The K-M analysis revealed that patients in the high-risk group had an apparently poor prognosis compared to those in the low-risk group in the TCGA (p < 0.001) and E-MTAB-1980 (p < 0.005). The area under ROC curve (AUC) of the prognostic signature was 0.8 at 1 year, 0.77 at 3 years, and 0.78 at 5 years in the TCGA, respectively, and was 0.82 at 1 year, 0.74 at 3 years, and 0.75 at 5 years in the E-MTAB-1980, respectively. Independent prognostic analysis confirmed the risk score as a separate prognostic factor in ccRCC patients (p < 0.001). The results of ssGSEA showed not only a high degree of immune cell infiltration but also high scores of immune-related functions in the high-risk group. The CIBERSORT analysis further confirmed that the abundance of immune cells was apparently different between the two risk groups. The risk score was significantly correlated with the expression of cytotoxic T lymphocyte-associated antigen-4 (CTLA4), lymphocyte-activation gene 3 (LAG3), and programmed cell death protein 1 (PD-1).

Conclusion

The HMRGs signature could be used to predict clinical prognosis, evaluate the efficacy of immunotherapy, and guide personalized immunotherapy in ccRCC patients.

Unraveling the prognostic significance and molecular characteristics of tumor-infiltrating B lymphocytes in clear cell renal cell carcinoma through a comprehensive bioinformatics analysis

Introduction

Clear cell renal cell carcinoma (ccRCC) is a prevalent subtype of kidney cancer that exhibits a complex tumor microenvironment, which significantly influences tumor progression and immunotherapy response. In recent years, emerging evidence has underscored the involvement of tumor-infiltrating B lymphocytes (TIL-Bs), a crucial component of adaptive immunity, and their roles in ccRCC as compared to other tumors. Therefore, the present study endeavors to systematically explore the prognostic and molecular features of TIL-Bs in ccRCC.

Methods

Initially, xCell algorithm was used to predict TIL-Bs in TCGA-KIRC and other ccRCC transcriptomic datasets. The Log-Rank test and Cox regression were applied to explore the relationship of B-cells with ccRCC survival. Then, we used WGCNA method to identify important modules related to TIL-Bs combining Consensus subcluster and scRNA-seq data analysis. To narrow down the prospective biomarkers, a prognostic signature was proposed. Next, we explored the feature of the signature individual genes and the risk-score. Finally, the potential associations of signature with clinical phenotypes and drugs were investigated.

Results

Preliminary, we found ccRCC survival was negatively associated with TIL-Bs, which was confirmed by other datasets. Afterwards, ten co-expression modules were identified and a distinct ccRCC cluster was subsequently detected. Moreover, we assessed the transcriptomic alteration of B-cell in ccRCC and a relevant B-cell subtype was also pinpointed. Based on two core modules (brown, red), a 10-gene signature (TNFSF13B, SHARPIN, B3GAT3, IL2RG, TBC1D10C, STAC3, MICB, LAG3, SMIM29, CTLA4) was developed in train set and validated in test sets. These biomarkers were further investigated with regards to their differential expression and correlation with immune characteristics, along with risk-score related mutations and pathways. Lastly, we established a nomogram combined tumor grade and discovered underlying drugs according to their sensitivity response.

Discussion

In our research, we elucidated the remarkable association between ccRCC and B-cells. Then, we detected several key gene modules, together with close patient subcluster and B-cell subtype,which could be responsible for the TIL-Bs in ccRCC. Moreover, we proposed a 10-gene signature and investigated its molecular features from multiple perspectives. Overall, understanding the roles of TIL-Bs could aid in the immunotherapeutic approaches for ccRCC, which deserve further research to clarify the implications for patient prognosis and treatment.

Endogenous retrovirus group FRD member 1 is a potential biomarker for prognosis and immunotherapy for kidney renal clear cell carcinoma

Introduction

The activation of endogenous retroviral (ERV) genes in kidney renal clear cell carcinoma (KIRC) suggests the necessity for further research on their functions.

Methods

In this study, KIRC and healthy cohorts were obtained from TGGA and GEO datasets. Subsequently, differential analysis and functional annotation were conducted using GO, KEGG, and GSEA. Clinical outcomes were then observed and utilized in the development of a nomogram.

Results

We observed the general low expression of ERVFRD-1 in KIRC tumors compared to normal tissue (P < 0.001) across multiple cohorts. Differential analysis and functional annotation using GO, KEGG, GSEA analysis revealed significant involvement of ERVFRD-1 in tumor immunoregulation: a close relation to the infiltration levels of mast cells and Treg cell (P < 0.001) and occurrence with a variety of immune markers. Methylation status was then applied to uncover potential mechanisms of ERVFRD-1 in KIRC. Notably, higher expression levels of ERVFRD-1 were associated with extended overall survival, disease-specific survival, and progression-free survival. Finally, based on Cox regression analysis, we constructed a nomogram incorporating ERVFRD-1, pathologic T, and age, which exhibited promising predictive power in assessing the survival outcomes of KIRC patients.

Discussion

To sum up, our study suggests that ERVFRD-1 plays a role in regulating immunological activity within the tumor microenvironment and is associated with overall survival in KIRC patients. ERVFRD-1 may therefore be a sensitive biomarker for diagnosis, immunotherapy, and prognosis assessment of KIRC.

Immune-related risk prognostic model for clear cell renal cell carcinoma: Implications for immunotherapy

Clear cell renal cell carcinoma (ccRCC) is associated with complex immune interactions. We conducted a comprehensive analysis of immune-related differentially expressed genes in patients with ccRCC using data from The Cancer Genome Atlas and ImmPort databases. The immune-related differentially expressed genes underwent functional and pathway enrichment analysis, followed by COX regression combined with LASSO regression to construct an immune-related risk prognostic model. The model comprised 4 IRGs: CLDN4, SEMA3G, CAT, and UCN. Patients were stratified into high-risk and low-risk groups based on the median risk score, and the overall survival rate of the high-risk group was significantly lower than that of the low-risk group, confirming the reliability of the model from various perspectives. Further comparison of immune infiltration, tumor mutation load, and immunophenoscore (IPS) comparison between the 2 groups indicates that the high-risk group could potentially demonstrate a heightened sensitivity towards immunotherapy checkpoints PD-1, CTLA-4, IL-6, and LAG3 in ccRCC patients. The proposed model not only applies to ccRCC but also shows potential in developing into a prognostic model for renal cancer, thus introducing a novel approach for personalized immunotherapy in ccRCC.

A novel oxidative stress-related genes signature associated with clinical prognosis and immunotherapy responses in clear cell renal cell carcinoma

Background

Oxidative stress plays a significant role in the tumorigenesis and progression of tumors. We aimed to develop a prognostic signature using oxidative stress-related genes (ORGs) to predict clinical outcome and provide light on the immunotherapy responses of clear cell renal cell carcinoma (ccRCC).

Methods

The information of ccRCC patients were collected from the TCGA and the E-MTAB-1980 datasets. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) were conducted to screen out overall survival (OS)-related genes. Then, an ORGs risk signature was built by multivariate Cox regression analyses. The performance of the risk signature was evaluated with Kaplan-Meier (K-M) survival. The ssGSEA and CIBERSORT algorithms were performed to evaluate immune infiltration status. Finally, immunotherapy responses was analyzed based on expression of several immune checkpoints.

