Identification of transforming growth factor beta induced (TGFBI) as an immune-related prognostic factor in clear cell renal cell carcinoma (ccRCC)

TGFBI promotes proliferation and epithelial-mesenchymal transition in renal cell carcinoma through PI3K/AKT/mTOR/HIF-1α pathway

BACKGROUND

Renal cell carcinoma (RCC) is presently recognized as the most prevalent kidney tumor. However, the role and underlying mechanism of action of the conversion factor-inducible protein (TGFBI), an extracellular matrix protein, in RCC remain poorly understood.

METHODS

In this study, we employed Western blot, quantitative real-time polymerase chain reaction (qRT-PCR), and immunohistochemistry techniques to assess the expression of TGFBI in RCC tissues or cells. Furthermore, we analyzed the proliferation and migration of RCC cells using CCK8, cloning, scratching, and migration assays. Additionally, we examined apoptosis and cell cycle progression through flow cytometry, analysis. Lastly, we employed gene set enrichment analysis (GSEA) to investigate the biological processes associated with TGFBI, which were subsequently validated.

RESULTS

The findings indicate that TGFBI exhibits significantly elevated expression levels in both renal cell carcinoma (RCC) tissues and cells. Furthermore, the knockdown of TGFBI in SiRNA transfected cells resulted in the inhibition of RCC cell proliferation, migration, invasiveness, apoptosis, and alteration of the cell cycle. Additionally, TGFBI was found to impede the epithelial-mesenchymal transition (EMT) process in RCC cells. Bioinformatics analysis suggests that TGFBI may exert its influence on various biological processes in RCC through the tumor immune microenvironment. Moreover, our study demonstrates that TGFBI promotes RCC progression by activating the PI3K/AKT/mTOR/HIF-1α.

CONCLUSIONS

Our research indicates that TGFBI exhibits high expression in RCC and facilitate RCC progression and metastasis through various molecular mechanisms. Hence, TGFBI has the potential to be a novel therapeutic target for the diagnosis and treatment of RCC in the future.

TGFBI: A novel therapeutic target for cancer

TGFBI, an extracellular matrix protein induced by transforming growth factor β, has been found to exhibit aberrant expression in various types of cancer. TGFBI plays a crucial role in tumor cell proliferation, angiogenesis, and apoptosis. It also facilitates invasion and metastasis in various types of cancer, including colon, head and neck squamous, renal, and prostate cancers. TGFBI, a prominent p-EMT marker, strongly correlates with lymph node metastasis. TGFBI demonstrates immunosuppressive effects within the tumor immune microenvironment. Targeted therapy directed at TGFBI shows promise as a potential strategy to combat cancer. Hence, a comprehensive review was conducted to examine the impact of TGFBI on various aspects of tumor biology, including cell proliferation, angiogenesis, invasion, metastasis, apoptosis, and the immune microenvironment. This review also delved into the underlying biochemical mechanisms to enhance our understanding of the research advancements related to TGFBI in the context of tumors.

Differential expression analysis identifies a prognostically significant extracellular matrix-enriched gene signature in hyaluronan-positive clear cell renal cell carcinoma

Hyaluronan (HA) accumulation in clear cell renal cell carcinoma (ccRCC) is associated with poor prognosis; however, its biology and role in tumorigenesis are unknown. RNA sequencing of 48 HA-positive and 48 HA-negative formalin-fixed paraffin-embedded (FFPE) samples was performed to identify differentially expressed genes (DEG). The DEGs were subjected to pathway and gene enrichment analyses. The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) data and DEGs were used for the cluster analysis. In total, 129 DEGs were identified. HA-positive tumors exhibited enhanced expression of genes related to extracellular matrix (ECM) organization and ECM receptor interaction pathways. Gene set enrichment analysis showed that epithelial-mesenchymal transition-associated genes were highly enriched in the HA-positive phenotype. A protein-protein interaction network was constructed, and 17 hub genes were discovered. Heatmap analysis of TCGA-KIRC data identified two prognostic clusters corresponding to HA-positive and HA-negative phenotypes. These clusters were used to verify the expression levels and conduct survival analysis of the hub genes, 11 of which were linked to poor prognosis. These findings enhance our understanding of hyaluronan in ccRCC.

