scholarly journals A united risk model of 11 immune‑related gene pairs and clinical stage for prediction of overall survival in clear cell renal cell carcinoma patients

Bioengineered ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 4259-4277
Author(s):  
Zijia Tao ◽  
Enchong Zhang ◽  
Lei Li ◽  
Jianyi Zheng ◽  
Yiqiao Zhao ◽  
...  
2021 ◽  
Vol 11 ◽  
Author(s):  
Yongying Zhou ◽  
Xiao Wang ◽  
Weibing Zhang ◽  
Huiyong Liu ◽  
Daoquan Liu ◽  
...  

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney tumor worldwide. Analysis of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases showed that the immune-related gene (IRG) hematopoietic cell signal transducer (HCST) could provide guidance for the diagnosis, prognosis, and treatment of ccRCC. The RNA-seq data of ccRCC tissues were extracted from two databases: TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) and GEO (https://www.ncbi.nlm.nih.gov/geo/). Corresponding clinical information was downloaded from TCGA. Immune-related gene data were extracted from the IMMPORT website (https://www.immport.org/). Differential analysis with R software (https://www.r-project.org/) was used to obtain a prognosis model of ccRCC IRGs. The differences were combined with the clinical data to assess the usefulness of the HCST as a prognostic biomarker. Based on data obtained from the Oncomine (https://www.oncomine.org/), Human Protein Atlas (https://www.proteinatlas.org/), and PubMed (https://pubmed.ncbi.nlm.nih.gov/) databases, the expression levels of the HCST in ccRCC, clinical-pathological indicators of relevance, and influence on prognosis were analyzed. Regulation of the HCST gene in ccRCC was assessed by gene set enrichment analysis (GSEA). In TCGA/GEO databases, the high HCST expression in tumor tissues was significantly correlated to the TMN stage, tumor grade, invasion depth, and lymphatic metastasis (p < 0.05). The overall survival (OS) of patients with high HCST gene expression was significantly lower than that of patients with low HCST gene expression (p < 0.001). Multivariate Cox regression analysis suggested that the HCST expression level [hazard ratio (HR) = 1.630, 95% confidence interval (CI) = 1.042–2.552], tumor cell grade (HR = 1.829, 95% CI = 1.115–3.001), and distant metastasis (HR = 2.634, 95%, CI = 1.562–4.442) were independent risk factors affecting the OS of ccRCC patients (all, p < 0.05). The GSEA study showed that there was significant enrichment in cell adhesion, tumorigenesis, and immune and inflammatory responses in HCST high expression samples. Hematopoietic cell signal transducer expression was closely associated with the levels of infiltrating immune cells around ccRCC tissues, especially dendritic cells (DCs). In conclusion, the present study suggested that the HCST was interrelated to the clinicopathology and poor prognosis of ccRCC. High HCST expression was also closely correlated with the levels of tumor-infiltrating immune cells, especially DCs.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jazmine Arévalo ◽  
David Lorente ◽  
Enrique Trilla ◽  
María Teresa Salcedo ◽  
Juan Morote ◽  
...  

AbstractClear cell renal cell carcinoma (ccRCC) is the most frequent and aggressive subtype of renal carcinoma. So far, the basis of its oncogenesis remains unclear resulting in a deficiency of usable and reliable biomarkers for its clinical management. Previously, we showed that nuclear expression of the signal transducer and activator of transcription 3 (STAT3), phosphorylated at its serine 727 (pS727), was inversely proportional to the overall survival of ccRCC patients. Therefore, in the present study, we validated the value of pS727-STAT3 as a clinically relevant biomarker in ccRCC. This work is a retrospective study on 82 ccRCC patients treated with nephrectomy and followed-up for 10 years. Immunohistochemical expression of pS727-STAT3 was analyzed on a tissue microarray and nuclear and cytosolic levels were correlated with clinical outcome of patients. Our results showed that pS727-STAT3 levels, whether in the nucleus (p = 0.002; 95% CI 1.004–1.026) or the cytosol (p = 0.040; 95% CI 1.003–1.042), significantly correlate with patients’ survival in an independent-manner of clinicopathological features (Fuhrman grade, risk group, and tumor size). Moreover, we report that patients with high pS727-STAT3 levels who undergone adjuvant therapy exhibited a significant stabilization of the disease (~ 20 months), indicating that pS727-STAT3 can pinpoint a subset of patients susceptible to respond well to treatment. In summary, we demonstrated that high pS727-STAT3 levels (regardless of their cellular location) correlate with low overall survival of ccRCC patients, and we suggested the use of pS727-STAT3 as a prognostic biomarker to select patients for adjuvant treatment to increase their survival.