Results

A prognostic 9-gene signature with ABCB1, AGER, E2F1, FOXM1, HADH, ISG15, KCNMA1, PLG, and TEK. The patients in the high risk group had apparently poor survival (TCGA: p < 0.001; E-MTAB-1980: p < 0.001). The AUC of the signature was 0.81 at 1 year, 0.76 at 3 years, and 0.78 at 5 years in the TCGA, respectively, and was 0.8 at 1 year, 0.82 at 3 years, and 0.83 at 5 years in the E-MTAB-1980, respectively. Independent prognostic analysis proved the stable clinical prognostic value of the signature (TCGA cohort: HR = 1.188, 95% CI =1.142-1.236, p < 0.001; E-MTAB-1980 cohort: HR =1.877, 95% CI= 1.377-2.588, p < 0.001). Clinical features correlation analysis proved that patients in the high risk group were more likely to have a larger range of clinical tumor progression. The ssGSEA and CIBERSORT analysis indicated that immune infiltration status were significantly different between two risk groups. Finally, we found that patients in the high risk group tended to respond more actively to immunotherapy.

Conclusion

We developed a robust prognostic signature based on ORGs, which may contribute to predict survival and guide personalize immunotherapy of individuals with ccRCC.

Prognostic analysis of uveal melanoma based on the characteristic genes of M2-type macrophages in the tumor microenvironment

Uveal melanoma arises from stromal melanocytes and is the most prevalent primary intraocular tumor in adults. It poses a significant diagnostic and therapeutic challenge due to its high malignancy and early onset of metastases. In recent years, there has been a growing interest in the role of diverse immune cells in tumor cell development and metastasis. Using The Cancer Genome Atlas and the gene expression omnibus databases, and the CIBERSORT method, we investigated the topography of intra-tumor immune infiltration in uveal melanoma in this research. We evaluated the prognosis of uveal melanoma patients using the M2 macrophage immune cell infiltration score in conjunction with clinical tumor patient data. We built a prognostic model based on the distinctive genes of M2 macrophages and combined it with patients' clinical data in the database; we ran a survival prognostic analysis to authenticate the model's accuracy. The functional study revealed the importance of macrophage-associated genes in the development of uveal melanoma. Moreover, the reliability of our prediction model was verified by combining tumor mutational load, immune checkpoint, and drug sensitivity, respectively. Our study provides a reference for the follow-up study of uveal melanoma.

B cells in the tumor microenvironment: Multi-faceted organizers, regulators, and effectors of anti-tumor immunity

Our understanding of tumor-infiltrating lymphocytes (TILs) is rapidly expanding beyond T cell-centric perspectives to include B cells and plasma cells, collectively referred to as TIL-Bs. In many cancers, TIL-Bs carry strong prognostic significance and are emerging as key predictors of response to immune checkpoint inhibitors. TIL-Bs can perform multiple functions, including antigen presentation and antibody production, which allow them to focus immune responses on cognate antigen to support both T cell responses and innate mechanisms involving complement, macrophages, and natural killer cells. In the stroma of the most immunologically "hot" tumors, TIL-Bs are prominent components of tertiary lymphoid structures, which resemble lymph nodes structurally and functionally. Additionally, TIL-Bs participate in a variety of other lympho-myeloid aggregates and engage in dynamic interactions with the tumor stroma. Here, we summarize our current understanding of TIL-Bs in human cancer, highlighting the compelling therapeutic opportunities offered by their unique tumor recognition and effector mechanisms.

Identify AGAP2 as prognostic biomarker in clear cell renal cell carcinoma based on bioinformatics and IHC staining

Background

Arf GTPase-activating proteins are aberrantly expressed in a variety of tumors, but their role in clear cell renal cell carcinoma (ccRCC) was unclear. Exploring the biological role of Arf GAP with GTP binding protein like domain, Ankyrin repeat and PH domain 2 (AGAP2) in ccRCC could improve our understanding on the aggressiveness and immune relevance of ccRCC.

Methods

The expression of AGAP2 was analyzed based on the Cancer Genome Atlas (TCGA) database and verified in ccRCC samples using immunohistochemistry. The association between AGAP2 and clinical cancer stages was explored by TCGA dataset and UALCAN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to analyze the biological functions of AGAP2-related genes. Moreover, the relationship between AGAP2 and immune cell infiltration was investigated with TIME and TCGA dataset.

Results

Compared to normal tissues, AGAP2 was upregulated in ccRCC tissues. Higher expression of AGAP2 was associated with clinical cancer stages, TNM stages, pathologic stages, and status. Prognostic analysis on AGAP2 showed that AGAP2 overexpression was associated with KIRC overall survival (OS) reduction (P = 0.019). However, higher expression of AGAP2 may improve the OS of CESC (P = 0.002), THYM (P = 0.006) and UCEC (P = 0.049). GO and KEGG analysis showed that AGAP2-related genes was related to T cell activation, immune activity and PD-L1 expression and PD-1 checkpoint pathway. Furthermore, our study showed that AGAP2 were significantly associated with T cells, Cytotoxic cells, Treg, Th1 cells, CD8 T cells, T helper cells. And AGAP2 expression level affected the abundance of immune cells infiltration. The infiltrating level of immune cells was different between the AGAP2 high-expression and low-expression groups.

Conclusion

The expression of AGAP2 in ccRCC was higher than that in normal kidney tissues. It was significantly associated with clinical stage, poor prognosis, and immune cell infiltration. Therefore, AGAP2 may become an important component for ccRCC patients who receive precision cancer therapy and may be a promising prognostic biomarker.

Development of a TGF-β signaling-related genes signature to predict clinical prognosis and immunotherapy responses in clear cell renal cell carcinoma

Background

Transforming growth factor (TGF)-β signaling is strongly related to the development and progression of tumor. We aimed to construct a prognostic gene signature based on TGF-β signaling-related genes for predicting clinical prognosis and immunotherapy responses of patients with clear cell renal cell carcinoma (ccRCC).

Methods

The gene expression profiles and corresponding clinical information of ccRCC were collected from the TCGA and the ArrayExpress (E-MTAB-1980) databases. LASSO, univariate and multivariate Cox regression analyses were conducted to construct a prognostic signature in the TCGA cohort. The E-MTAB-1980 cohort were used for validation. Kaplan-Meier (K-M) survival and time-dependent receiver operating characteristic (ROC) were conducted to assess effectiveness and reliability of the signature. The differences in gene enrichments, immune cell infiltration, and expression of immune checkpoints in ccRCC patients showing different risks were investigated.

Results

We constructed a seven gene (PML, CDKN2B, COL1A2, CHRDL1, HPGD, CGN and TGFBR3) signature, which divided the ccRCC patients into high risk group and low risk group. The K-M analysis indicated that patients in the high risk group had a significantly shorter overall survival (OS) time than that in the low risk group in the TCGA (p < 0.001) and E-MTAB-1980 (p = 0.012). The AUC of the signature reached 0.77 at 1 year, 0.7 at 3 years, and 0.71 at 5 years in the TCGA, respectively, and reached 0.69 at 1 year, 0.72 at 3 years, and 0.75 at 5 years in the E-MTAB-1980, respectively. Further analyses confirmed the risk score as an independent prognostic factor for ccRCC (p < 0.001). The results of ssGSEA that immune cell infiltration degree and the scores of immune-related functions were significantly increased in the high risk group. The CIBERSORT analysis indicated that the abundance of immune cell were significantly different between two risk groups. Furthermore, The risk score was positively related to the expression of PD-1, CTLA4 and LAG3.These results indicated that patients in the high risk group benefit more from immunotherapy.