Transforming growth factors β and their signaling pathway in renal cell carcinoma and peritumoral space-transcriptome analysis

PURPOSE

The aim of the study was to verify hypotheses: Are transforming growth factors TGFβ1-3, their receptors TGFβI-III, and intracellular messenger proteins Smad1-7 involved in the pathogenesis of kidney cancer? What is the expression of genes of the TGFβ/Smads pathway in renal cell carcinoma (RCC) tissues, peritumoral tissues (TME; tumor microenvironment), and in normal kidney (NK) tissue?.

METHODS

Twenty patients with RCC who underwent total nephrectomy were included into the molecular analysis. The mRNA expression of the genes was quantified by RT-qPCR.

RESULTS

The study showed that the expression of the genes of TGFβ/Smads pathway is dysregulated in both RCC and the TME: TGFβ1, TGFβ3 expression is increased in the TME in comparison to the NK tissues; TGFβ2, TGFβ3, TGFβRI, TGFβRIII, Smad1, Smad2, Smad3, and Smad6 are underexpressed in RCC comparing to the TME tissues; TGFβRI, TGFβRIII, and Smad2 are underexpressed in RCC in comparison to the NK tissues.

CONCLUSION

On the one hand, the underexpression of the TGFβ signaling pathway genes within the malignant tumor may result in the loss of the antiproliferative and pro-apoptotic activity of this cytokine. On the other hand, the overexpression of the TGFβ/Smads pathway genes in the TME than in tumor or NK tissues most probably results in an immunosuppressive effect in the space surrounding the tumor and may have an antiproliferative and pro-apoptotic effect on non-neoplastic cells present in the TME. The functional and morphological consistency of this area may determine the aggressiveness of the tumor and the time in which the neoplastic process will spread.

Dysregulation of deubiquitination in breast cancer

Breast cancer (BC) is a highly frequent malignant tumor that poses a serious threat to women's health and has different molecular subtypes, histological subtypes, and biological features, which act by activating oncogenic factors and suppressing cancer inhibitors. The ubiquitin-proteasome system (UPS) is the main process contributing to protein degradation, and deubiquitinases (DUBs) are reverse enzymes that counteract this process. There is growing evidence that dysregulation of DUBs is involved in the occurrence of BC. Herein, we review recent research findings in BC-associated DUBs, describe their nature, classification, and functions, and discuss the potential mechanisms of DUB-related dysregulation in BC. Furthermore, we present the successful treatment of malignant cancer with DUB inhibitors, as well as analyzing the status of targeting aberrant DUBs in BC.

Identification of ATP6V0A4 as a potential biomarker in renal cell carcinoma using integrated bioinformatics analysis