2018 ◽  
Vol 16 (2) ◽  
pp. e297-e305 ◽  
Author(s):  
Antoine Thiery-Vuillemin ◽  
Tiphaine Cholley ◽  
Fabien Calcagno ◽  
Marion Hugues ◽  
Tristan Maurina ◽  
...  

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8096 ◽  
Author(s):  
Haiping Zhang ◽  
Jian Zou ◽  
Ying Yin ◽  
Bo Zhang ◽  
Yaling Hu ◽  
...  

Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset GSE53757 was downloaded from the Gene Expression Omnibus database. The GSE53757 dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein–protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.


BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qianwei Xing ◽  
Tengyue Zeng ◽  
Shouyong Liu ◽  
Hong Cheng ◽  
Limin Ma ◽  
...  

Abstract Background The role of glycolysis in tumorigenesis has received increasing attention and multiple glycolysis-related genes (GRGs) have been proven to be associated with tumor metastasis. Hence, we aimed to construct a prognostic signature based on GRGs for clear cell renal cell carcinoma (ccRCC) and to explore its relationships with immune infiltration. Methods Clinical information and RNA-sequencing data of ccRCC were obtained from The Cancer Genome Atlas (TCGA) and ArrayExpress datasets. Key GRGs were finally selected through univariate COX, LASSO and multivariate COX regression analyses. External and internal verifications were further carried out to verify our established signature. Results Finally, 10 GRGs including ANKZF1, CD44, CHST6, HS6ST2, IDUA, KIF20A, NDST3, PLOD2, VCAN, FBP1 were selected out and utilized to establish a novel signature. Compared with the low-risk group, ccRCC patients in high-risk groups showed a lower overall survival (OS) rate (P = 5.548Ee-13) and its AUCs based on our established signature were all above 0.70. Univariate/multivariate Cox regression analyses further proved that this signature could serve as an independent prognostic factor (all P < 0.05). Moreover, prognostic nomograms were also created to find out the associations between the established signature, clinical factors and OS for ccRCC in both the TCGA and ArrayExpress cohorts. All results remained consistent after external and internal verification. Besides, nine out of 21 tumor-infiltrating immune cells (TIICs) were highly related to high- and low- risk ccRCC patients stratified by our established signature. Conclusions A novel signature based on 10 prognostic GRGs was successfully established and verified externally and internally for predicting OS of ccRCC, helping clinicians better and more intuitively predict patients’ survival.


2019 ◽  
Author(s):  
Yang Qu ◽  
Jiajun Wang ◽  
Qi Bai ◽  
Yangyang Qi ◽  
Yifan Chen ◽  
...  

Abstract Background: Little is known about the associations between PAK1 and anti-tumor immunity in clear-cell renal cell carcinoma (ccRCC). This study aims to explore the prognostic value of PAK1 in ccRCC patients and investigated the molecular immune mechanism for its oncogenic role. Methods: We retrospectively enrolled 282 ccRCC patients undergoing nephrectomy between 2005 and 2007 in Zhongshan hospital. Immunohistochemistry evaluated PAK1, CCL22, FOXP3 and CD8 expression in clinical specimens. Fresh tumor tissues, para-tumor tissues and peripheral blood samples for RT-PCR, ELISA, flow cytometry analyses were collected from patients who underwent nephrectomy in Zhongshan Hospital from September 2017 to April 2018. We compared clinical outcomes by Kaplan-Meier survival analysis and Cox regression model. Bioinformatics analysis performed in TCGA KIRC cohort. Results: High PAK1 expression indicated poorer overall survival (OS) and recurrence free survival (RFS) (both p<0.001) in ccRCC patients. Multivariate analyses indicated PAK1 as an independent prognostic factor. In clinical samples, PAK1 clearly correlated with immunosuppressive microenvironment in ccRCC tissues. Significantly, PAK1 positively correlated with Tumor-infiltrating regulatory T lymphocytes (Tregs). Furthermore, IL-10+ and TGF-β+ tumor-infiltrating Tregs both increased in PAK1 high tumors. Additionally, CCL22 was highly secreted in PAK1 high tumors. After treated by IPA-3 (an PAK1 inhibitor), CCL22 secretion was clearly inhibited (p<0.001). Finally, we built a nomogram to predict overall survival for ccRCC patients with higher predictive accuracy. Conclusions: Increased PAK1 expression predicted dismal prognosis in ccRCC patients by inducing tumor immune escape. IL-10+ and TGF-β+ tumor-infiltrating Tregs recruited by CCL22 play dominant immunosuppressive roles in PAK1 high tumors.


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