Conclusion

We constructed a novel TGF-β signaling-related genes signature that could serve as an promising independent factor for predicting clinical prognosis and immunotherapy responses in ccRCC patients.

Identification and Validation of the Prognostic Panel in Clear Cell Renal Cell Carcinoma Based on Resting Mast Cells for Prediction of Distant Metastasis and Immunotherapy Response

Clear cell renal cell carcinoma (ccRCC) has a high metastatic rate, and its incidence and mortality are still rising. The aim of this study was to identify the key tumor-infiltrating immune cells (TIICs) affecting the distant metastasis and prognosis of patients with ccRCC and to construct a relevant prognostic panel to predict immunotherapy response. Based on ccRCC bulk RNA sequencing data, resting mast cells (RMCs) were screened and verified using the CIBERSORT algorithm, survival analysis, and expression analysis. Distant metastasis-associated genes were identified using single-cell RNA sequencing data. Subsequently, a three-gene (CFB, PPP1R18, and TOM1L1) panel with superior distant metastatic and prognostic performance was established and validated, which stratified patients into high- and low-risk groups. The high-risk group exhibited lower infiltration of RMCs, higher tumor mutation burden (TMB), and worse prognosis. Therapeutically, the high-risk group was more sensitive to anti-PD-1 and anti-CTLA-4 immunotherapy, whereas the low-risk group displayed a better response to anti-PD-L1 immunotherapy. Furthermore, two immune clusters revealing distinct immune, clinical, and prognosis heterogeneity were distinguished. Immunohistochemistry of ccRCC samples verified the expression patterns of the three key genes. Collectively, the prognostic panel based on RMCs is able to predict distant metastasis and immunotherapy response in patients with ccRCC, providing new insight for the treatment of advanced ccRCC.

Construction of an interferon regulatory factors-related risk model for predicting prognosis, immune microenvironment and immunotherapy in clear cell renal cell carcinoma

Background

Interferon regulatory factors (IRFs) played complex and essential roles in progression, prognosis, and immune microenvironment in clear cell renal cell carcinoma (ccRCC). The purpose of this study was to construct a novel IRFs-related risk model to predict prognosis, tumor microenvironment (TME) and immunotherapy response in ccRCC.

Methods

Multi-omics analysis of IRFs in ccRCC was performed based on bulk RNA sequencing and single cell RNA sequencing data. According to the expression profiles of IRFs, the ccRCC samples were clustered by non-negative matrix factorization (NMF) algorithm. Then, least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were applied to construct a risk model to predict prognosis, immune cells infiltration, immunotherapy response and targeted drug sensitivity in ccRCC. Furthermore, a nomogram comprising the risk model and clinical characteristics was established.

Results

Two molecular subtypes with different prognosis, clinical characteristics and infiltration levels of immune cells were identified in ccRCC. The IRFs-related risk model was developed as an independent prognostic indicator in the TCGA-KIRC cohort and validated in the E-MTAB-1980 cohort. The overall survival of patients in the low-risk group was better than that in the high-risk group. The risk model was superior to clinical characteristics and the ClearCode34 model in predicting the prognosis. In addition, a nomogram was developed to improve the clinical utility of the risk model. Moreover, the high-risk group had higher infiltration levels of CD8⁺ T cell, macrophages, T follicular helper cells and T helper (Th1) cells and activity score of type I IFN response but lower infiltration levels of mast cells and activity score of type II IFN response. Cancer immunity cycle showed that the immune activity score of most steps was remarkably higher in the high-risk group. TIDE scores indicated that patients in the low-risk group were more likely responsive to immunotherapy. Patients in different risk groups showed diverse drug sensitivity to axitinib, sorafenib, gefitinib, erlotinib, dasatinib and rapamycin.

Conclusions

In brief, a robust and effective risk model was developed to predict prognosis, TME characteristics and responses to immunotherapy and targeted drugs in ccRCC, which might provide new insights into personalized and precise therapeutic strategies.

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.

Comprehensive analysis of a homeobox family gene signature in clear cell renal cell carcinoma with regard to prognosis and immune significance

The homeobox (HOX) family genes have been linked to multiple types of tumors, while their effect on malignant behaviors of clear cell renal cell carcinoma (ccRCC) and clinical significance remains largely unknown. Here, we comprehensively analyzed the expression profiles and prognostic value of HOX genes in ccRCC using datasets from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. We developed a prognostic signature comprising eight HOX genes (HOXB1, HOXA7, HOXB5, HOXD8, HOXD9, HOXB9, HOXA9, and HOXA11) for overall survival prediction in ccRCC and it allowed patients to be subdivided into high- and low-risk groups. Kaplan-Meier survival analysis in all the internal and external cohorts revealed significant difference in clinical outcome of patients in different risk groups, indicating the satisfactory predictive power of the signature. Additionally, we constructed a prognostic nomogram by integrating signature-derived risk score and clinical factors such as gender, age, T and M status, which might be helpful for clinical decision-making and designing tailored management schedules. Immunological analysis revealed that the regulatory T cells (Tregs) infiltrated differently between the two subgroups in both TCGA and ICGC cohorts. ssGSEA method showed that the enrichment scores for mast cells were significantly lower in high-risk group compared with the low-risk group, which was consistent in both TCGA and ICGC cohorts. As for the related immune function, the enrichment scores of APC co-inhibition, para-inflammation, and type II IFN response were consistently lower in high-risk group in both cohorts. Of the eight HOX genes, the mRNA and protein levels of HOXD8 were downregulated in ccRCC than that in normal tissues, and decreased expression of HOXD8 was associated with increased tumor grade and stage, and lymph node metastasis. Survival analysis revealed that lower expression of HOXD8 predicted worse overall survival in ccRCC. In conclusion, our HOX gene-based signature was a favorable indicator to predict the prognosis of ccRCC cases and associated with immune cell infiltration. HOXD8 might be a tumor suppressor gene in ccRCC and a potential predictor of tumor progression.

CDCA3 Predicts Poor Prognosis and Affects CD8+ T Cell Infiltration in Renal Cell Carcinoma

Background

Cell division cycle associated 3 (CDCA3) mediates the ubiquitination WEE1 kinase at G2/M phase. However, its contribution to cancer immunity remains uncertain.

Methods

We first evaluated the effect of CDCA3 on the prognosis of patients with renal cell carcinoma (RCC). The results of bioinformatics analysis were verified by the tissue microarray, immunofluorescence (IF) staining, CCK-8 assay, colony formation, cell cycle, and Western blot.

Results

Bioinformatics analysis predicated CDCA3 was an independent predictor of poor prognosis in RCC and was associated with poor TNM stage and grade. CDCA3 was related to the infiltration of CD8⁺ T cells and Tregs. Tissue microarray demonstrated that CDCA3 was strongly associated with poor prognosis and positively relevant to CD8⁺ T infiltration. In vitro experiments showed that exgenomic interference of CDCA3 could attenuate cellular proliferation, arrest cell cycle, and blockade accumulation of CDK4, Bub3, and Cdc20 in mitosis process.

Conclusion

CDCA3 presents as a good biomarker candidate to predict the prognosis of RCC patients and potentiates the immune tumor microenvironment (TME) of RCC.