Clear cell renal cell carcinoma (ccRCC) is the most common pathological type of renal cancer, and is associated with a high mortality rate, which is related to high rates of tumor recurrence and metastasis. The aim of the present study was to identify reliable molecular biomarkers with high specificity and sensitivity for ccRCC. A total of eight ccRCC-related expression profiles were downloaded from Gene Expression Omnibus for integrated bioinformatics analysis to screen for significantly differentially expressed genes (DEGs). Reverse transcription-quantitative (RT-q)PCR, western blotting and immunohistochemistry staining assays were performed to evaluate the expression levels of candidate biomarkers in ccRCC tissues and cell lines. In total, 255 ccRCC specimens and 165 adjacent normal kidney specimens were analyzed, and 344 significant DEGs, consisting of 115 upregulated DEGs and 229 downregulated DEGs, were identified. The results of Gene Ontology analysis suggested a significant enrichment of DEGs in 'organic anion transport' and 'small molecule catabolic process' in biological processes, in 'apical plasma membrane' and 'apical part of the cell' in cell components, and in 'anion transmembrane transporter activity' and 'active transmembrane transporter activity' in molecular functions. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that the DEGs were significantly enriched in the 'phagosome', the 'PPAR signaling pathway', 'complement and coagulation cascades', the 'HIF-1 signaling pathway' and 'carbon metabolism'. Next, 7 hub genes (SUCNR1, CXCR4, VCAN, CASR, ATP6V0A4, VEGFA and SERPINE1) were identified and validated using The Cancer Genome Atlas database. Survival analysis showed that low expression of ATP6V0A4 was associated with a poor prognosis in patients with ccRCC. Additionally, received operating characteristic curves indicated that ATP6V0A4 could distinguish ccRCC samples from normal kidney samples. Furthermore, RT-qPCR, western blotting and immunohistochemistry staining results showed that ATP6V0A4 was significantly downregulated in ccRCC tissues and cell lines. In conclusion, ATP6V0A4 may be involved in tumor progression and regarded as a potential therapeutic target for the recurrence and metastasis of ccRCC.

Analysis of genomes and transcriptomes of clear cell renal cell carcinomas identifies mutations and gene expression changes in the TGF-beta pathway

The occurrence of clear cell renal cell carcinoma (ccRCC) is related to changes in the transforming growth factor-β (TGF-β) signaling pathway. In this study, we adopted an integrated approach to identify and verify the effects of changes in this pathway on ccRCC and provide a guide for identifying new therapeutic targets. We performed transcriptome analysis of 539 ccRCC cases from The Cancer Genome Atlas (TCGA) and divided the samples into different TGF-β clusters according to unsupervised hierarchical clustering. We found that 76 of the 85 TGF-β pathway genes were dysregulated, and 55 genes were either protective or risk factors affecting the prognosis of ccRCC. The survival time of patients with tumors with low TGF-β scores was shorter than that of patients with tumors with high TGF-β scores. The overall survival (OS) of patients with ccRCC with high TGF-β scores was better than that of patients with low TGF-β scores. The TGF-β score correlated with the expression of key ccRCC and deacetylation genes. The sensitivity of tumor patients to targeted drugs differed between the high and low TGF-β score groups. Therefore, a prognostic model based on the TGF-β gene pathway can predict the prognosis of ccRCC patients. Grouping patients with ccRCC according to their TGF-β score is of great significance for evaluating the prognosis of patients, selecting targeted drugs, and identifying new therapeutic targets.

Identifying an Immune-Related Gene ST8SIA1 as a Novel Target in Patients With Clear-Cell Renal Cell Carcinoma

Clear-cell renal cell carcinoma (ccRCC) is one of the most common urological cancers. The tumor microenvironment plays an important role in tumor development. The present study was conducted to identify novel immune-related biomarkers. The differentially expressed genes were identified using the ESTIMATE algorithm base on GEO and TCGA databases. The Kaplan–Meier survival curve and univariate and multivariate analyses were performed. The association between ST8SIA1 and the immune system was explored. The gene set enrichment analysis (GSEA) and online databases were used for functional annotation. ST8SIA1 was identified as a potential prognostic gene. Elevated ST8SIA1 was observed in the tumor tissues compared with adjacent normal tissues and associated with higher T stage and advanced TNM stage (all p < 0.05). The mRNA and protein levels of ST8SIA1 in cancer tissues and cells are also upregulated. The Kaplan–Meier survival curve and univariate and multivariate analyses showed that higher expression of ST8SIA1 was associated with worse OS (all p < 0.05). ST8SIA1 expression levels were negatively correlated with tumor purity and positively associated with infiltrated immune cells and expression of immune checkpoint genes. Function analysis also revealed that ST8SIA1 was significantly associated with immune-related pathways. In conclusion, ST8SIA1 was identified as an immune-related gene and a potential target in ccRCC patients. Further relevant studies are required to validate our findings.