A novel marker based on necroptosis-related long non-coding RNA for forecasting prognostic in patients with clear cell renal cell carcinoma

Background: The incidence of clear cell renal cell carcinoma (ccRCC) is high and has increased gradually in recent years. At present, due to the lack of effective prognostic indicators, the prognosis of ccRCC patients is greatly affected.Necroptosis is a type of cell death, and along with cell necrosis is considered a new cancer treatment strategy. The aim of this study was to construct a new marker for predicting the prognosis of ccRCC patients based on long non-coding RNA (nrlncRNAs) associated with necroptosis.

Methods: RNA sequence data and clinical information of ccRCC patients from the Cancer Genome Atlas database (TCGA) were downloaded. NrlncRNA was identified by Pearson correlation study. The differentially expressed nrlncRNA and nrlncRNA pairs were identified by univariate Cox regression and Lasso-Cox regression. Finally, a Kaplan-Meier survival study, Cox regression, clinicopathological features correlation study, and receiver operating characteristic (ROC) spectrum were used to evaluate the prediction ability of 25-nrlncrnas for markers. In addition, correlations between the risk values and sensitivity to tumor-infiltrating immune cells, immune checkpoint inhibitors, and targeted drugs were also investigated.

Results: In the current research, a novel marker of 25-nrlncRNAs pairs was developed to improve prognostic prediction in patients with ccRCC. Compared with clinicopathological features, nrlncRNAs had a higher diagnostic validity for markers, with the 1-year, 3-years, and 5-years operating characteristic regions being 0.902, 0.835, and 0.856, respectively, and compared with the stage of 0.868, an increase of 0.034. Cox regression and stratified survival studies showed that this marker could be an independent predictor of ccRCC patients. In addition, patients with different risk scores had significant differences in tumor-infiltrating immune cells, immune checkpoint, and semi-inhibitory concentration of targeted drugs. The feature could be used to evaluate the clinical efficacy of immunotherapy and targeted drug therapy.

Conclusion: 25-nrlncRNAs pair markers may help to evaluate the prognosis and molecular characteristics of ccRCC patients, which improve treatment methods and can be more used in clinical practice.

Identification of an immune-related gene prognostic index for predicting survival and immunotherapy efficacy in papillary renal cell carcinoma

Despite considerable progress has been made in the understanding of the genetics and molecular biology of renal cell carcinoma (RCC), therapeutic options of patients with papillary renal cell carcinoma (PRCC) are limited. Immunotherapy based on immune checkpoint inhibitors (ICIs) has become a hot point in researching new drug for tumor and been tested in a number of human clinical trials. In this study, an immune-related gene prognostic index (IRGPI) was developed and provided a comprehensive and systematic analysis of distinct phenotypic and molecular portraits in the recognition, surveillance, and prognosis of PRCC. The reliability of the IRGPI was evaluated using independent datasets from GEO database and the expression levels of the genes in the IRGPI detected by real-time PCR. Collectively, the currently established IRGPI could be used as a potential biomarker to evaluate the response and efficacy of immunotherapy in PRCC.

Immune-Associated Gene Signatures Serve as a Promising Biomarker of Immunotherapeutic Prognosis for Renal Clear Cell Carcinoma

As the most common type of renal cell carcinoma (RCC), the renal clear cell carcinoma (ccRCC) is highly malignant and insensitive to chemotherapy or radiotherapy. Although systemic immunotherapies have been successfully applied to ccRCC in recent years, screening for patients who can benefit most from these therapies is still essential and challenging due to immunological heterogeneity of ccRCC patients. To this end, we implemented a series of deep investigation on the expression and clinic data of ccRCC from The Cancer Genome Atlas (TCGA) International Consortium for Cancer Genomics (ICGC). We identified a total of 946 immune-related genes that were differentially expressed. Among them, five independent genes, including SHC1, WNT5A, NRP1, TGFA, and IL4R, were significantly associated with survival and used to construct the immune-related prognostic differential gene signature (IRPDGs). Then the ccRCC patients were categorized into high-risk and low-risk subgroups based on the median risk score of the IRPDGs. IRPDGs subgroups displays distinct genomic and immunological characteristics. Known immunotherapy-related genes show different mutation burden, wherein the mutation rate of VHL was higher than 40% in the two IRPDGs subgroups, and SETD2 and BAP1 mutations differed most between two groups with higher frequency in the high-risk subgroup. Moreover, IRPDGs subgroups had different abundance in tumor-infiltrating immune cells (TIICs) with distinct immunotherapy efficacy. Plasma cells, regulatory cells (Tregs), follicular helper T cells (Tfh), and M0 macrophages were enriched in the high-risk group with a higher tumor immune dysfunction and rejection (TIDE) score. In contrast, the low-risk group had abundant M1 macrophages, mast cell resting and dendritic cell resting infiltrates with lower TIDE score and benefited more from immune checkpoint inhibitors (ICI) treatment. Compared with other biomarkers, such as TIDE and tumor inflammatory signatures (TIS), IRPDGs demonstrated to be a better biomarker for assessing the prognosis of ccRCC and the efficacy of ICI treatment with the promise in screening precise patients for specific immunotherapies.

Genetic association study of intron variants in the forkhead box protein P3 gene in Chinese patients diagnosed with cervical cancer

The aim of this study was to investigate the effects of forkhead box protein P3 (FOXP3) intron single nucleotide variants (SNVs) in high-risk human papilloma virus (HR-HPV) infection and cervical cancer (CC) malignant lesions. We performed FOXP3 genotyping in 350 patients with CC and 350 healthy controls using the ImLDR multiple single nucleotide polymorphism genotyping technology. The heterozygous mutation TC in rs2294021 decreased the risk of HR-HPV infection and CC malignant lesions (TC vs. TT: OR = 0.71, 95% CI = 0.51-0.99); the dominant model TC+CC and allele C in rs2294021 decreased the risk of CC malignant lesions (TC+CC vs. TT: OR = 0.69, 95% CI = 0.50-0.95; C vs. T: OR = 0.78, 95% CI = 0.63-0.97). The heterozygous mutation GA, dominant model GA+AA and allele A in rs3761549 also decreased the risk of HR-HPV infection and CC malignant lesions (GA vs. GG: OR = 0.70, 95% CI = 0.51-0.96; GA+AA vs. GG: OR = 0.69, 95% CI = 0.51-0.94; A vs. G: OR = 0.75, 95% CI = 0.58-0.96). Patients with CC and HR-HPV infection carrying rs2294021 TC and rs3761549 GA had lower expression of FOXP3 protein. Haplotype analysis revealed that T-C-A decreased the risk of HR-HPV infection. Furthermore, we found a significant association between immune cells infiltration and prognosis in patients with CC. Our findings demonstrated that rs2294021 and rs3761549 variants may protect against HR-HPV and CC malignant lesions by downregulating FOXP3 and that FOXP3 was associated with immune cells infiltration, which affected the prognosis of CC.

Identification and Validation of a Prognostic Immune-Related Gene Signature in Esophageal Squamous Cell Carcinoma

Esophageal carcinoma (EC) is a common malignant cancer worldwide. Esophageal squamous cell carcinoma (ESCC), the main type of EC, is difficult to treat because of the widespread morbidity, high fatality rates, and low quality of life caused by postoperative complications and no specific molecular target. In this study, we screened genes to establish a prognostic model for ESCC. The transcriptome expression profiles of 81 ESCC tissues and 340 normal esophageal mucosal epithelium tissues were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) cohorts. The transcriptome expression datasets of 19 esophageal squamous carcinoma cell lines were downloaded from Cancer Cell Line Encyclopedia (CCLE). The R software Limma package was used to identify 6,231 differentially expressed genes and 647 differentially expressed immune-related genes between normal and ESCC tissues. Gene functional analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) was used to screen out 18 immune-related prognostic genes. We then established the prognostic and risk signature using these genes, and the patients were divided into low-risk and high-risk groups. Compared with high-risk group patients, the low-risk group patients had longer overall survival. M1 macrophages and resting dendritic cells were differentially distributed between the low-risk and high-risk groups and were related to patient survival. We also examined the functional immune cell and immune molecule levels in low-risk and high-risk group patients, with significant differences in the tumor microenvironment between the two groups. To further verify the accuracy of the prognostic risk model, we performed area under the ROC curve (AUC) analysis. The AUC value was 0.931 for the prognostic risk, which was better than the microsatellite instability (MSI) and Tumor Immune Dysfunction and Exclusion (TIDE) scores. In conclusion, we found 18 immune-related prognostic genes related to the occurrence of ESCC and established a prognostic model for predicting disease severity.

Immune Infiltration in Gastric Cancer Microenvironment and Its Clinical Significance

Immunotherapy has developed rapidly and has gradually become one of the important methods for treatment of gastric cancer (GC). The research on tumor infiltrating immune cells (TIICs) and immune-related genes in the tumor microenvironment (TME) greatly encourages the development of immunotherapy. The devolution algorithm (CIBERSORT) was applied to infer the proportion of 22 TIICs based on gene expression profiles of GC tissues, which were downloaded from TCGA and GEO. TCGA was utilized to analyze the differential expression of immune-related genes, and explore the potential molecular functions of these genes. We have observed the enrichment of multiple TIICs in microenvironment of GC. Some of these cells were closely related to tumor mutational burden (TMB), microsatellite instability (MSI), Fuhrman grade, and TNM staging. Survival analysis showed that the infiltration level of CD8+ T cells, activated CD4+ memory T cells and M2 macrophages were significantly related to the prognosis of GC patients. The functional enrichment analysis of immune-related genes revealed that these genes were mainly associated with cytokine activation and response. Four significant modules were screened by PPI network and 20 key genes were screened from the modules. The expression levels of CALCR and PTH1R are strikingly related to the expression of immune checkpoint and the prognosis of GC patients. The type and number of TIICs in microenvironment of GC, as well as immune-related genes are closely related to tumor progression, and can be used as important indicators for patient prognosis assessment.

A Ferroptosis-Related Genes Model Allows for Prognosis and Treatment Stratification of Clear Cell Renal Cell Carcinoma: A Bioinformatics Analysis and Experimental Verification

Introduction

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor characterized by poor prognosis and difficult treatment. Ferroptosis is a relatively new form of programmed cell death that involved in cancer development and therapy resistance. Studies have shown that targeted ferroptosis may be a novel option for the treatment of ccRCC, but key genes and their roles between ferroptosis and ccRCC are limited so far. This study aims to develop a ccRCC stratified model based on ferroptosis-related genes to provide a reference for the prognosis prediction and the individualized treatment of ccRCC.

Materials and Methods

The mRNAs expression data of ccRCC and FRGs were obtained from TCGA and FerrDb database, respectively. Through multiple analysis, a 4-FRG based prognostic stratified model was constructed and its predictive performance was validated through various methods. Then, a nomogram based on the model was constructed and ccRCC patients stratified by the model were analyzed for tumor microenvironment, immune infiltration, sensitivity for immune checkpoint inhibitors (ICIs)/traditional anti-tumor therapy and tumor mutation burden (TMB). Functional enrichment analysis was performed to explore potential biological pathways. Finally, we verified our model by RT-qPCR, siRNA transfection, scratch assay and CCK-8 assay.

Results

In this study, the stratified model and a model-based nomogram can accurately predict the prognosis of ccRCC patients in TCGA database. The patients stratified by the model showed different tumor microenvironments, immune infiltration, TMB, resistance to traditional and ICIs therapy, and sensitivity to ferroptosis. Functional enrichment analysis suggested several biological pathways related to the process and prognosis of ccRCC. RT-qPCR confirmed the differential expression of ferroptosis-related genes. Scratch assay and CCK-8 assay indicated the promotion effects of CD44 on the proliferation and migration of ccRCC.

Conclusion

In this study, we established a novel ccRCC stratified model based on FRGs, which can accurately predict the prognosis of ccRCC patients and provide a reference for clinical individualized treatment.

Bioinformatics Analysis of Prognostic Value of SPC24 in ccRCC and Pan-Cancer

Purpose

Clear cell renal cell carcinoma (ccRCC) is one of the most common diseases in the world, with high morbidity and mortality. Recent studies have revealed the important role of SPC24, a subunit of the Ndc80 complex, in the occurrence and development of carcinoma. However, the latent effect of SPC24 in the progress of ccRCC remains to be further explored. The intent of this research is to investigate whether SPC24 can be used as an index to predict the progression of ccRCC and to explore its relationship with the immune microenvironment and pan-cancer.

Materials and Methods

Based on data from public databases, we determined the expression level and clinical value of SPC24 in ccRCC and human pan-cancer. RT-qPCR analysis was carried out to detect the expression level of SPC24 in the OSRC/786O (human ccRCC cells) cell lines and HK2 (human normal kidney cells) cell line. The signal transduction pathways activated by different levels of SPC24 expression were explored by Gene Set Enrichment Analysis (GSEA), and the CIBERSORT algorithm was applied to analyze the relationship between infiltrating immune cells and SPC24 expression in ccRCC and pan-cancer tissues.

Results

SPC24 is overexpressed in ccRCC and several types of tumors, which is associated with poor prognosis. GSEA and CIBERSORT algorithms suggested that the high expression group of SPC24 enriched various pathways including immune-related pathways, and the several infiltrated immunocytes were related to the expression of SPC24.

Conclusion

Our study revealed that SPC24 is a prognostic factor in ccRCC related to immunomodulation and has generalized value in pan-cancer.

Identification of a competing endogenous RNA network related to immune signature in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a fatal cancer of the urinary system. Long non-coding RNAs (lncRNAs) act as competitive endogenous RNAs (ceRNAs) involving the ccRCC progression. However, the relationship between the ceRNA network and immune signature is largely unknown. In this study, the ccRCC-related gene expression profiles retrieved from the TCGA database were used first to identify the differentially expressed genes through differential gene expression analysis and weighted gene co-expression network analysis. The interaction among differentially expressed lncRNAs, miRNAs, and mRNAs were matched using public databases. As a result, a ceRNA network was developed that contained 144 lncRNAs, 23 miRNAs, as well as 62 mRNAs. Four of 144 lncRNAs including LINC00943, SRD5A3-AS1, LINC02345, and U62317.3 were identified through LASSO regression and Cox regression analyses, and were used to create a prognostic risk model. Then, the ccRCC samples were divided into the high- and low-risk groups depending on their risk scores. ROC curves, Kaplan-Meier survival analysis, and the survival risk plots indicated that the predictive performance of our developed risk model was accurate. Moreover, the CIBERSORT algorithm was used to measure the infiltration levels of immune cells in the ccRCC samples. The further genomic analysis illustrated a positive correlation between most immune checkpoint blockade-related genes and the risk score. In conclusion, the present findings effectually contribute to the comprehensive understanding of the ccRCC pathogenesis, and may offer a reference for developing novel therapeutic and prognostic biomarkers.