FMR1 is identified as an immune-related novel prognostic biomarker for renal clear cell carcinoma: A bioinformatics analysis of TAZ/YAP

WW domain-containing transcription regulator 1 (TAZ, or WWTR1) and Yes-associated protein 1 (YAP) are both important effectors of the Hippo pathway and exhibit different functions. However, few studies have explored their co-regulatory mechanisms in kidney renal clear cell carcinoma (KIRC). Here, we used bioinformatics approaches to evaluate the co-regulatory roles of TAZ/YAP and screen novel biomarkers in KIRC. GSE121689 and GSE146354 were downloaded from the GEO. The limma was applied to identify the differential expression genes (DEGs) and the Venn diagram was utilized to screen co-expressed DEGs. Co-expressed DEGs obtained the corresponding pathways through GO and KEGG analysis. The protein-protein interaction (PPI) network was constructed using STRING. The hub genes were selected applying MCODE and CytoHubba. GSEA was further applied to identify the hub gene-related signaling pathways. The expression, survival, receiver operating character (ROC), and immune infiltration of the hub genes were analyzed by HPA, UALCAN, GEPIA, pROC, and TIMER. A total of 51 DEGs were co-expressed in the two datasets. The KEGG results showed that the enriched pathways were concentrated in the TGF-β signaling pathway and endocytosis. In the PPI network, the hub genes (STAU2, AGO2, FMR1) were identified by the MCODE and CytoHubba. The GSEA results revealed that the hub genes were correlated with the signaling pathways of metabolism and immunomodulation. We found that STAU2 and FMR1 were weakly expressed in tumors and were negatively associated with the tumor stages. The overall survival (OS) and disease-free survival (DFS) rate of the high-expressed group of FMR1 was greater than that of the low-expressed group. The ROC result exhibited that FMR1 had certainly a predictive ability. The TIMER results indicated that FMR1 was positively correlated to immune cell infiltration. The abovementioned results indicated that TAZ/YAP was involved in the TGF-β signaling pathway and endocytosis. FMR1 possibly served as an immune-related novel prognostic gene in KIRC.

Application Progress of Organoids in Colorectal Cancer

Currently, colorectal cancer is still the third leading cause of cancer-related mortality, and the incidence is rising. It is a long time since the researchers used cancer cell lines and animals as the study subject. However, these models possess various limitations to reflect the cancer progression in the human body. Organoids have more clinical significance than cell lines, and they also bridge the gap between animal models and humans. Patient-derived organoids are three-dimensional cultures that simulate the tumor characteristics in vivo and recapitulate tumor cell heterogeneity. Therefore, the emergence of colorectal cancer organoids provides an unprecedented opportunity for colorectal cancer research. It retains the molecular and cellular composition of the original tumor and has a high degree of homology and complexity with patient tissues. Patient-derived colorectal cancer organoids, as personalized tumor organoids, can more accurately simulate colorectal cancer patients’ occurrence, development, metastasis, and predict drug response in colorectal cancer patients. Colorectal cancer organoids show great potential for application, especially preclinical drug screening and prediction of patient response to selected treatment options. Here, we reviewed the application of colorectal cancer organoids in disease model construction, basic biological research, organoid biobank construction, drug screening and personalized medicine, drug development, drug toxicity and safety, and regenerative medicine. In addition, we also displayed the current limitations and challenges of organoids and discussed the future development direction of organoids in combination with other technologies. Finally, we summarized and analyzed the current clinical trial research of organoids, especially the clinical trials of colorectal cancer organoids. We hoped to lay a solid foundation for organoids used in colorectal cancer research.

Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma

Glioblastoma multiforme (GBM) is the most malignant and multiple tumors of the central nervous system. The survival rate for GBM patients is less than 15 months. We aimed to uncover the potential mechanism of GBM in tumor microenvironment and provide several candidate biomarkers for GBM prognosis. In this study, ESTIMATE analysis was used to divide the GBM patients into high and low immune or stromal score groups. Microenvironment associated genes were filtered through differential analysis. Weighted gene co-expression network analysis (WGCNA) was performed to correlate the genes and clinical traits. The candidate genes’ functions were annotated by enrichment analyses. The potential prognostic biomarkers were assessed by survival analysis. We obtained 81 immune associated differentially expressed genes (DEGs) for subsequent WGCNA analysis. Ten out of these DEGs were significantly associated with targeted molecular therapy of GBM patients. Three genes (S100A4, FCGR2B, and BIRC3) out of these genes were associated with overall survival and the independent test set testified the result. Here, we obtained three crucial genes that had good prognostic efficacy of GBM and may help to improve the prognostic prediction of GBM.

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

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

Comprehensive Analysis and Identification of an Immune-Related Gene Signature with Prognostic Value for Prostate Cancer

Background

The tumor microenvironment (TME) has recently been proven to play a crucial role in the development and prognosis of tumors. However, the current knowledge on the potential of the TME in prostate cancer (PCa) remains scarce.

Purpose

This study aims to elucidate the value of TME-related genes for PCa prognosis by integrative bioinformatics analysis.

Materials and Methods

We downloaded the immune and stromal scores of PCa samples via the ESTIMATE and correlated these scores to clinicopathological characteristics and recurrence-free survival (RFS) of patients. Based on these scores, the TME-related differentially expressed genes were identified for functional enrichment analysis. Cox regression analyses were performed to identify prognostic genes and establish a predictive risk model. Moreover, gene set enrichment analysis (GSEA) was performed to evaluate the relationship between risk score and immune pathway.

Results

The stromal and immune scores were associated with clinicopathological characteristics and RFS in PCa patients. In total, 238 intersecting differentially expressed genes were identified. Functional enrichment analysis further revealed that these genes dramatically participated in the immune-related pathways. The immune-related risk model was built with C-type lectin domain containing 7A (CLEC7A) and collagen type XI alpha 1 chain (COL11A1) using Cox regression analyses. Kaplan–Meier survival analysis showed that the expression levels of CLEC7A and COL11A1 were significantly associated with the RFS. Further, the RFS time in high-risk group was significantly shorter than that in low-risk group. The areas under the curve for the risk model in predicting 3- and 5-year RFS rates were 0.694 and 0.731, respectively. GSEA suggested that immunosuppression existed in high-risk PCa patients.

Conclusion

CLEC7A and COL11A1 were selected to build a predictive risk model, which may help clinicians to assess the prognosis of PCa patients and select appropriate targets for immunotherapy.

A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes

Objective

To screen glycolytic genes linked to the glioma prognosis and construct the prognostic model.

Methods

The relevant data of glioma were downloaded from TCGA and GTEx databases. GSEA of glycolysis-related pathways was carried out, and enriched differential genes were extracted. Screening out prognostic-related genes with conspicuous significance and construction of the prognostic model were conducted by multivariate Cox regression analysis and Lasso regression analysis. The model was evaluated, and cBioPortal was used to analyze the mutation of the model gene. The expression of the model gene in tumor and normal colon tissue was analyzed. The model was used to evaluate the prognosis of patients in different groups to verify the applicability of the model.

Results

339 differentially glycolytic-related genes were enriched in REACTOME_GLYCOLYSIS, GLYCOLYTIC_PROCESS, HALLMARK_GLYCOLYSIS, and other pathways. We obtained 9 key prognostic genes and constructed the prognostic evaluation model. The 3-year AUC values of the ROC curve display model are greater than 0.75, which indicates that the accuracy of the model is good. The relation of age and risk score to prognosis is shown by univariate and multivariate Cox analysis. The expression of SRD5A3, MDH2, and B3GAT3 genes was significantly upregulated in the tumor tissues, while the HDAC4 and G6PC2 genes were downregulated. The mutation rate of MDH2 and HDAC4 genes was the highest. This model could effectively distinguish the risk of poor prognosis of patients in any age stage.