An immune-related lncRNA risk coefficient model to predict the outcomes in clear cell renal cell carcinoma

Objective: Using model algorithms, we constructed an immune-related long non-coding RNAs (lncRNAs) risk coefficient model to predict outcomes for patients with clear cell renal cell carcinoma (ccRCC) to understand the infiltration of tumor immune cells and the sensitivity to immune-targeted drugs.

Methods: Open genes data were downloaded from The Cancer Genome Atlas and The Immunology Database and Analysis Portal, and immune-related lncRNAs were obtained through Pearson correlation analysis. R language software was used to obtain differentially expressed immune-related lncRNAs and immune-related lncRNA pairs. The model was constructed using least absolute shrinkage and selector operation regression analysis, and receiver operator characteristic curves were drawn. The Akaike information criterion was used to distinguish the high-risk from the low-risk group. We also conducted correlation analysis for the high- and low-risk subgroups.

Results: We identified 27 immune-related lncRNAs pairs, 16 of which were included in the model construction. After merging clinical data, the areas under the curve of 1 -year, 3-year, and 5-year survival times of ccRCC patients were 0.867, 0.832, and 0.838, respectively. Subgroup analyses were conducted according to the cut-off value. We found that the high-risk group was associated with poor outcomes. The risk score and tumor stage were independent predictors of the outcome of ccRCC. The risk model predicted specific immune cell infiltration, immune checkpoint gene expression levels, and high-risk groups more sensitive to sunitinib targeted therapy.

Conclusion: We obtained prognostic-related novel ccRCC markers and risk model that predicts the outcome of patients with ccRCC and helps identify those who can benefit from sunitinib.

The Role of Critical N6-Methyladenosine-Related Long Non-Coding RNAs and Their Correlations with Immune Checkpoints in Renal Clear Cell Carcinoma

Purpose

This study aimed to evaluate the functions of critical N6-methyladenosine (m6A)-related long non-coding RNAs (lncRNAs) and their correlations with immunotherapeutic targets in clear cell renal cell carcinoma (ccRCC).

Methods

m6A-related lncRNAs were analyzed using the dataset from The Cancer Genome Atlas database via Pearson correlation analysis. Then, their prognostic functions in patients with ccRCC were determined via univariate Cox analysis. A prognostic m6A-related lncRNA signature (MRLS) in ccRCC was established using the least absolute shrinkage and selection operator (LASSO) Cox regression model. In addition, the correlations between these prognostic m6A-related lncRNAs with immune checkpoints were further evaluated in clinical samples.

Results

MRLS was established by the LASSO Cox regression model on the basis of seven prognostic m6A-related lncRNAs. The risk score for each patient was calculated using the MRLS model, and the patients were further stratified into high- and low-risk subgroups. The MRLS model was validated with a robust prognostic ability by the stratification analysis. On the basis of age, grade, stage, and risk score, a nomogram was developed with a strong reliability in forecasting the overall survival percentages of the patients with ccRCC. Moreover, seven prognostic m6A-related lncRNAs enrolled in the MRLS model were found to be correlated with various immunotherapeutic targets, namely, PD-1, PD-L1, CTLA4, and LAG3, and the expression levels of which in the high-risk subgroup were significantly higher than those in the low-risk subgroup. The significant correlations between LINC00342 and the aforementioned immunotherapeutic targets were also confirmed in clinical samples.

Conclusion

In this study, seven m6A-related lncRNAs were identified as potential biomarkers for forecasting the prognosis of patients with ccRCC and evaluating the efficacy of immunotherapy for these patients. Furthermore, a prognostic and predictive MRLS model with a high reliability was constructed to predict the overall survival probability of patients with ccRCC.

Integrin Alpha-2 as a Potential Prognostic and Predictive Biomarker for Patients With Lower-Grade Glioma

Diffuse gliomas are the most common malignant brain tumors with the highest mortality and recurrence rate in adults. Integrin alpha-2 (ITGA2) is involved in a series of biological processes, including cell adhesion, stemness regulation, angiogenesis, and immune/blood cell functions. The role of ITGA2 in lower-grade gliomas (LGGs) is not well defined. Firstly, we downloaded RNA sequencing and relevant clinical information from The Cancer Genome Atlas cohort, the Chinese Glioma Genome Atlas cohort, and related immune cohorts. Next, prognosis analysis, difference analysis, clinical model construction, enrichment analysis, and immune infiltration analysis are performed for this study. These analyses indicated that ITGA2 may have clinical application value and research value in LGG immunotherapy. We also detected the mRNA and protein expression of ITGA2 in three LGG cell lines and normal glial cells using quantitative real-time polymerase chain reaction assay and western blot assay. Our study not only offers a novel target for LGG immunotherapy but also can better comprehend the mechanism of the development and progression of patients with LGG. This study revealed that ITGA2 may be a potential prognostic and predictive biomarker for LGG, which can bring new insights into targeted immunotherapy.

Dipetidyl peptidase-4 and transferrin receptor serve as prognostic biomarkers for acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is the most common hematological malignancy in adult patients. Ferroptosis-related signatures have been shown to act as regulators of the progression of multiple cancer types, but the role of ferroptosis in AML remains to be elucidated. We performed the present study to preliminarily investigate the roles of ferroptosis-related genes (FRGs) in AML.

Methods

The transcriptome data of AML patients was downloaded from The Cancer Genome Atlas (TCGA) and the transcriptome data of normal samples was obtained from the Genotype-Tissue Expression (GTEx) database. FRGs were selected via public articles. Expression levels of FRGs between AML and normal samples were analyzed. The prognostic model based on FRGs was constructed via lasso regression. The expression levels and prognostic role of FRGs were identified from the risk model. We also performed validation experiments to verify the expression levels of the final selected genes via immunohistochemistry, polymerase chain reaction (PCR), and RNA-seq. Finally, we explored the associations between immune infiltration, drug sensitivity, and the selected FRGs.

Results

The transcriptome data of 151 AML samples were retrieved from TCGA and 70 bone marrow normal samples were retrieved from the GTEx database. Additionally, 23 FRGs were collected from the published articles. There were 22 differentially expressed FRGs, and among them, dipetidyl peptidase-4 (DPP4) (P= 0.011, HR =1.504), GPX4 (P=0.055, HR =1.569), LPCAT3 (P<0.001, HR =2.243), SLC7A11 (P=0.012, HR =2.243), and transferrin receptor (TFRC) (P=0.029, 0.774) had a significant influence on the prognosis of AML patients via lasso regression. The area under the curve (AUC) values of the 1-, 3-, and 5-year receiver operating characteristic (ROC) curves of the FRG signatures indicated that this model is novel and effective method for predicting the prognosis of AML patients. DPP4 (P<0.001) was overexpressed while LPCAT3 (P<0.001), TFRC (P<0.001), GPX4 (P<0.001), and SLC7A11 (P<0.001) were downregulated, further validation experiment results indicated that DPP4 was significantly downregulated but TFRC was upregulated in AML samples. Dysregulation of DPP4 and TFRC influence numbers of chemotherapy regimens sensitivity.

Conclusions

DPP4 and TFRC act as biomarkers for predicting and diagnosing AML, and their expression levels also have significant correlations with drug resistance in AML.

Integrated analysis on the N6-methyladenosine-related long noncoding RNAs prognostic signature, immune checkpoints, and immune cell infiltration in clear cell renal cell carcinoma

Background

Patients with advanced clear cell renal cell carcinoma (ccRCC) have a poor prognosis and lack effective prognostic biomarkers. N6‐methyladenosine‐related lncRNAs (m6A‐related long noncoding RNAs [lncRNAs]) have been confirmed to be associated with the development of multiple tumors, but its role in ccRCC is not clear.

Methods

Gene expression data and clinical information of ccRCC patients were extracted from The Cancer Genome Atlas Database. The prognostic m6A‐related lncRNAs were obtained by Pearson's correlation analysis and univariate Cox regression analysis. Afterward, the cluster classification and its correlation with prognosis, clinical characteristics, and immunity were analyzed. LASSO regression was used to establish the prognostic risk model. The predictive performance of the prognostic model was evaluated and validated by survival analysis and receiver operating characteristic curve analysis, et al. The expression of immune checkpoints and immune cell infiltration in patients with different risks were systematically analyzed.

Results

A total of 27 prognostic m6A‐related lncRNAs were identified. These m6A‐related lncRNAs were differentially expressed between tumor and normal tissues. Among them, 24 high‐risk m6A‐related lncRNAs were overexpressed in Cluster 2 and correlated with poor prognosis, low stromal score, high expression of immune checkpoints, and immunosuppressive cells infiltration. Based upon, a prognostic risk model composed of seven m6A‐related lncRNAs was constructed. After a series of analyses, it was proved that this model had good sensitivity and specificity, and could predict the prognosis of patients with different clinical stratification. The expression of PD‐1, PD‐L1, CTLA‐4, LAG‐3, TIM‐3, and TIGIT were significantly increased in the high‐risk patients, and there was a correlation between the risk score and immune cell infiltration.

Conclusions

The seven m6A‐related lncRNAs prognostic risk signature showed reliable prognostic predictive power for ccRCC and was associated with the expression of immune checkpoints and immune cell infiltration. This seven m6A‐related lncRNAs signature will be helpful in managing ccRCC and guiding individualized immunotherapy.

Nucleolar protein NOP2 could serve as a potential prognostic predictor for clear cell renal cell carcinoma

As an indispensable part for cancer precision medicine, biomarkers and signatures for predicting cancer prognosis and therapeutic benefits were urgently required. The purpose of this study was to investigate the prognostic roles of NOP2 in renal clear cell carcinoma (ccRCC) for overall survival (OS) and its relationships with immunity. NOP2-related gene expression matrix associated with clinical information was obtained from the Cancer Genome Atlas (TCGA) ccRCC dataset and NOP2-related pathways were identified by gene set enrichment analysis (GSEA). Associations among the NOP2 expression and MSI, TMB, TNB, and immunity were also explored. Both the NOP2 mRNA and protein/phosphoprotein had a higher expression in ccRCC tumor tissues than in normal kidney tissues (both P < 0.001) and elevated NOP2 expression was associated with poor OS (P < 0.001). Logistic regression analysis revealed the NOP2 expression was significantly linked to stage, age, grade, N stage, T stage, and M stage (all P < 0.05). Univariate/multivariate Cox hazard regression analysis results indicated that NOP2 was an independent prognostic factor for OS in ccRCC and GSEA revealed five NOP2-related signaling pathways. Nomogram based on NOP2 and eight clinical characteristic parameters (grade, age, stage, gender, T stage, race, M stage, N stage) was constructed and carefully evaluated. Furthermore, NOP2 gene expression was also found to be significantly related to MSI, TMB, and immunity. Our findings revealed that NOP2 might be a potential prognostic factor for OS in ccRCC and it was significantly associated with immunity, MSI, and TMB.

Accumulation of CD45RO+CD8+ T cells is a diagnostic and prognostic biomarker for clear cell renal cell carcinoma

Renal cell carcinoma is characterized by high immunogenicity and infiltration of immune cells. CD45RO+CD8+ T cells are well known as a critical role in host defense of the immune environment. However, their role in clear cell renal carcinoma (ccRCC) remains unknown. To elucidate the clinical importance of CD45RO+CD8+ T cells in ccRCC as well as its underlying mechanism, we analyzed several types of peripheral immune cells from 274 patients with ccRCC who have received radical or partial nephrectomy and 350 healthy people. Flow cytomety assays showed there was no significant difference in the proportions of CD8+ T cells and its subtypes other than CD45RO+/CD45RA+CD8+ cells. Both gene and protein expression levels of CD45RO in ccRCC tissues were decreased. CD45RO+CD8+ T cells showed increased proliferative abilities but decreased apoptotic abilities through MAPK signaling activation in ccRCC. High expression level of CD45RO+CD8+ T cells inhibited ccRCC progression, including proliferation, invasion, as well as autophagy of ccRCC through many signaling pathways. Bioinformatics and immunohistochemical chip analysis measured gene and protein levels of CD45RO and other related proteins. The combination of UCHL1, HMGB3, and CD36 has diagnostic value in ccRCC and is able to predict prognosis. Collectively, CD45RO+CD8+ T cells play a critical role in ccRCC progression and may be regarded as clinical indicators.

A signature of 24 aging‑related gene pairs predict overall survival in gastric cancer

BACKGROUND

Aging is the major risk factor for most human cancers. We aim to develop and validate a reliable aging-related gene pair signature (ARGPs) to predict the prognosis of gastric cancer (GC) patients.

METHODS

The mRNA expression data and clinical information were obtained from two public databases, The Cancer Genome Atlas (TCGA) dataset, and Gene Expression Omnibus (GEO) dataset, respectively. The best prognostic signature was established using Cox regression analysis (univariate and least absolute shrinkage and selection operator). The optimal cut-off value to distinguish between high- and low-risk patients was found by time-dependent receiver operating characteristic (ROC). The prognostic ability of the ARGPS was evaluated by a log-rank test and a Cox proportional hazards regression model.

RESULTS

The 24 ARGPs were constructed for GC prognosis. Using the optimal cut-off value - 0.270, all patients were stratified into high risk and low risk. In both TCGA and GEO cohorts, the results of Kaplan-Meier analysis showed that the high-risk group has a poor prognosis (P < 0.001, P = 0.002, respectively). Then, we conducted a subgroup analysis of age, gender, grade and stage, and reached the same conclusion. After adjusting for a variety of clinical and pathological factors, the results of multivariate COX regression analysis showed that the ARGPs is still an independent prognostic factor of OS (HR, 4.919; 95% CI 3.345-7.235; P < 0.001). In comparing with previous signature, the novel signature was superior, with an area under the receiver operating characteristic curve (AUC) value of 0.845 vs. 0.684 vs. 0.695. The results of immune infiltration analysis showed that the abundance of T cells follicular helper was significantly higher in the low-risk group, while the abundance of monocytes was the opposite. Finally, we identified and incorporated independent prognostic factors and developed a superior nomogram to predict the prognosis of GC patients.

CONCLUSION

Our study has developed a robust prognostic signature that can accurately predict the prognostic outcome of GC patients.