Conclusion

The prognostic assessment models based on glycolysis-related nine-gene signature could accurately predict the prognosis of patients with GBM.

Proteomics of extracellular vesicles in plasma reveals the characteristics and residual traces of COVID-19 patients without underlying diseases after 3 months of recovery

More and more patients suffered from Coronavirus disease 2019 (COVID-19) have got recovery gradually due to suitable intervention. Increasing data mainly studies the clinical characteristics of recovered COVID-19 patients, and their molecular changes especially proteome changes also play the same important role in understanding of biological characteristics of recovered COVID-19 patients as clinical characteristics do. In our study, we reported the whole lung-ground glass-CT value-average of mild/severe recovered patients 3 months after discharge without underlying diseases was significantly lower than that of healthy subjects. Then we isolated the extracellular vesicles (EVs) of plasma from 19 healthy subjects and 67 recovered COVID-19 patients. Mass Spectrometry was used to catalogue the proteins of these EVs compared to a defined group of controls. Identified 174 proteins were differentially expressed in the EVs of COVID-19 patients compared with healthy subjects, which involved in lipid metabolic process, response to cellular, and response to stress oxygen-containing compound. Besides, we identified several protein of plasma EVs in recovered patients associated with coagulation activity, inflammatory reaction, immune response, and low organ function. In addition, proteins correlating with clinical index such as alkaline phosphatase (ALP) and alanine aminotransferase (ALT) were also detected. Moreover, we also identified many unique or characteristic associations found in the recovered COVID-19 patients, which especially involved the kidney, serum electrolyte levels, and inflammation functions. This finding suggests that monitoring the situation of recovered patients might be useful, especially the indexes of coagulation, inflammation, immunity, and organ function, which can prevent bleeding, reinfection and organ dysfunction.

Development and validation of the prognostic value of the immune-related genes in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is a highly heterogeneous tumor, resulting a challenge of developing target therapeutics. Not long ago, immune checkpoint blockade regimens combine with tyrosin kinase inhibitors have evolved frontline options in metastatic RCC, which implies arrival of the era of tumor immunotherapy. Studies have demonstrated immune-related genes (IRGs) could characterize tumor milieu and related to patient survival. Nevertheless, the clinical significance of classifier depending on IRGs in ccRCC has not been well established.

Methods

The R package limma, univariate and LASSO cox regression analysis were used to screen the prognostic related IRGs from TCGA database. Multivariate cox regression was utilized to establish a risk prediction model for candidate genes. Quantitative real-time PCR was used to confirm the expression of candidates in clinical samples from our institution. CIBERSORT algorithm and correlation analysis were applied to explore tumor-infiltrating immune cells signature between different risk groups. A clinical nomogram was also developed to predict OS by using the rms R package based on the risk prediction model and other independent risk factors. The ICGC data was used for external validation of either gene risk model or nomogram.

Results

We identified 382 differentially expressed immune related genes. Four unique prognostic IRGs (CRABP2, LTB4R, PTGER1 and TEK) were finally affirmed to associate with tumor survival independently and utilized to establish the risk score model. All candidates’ expression was successfully laboratory confirmed by q-PCR. CIBERSORT analysis implied patients in unfavorable-risk group with high CD8 T cell, regulatory T cell and NK cell infiltration, as well as high expression of PD-1, CTLA4, TNFRSF9, TIGIT and LAG3. A nomogram combined IRGs risk score with age, gender, TNM stage, Fuhrman grade, necrosis was further generated to predict of 3- and 5-year OS, which exhibited superior discriminative power (AUCs were 0.811 and 0.795).