Classification of Clear Cell Renal Cell Carcinoma based on Tumor Suppressor Genomic Profiling

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of malignancy in adults. However, the clinical significance of tumor suppressor genes (TSG) is largely elusive. Herein, the expression profile TSGs and its clinical response in ccRCC were investigated. A total of 603 ccRCC samples from two cohorts (TCGA and ICGC) were retrieved in this study. Three molecular subtypes (C1, C2, and C3) were identified based on the TSGs expression profile in the TCGA dataset. Through Weighted Gene Correlation Network Analysis (WGCNA), six modules associated with three subtypes were identified. Pathway enrichment for the modules revealed that crucial pathways including p53 signaling and immune-related pathways were significantly enriched. We further focused on the relationship between immune infiltration level and subtypes, and found that subtype C1 was associated with higher immune infiltration level, subtype C2 was corresponding with medium immune infiltration level, whereas subtype C3 was correlated with lower immune infiltration level. Interestingly, C2 have a better survival outcome, while C1 and C3 showed a poor prognosis. Considering their survival difference, we then performed a differentially expression analysis between C2 and C1&3, and a total of 99 differentially expressed tumor suppressor genes (DETSGs) were identified. According to these DETSGs, 59 potential compounds with 28 mechanisms of action (MOA) were predicted using the Connectivity Map (CMap) database. Among these compounds, leflunomide, naftopidil, and ribavirin were the most prospective compounds for the treatment of ccRCC. In addition, we found that subtype C2 is more sensitive to sorafenib and sunitinib drugs, and C2 have more likelihood to be responded to immunotherapy. In summary, the three subtypes hinged on the tumor suppressor gene expression for ccRCC might contribute to understanding the underlying molecular mechanisms of ccRCC. Also, its potential compounds might offer guidelines for developing a novel treatment strategy of ccRCC.

Succinylation Regulators Promote Clear Cell Renal Cell Carcinoma by Immune Regulation and RNA N6-Methyladenosine Methylation

Succinylation is a newly discovered and multienzyme-regulated post-translational modification (PTM) that is associated with the initiation and progression of cancer. Currently, no systematic analyses on the role of succinylation regulators in tumors have been reported. In this study, we performed a comprehensive pan-cancer analysis on four well-known succinylation regulators (CPT1A, KAT2A, SIRT5, and SIRT7). We found that these regulators played specific and critical roles in the prognosis of clear cell renal cell carcinoma (ccRCC). We constructed a risk score (RS) based on two independent prognostic prediction factors, CPT1A and KAT2A, and subsequently developed a nomogram model containing the RS, which showed good accuracy in the prediction of overall survival (OS) in ccRCC patients. Furthermore, we used the similar expression pattern of four succinylation regulators according to consensus clustering analysis to divide the patients into three clusters that exhibited prominently different OS as well as clinicopathological characteristics. Differently expressed genes (DEGs) and pathway enrichment analyses of three clusters indicated that succinylation regulators might promote malignant progression of ccRCC by regulating the infiltration of immune cells and RNA N6-methyladenosine (m6A) methylation. Importantly, our data suggest that CPT1A and SIRT5 might up-regulate and down-regulate the expression of LRPPRC and EIF3B, respectively. Our study systematically analyzed the prognostic predictive values of four succinylation regulators and revealed their potential mechanisms in ccRCC aggressiveness. These data provide new insight into the understanding of succinylation modification and present clinical evidence for its role in ccRCC treatments.

Prognostic value of peripheral blood T lymphocyte subsets in clear cell renal cell carcinoma

Background

To date, few studies have evaluated the role of peripheral blood T lymphocyte subsets in patients with clear cell renal cell carcinoma (ccRCC). Here we measured the levels of peripheral blood T lymphocyte subsets and evaluated its prognostic value in ccRCC.

Methods

Data from 122 patients with RCC from January 2018 to January 2020 were collected. Preoperative peripheral blood T lymphocyte subsets and medical records were analyzed. Kaplan-Meier cures and log rank test were used for analyzing overall survival (OS). Univariate and multivariate survival analyses were underwent by performing the Cox proportional hazards models. Correlations were tested by Pearson's correlation analysis.

Results

Of 122 patients, a total of 80 ccRCC patients was enrolled. Patients with low CD3⁺ T cells and low CD4⁺/CD8⁺ ratio displayed a worse OS than patients with high CD3⁺ T cells and high CD4⁺/CD8⁺ ratio (P=0.029 and 0.002, respectively). Multivariate analyses showed CD3⁺ T cells and CD4⁺/CD8⁺ ratio were independent predictive factors for the OS (HR: 0.295, 95% CI, 0.091-0.956; P=0.042 and HR: 0.244, 95% CI, 0.065-0.920; P=0.037, respectively). Moreover, NLR negatively correlated with both levels of CD3⁺ T cells and CD4⁺/CD8⁺ ratio (P<0.001, r=-0.398 and P=0.012, r=-0.280, respectively).

Conclusions

The findings of our study suggest that preoperative CD3⁺ T cells and CD4⁺/CD8⁺ ratio in peripheral blood are independent predictors for patients with ccRCC.

Development and validation of a VHL-associated immune prognostic signature for clear cell renal cell carcinoma

Background

VHL mutation is the most common mutation in clear cell renal cell carcinoma (ccRCC). Here, we developed and validated an immune-related signature to predict the prognosis of ccRCC with VHL mutations.

Methods

VHL mutation status and RNA expression were analysed in the TCGA datasets and our cohort. LASSO Cox analysis was performed to develop an immune-related signature. Candidate genes for the immune-related signature were differentially expressed between VHLwt and VHLmut ccRCC patients.

Results

VHL mutations resulted in the downregulation of the immune response in ccRCC. To develop an immune-related signature, LASSO Cox analysis was constructed by immune-related genes that were differentially expressed between VHLwt (WHL wild type) and VHLmut (VHL mutation) ccRCC patients. The signature was developed and validated in the TCGA and our own cohort to classify patients into groups based on having a low or high risk of poor survival. Functional enrichment analysis showed that the immune-related pathway represented the major function and pathway. In addition, patients in the high-risk group had a positive correlation with low fractions of CD4 + T cells and dendritic cells and presented a lower expression of CTLA-4 and PD-1 than the low-risk group.

Conclusion

In this study, we proposed a novel immune-related signature, which is a feasible biomarker for predicting the overall survival in VHLmut patients with ccRCC.

Development and Validation of an Individualized Immune-Related Gene Pairs Prognostic Signature in Papillary Renal Cell Carcinoma

Papillary renal carcinoma (PRCC) is one of the important subtypes of kidney cancer, with a high degree of heterogeneity. At present, there is still a lack of robust and accurate biomarkers for the diagnosis, prognosis and treatment selection of PRCC. Considering the important role of tumor immunity in PRCC, we aim to construct a signature based on immune-related gene pairs (IRGPs) to estimate the prognostic of patients with PRCC. We obtained gene expression profiling and clinical information of patients with PRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), which were divided into discovery (n = 287) and validation (n = 28) cohorts, respectively. By univariate analysis, multivariate Cox analysis, and least absolute shrinkage and selection operator (Lasso) analysis, we selected 14 IRGPs with a panel of 22 unique genes to construct the prognostic signature. According to the signature, we stratified patients into high-risk group and low-risk group. In both discovery and validation cohorts, the results of Kaplan-Meier analysis showed that there were significant differences in OS between the two groups (p < 0.001). Combined with multiple clinical and pathological factors, the results of multivariate analyses confirmed that this signature was an independent predictor of OS (HR, 3.548; 95%CI, 2.096-6.006; p < 0.001). The results of immune infiltration analysis demonstrated that the abundance of multiple tumor-infiltration lymphocytes such as CD8 + T cells, Tregs, and T follicular cell helper were significantly higher in the high-risk group. Functional analysis showed that multiple immune-related signaling pathways were enriched in the high-risk group. In conclusion, we successfully established an individualized prognostic IRGPs signature, which can accurately assess and predict the OS of patients with PRCC.

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.