Conclusions

Our study established and validated a survival prognostic model system based on 4 unique immune related genes in ccRCC, which expands knowledge in tumor immune status and provide a potent prediction tool in future.

Global Characterization of Immune Infiltration in Clear Cell Renal Cell Carcinoma

Background

Immunotherapy has revolutionized the treatment of clear cell renal cell carcinoma (ccRCC). However, the therapy is constrained by drug resistance. Therefore, further characterization of immune infiltration in ccRCC is needed to improve its efficacy.

Methods

Here, we adopted the CIBERSORT method to analyze the level of 22 immune cells, and analyzed the correlation of immune cells and clinical parameters in ccRCC in The Cancer Genome Atlas. We used consensus clustering to cluster ccRCC and identified differently expressed genes (DEGs) between hot and cold tumors using the "Limma" package, and then performed enrichment analysis of DEGs. Finally, we constructed and validated a Cox regression model using the "survival", "glmnet", and "survivalROC" packages, implemented in R.

Results

Regulatory T cells upregulated in tumor tissue increased during tumor progression, and correlated with poor overall survival in ccRCC. Consensus clustering identified four clusters of ccRCC. To elucidate the underlying mechanisms of immune cell infiltration, we subdivided these four clusters into two major types, immune hot and cold, and identified DEGs between them. The results revealed different transcription profiles in the two tumor types, with hot tumors being enriched in immune-related signaling, whereas cold tumors were enriched in extracellular matrix remodeling and the phosphatidylinositol 3-kinase-AKT (PI3K/AKT) pathway. We further identified hub genes and prognostic-related genes from the DEGs, and constructed a Cox regression model for predicting the overall survival of patients with ccRCC. The areas under the receiver operating characteristics curve for the risk model for the training, testing, and external Zhengzhou validation cohorts were 0.834, 0.733, and 0.812, respectively. Notably, gene sets in the prediction model could also predict the overall survival of patients receiving immunotherapy.

Conclusion

These findings provide a comprehensive characterization of immune infiltration in ccRCC, while the constructed model can be used effectively to predict the overall survival of ccRCC patients.

Identification of a three-miRNA signature as a novel prognostic model for papillary renal cell carcinoma

Background

Papillary renal cell carcinoma (pRCC) accounting for near 20% of renal cell carcinoma is the second most common histological subtype. MiRNAs have been demonstrated to played significant roles on predicting prognosis of patients with tumors. An appropriate and comprehensive miRNAs analysis based on a great deal of pRCC samples from The Cancer Genome Atlas (TCGA) will provide perspective in this field.

Methods

We integrated the expression of mRNAs, miRNAs and the relevant clinical data of 321 pRCC patients recorded in the TCGA database. The survival-related differential expressed miRNAs (sDEmiRs) were estimated by COX regression analysis. The high-risk group and the low-risk group were separated by the median risk score of the risk score model (RSM) based on three screened sDEmiRs. The target genes, underlying molecular mechanisms of these sDEmiRs were explored by computational biology. The expression levels of the three sDEmiRs and their correlations with clinicopathological parameters were further validated by qPCR.

Results

Based on univariate COX analysis (P < 0.001), eighteen differential expressed miRNAs (DEmiRs) were remarkably related with the overall survival (OS) of pRCC patients. Three sDEmiRs with the most significant prognostic values (miR-34a-5p, miR-410-3p and miR-6720-3p) were employed to establish the RSM which was certified as an independent prognosis factor and closely correlated with OS. In the verification of clinical samples, the overexpression of miR-410-3p and miR-6720-3p were detected to be associated with the advanced T-stages, while miR-34a-5p showed the reversed results.

Conclusion

The study developed a RSM based on the identified sDEmiRs with significant prognosis prediction values for pRCC patients. The results pave the avenue for establishing and optimizing a reliable and referable risk assessing model and provide novel insight into the researches of biomarkers and clinical treatment strategies.