Decreased SPTLC1 expression predicts worse outcomes in ccRCC patients

Zhu, Wen; Xu, Wen; Wang, Jun; Huang, Yong; Abudurexiti, Mierxiati; Qu, Yuan; Zhu, Yi Ping; Zhang, Hai Liang; Ye, Ding Wei

doi:10.1002/jcb.29390

Cited by 23 publications

(25 citation statements)

References 36 publications

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“…The incidence of RCC has increased steadily by about 1% per year, and the high mortality rate remains unchanged worldwide (18). Despite substantial improvements in available treatment options, the 5-year survival rate for advanced/metastatic RCC is <23% (19).…”

Section: Discussionmentioning

confidence: 99%

The Diagnostic and Immunotherapeutic Value of CD248 in Renal Cell Carcinoma

Zhang

et al. 2021

Front. Oncol.

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Background: Renal cell carcinoma (RCC) is the most common malignancy in the urinary system. Despite substantial improvements in available treatment options, the survival outcome of advanced RCC is unsatisfactory. Identifying novel biomarkers to assist in early diagnosis and to screen patients who are sensitive to immunotherapy would be beneficial. CD248 is a promising candidate that deserves to be investigated.Methods: The Cancer Genome Atlas (TCGA) data set and clinical specimens were adopted to analyze the expression of CD248 between normal and tumor tissues. Univariate and multivariate Cox regression analyses were employed to identify independent prognostic factors and construct a CD248-based prognostic signature. The correlation among the present signature, tumor-infiltrating immune cells (TIICs), the tumor mutation burden (TMB), and immunomodulatory molecules was evaluated. The weighted gene co-expression network analysis (WGCNA), the enrichment analysis, and the miRNA correlation analysis were performed to explore the underlying mechanism of CD248 in the progression of RCC.Results: The overexpression of CD248 in RCC was related to a poor prognosis, and a CD248-based prognostic signature could precisely stratify patients with RCC with different survival outcomes regardless of the training or testing cohort. The present signature could reflect the immunosuppressive landscape of RCC (i.e., increased infiltration of regulatory T cells and upregulated immune checkpoints), accompanied by deteriorated clinicopathologic indexes. The TMB and immunostimulatory molecules expression also increased with the risk score generated from the present signature. CD248 co-expressed gene sets were identified through the WGCNA algorithm, and several immunosuppressive Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly enriched. The result of CD248-correlated miRNA further emphasized the importance of CD248 in RCC.Conclusion: CD248 is a valuable biomarker to improve the diagnostic and therapeutic efficiency of RCC. The immunosuppressive effect of CD248 co-expressed genes may provide insight for the present study, and miRNA would help to reveal the mechanism of the expressive regulation of CD248.

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Section: Discussionmentioning

confidence: 99%

The Diagnostic and Immunotherapeutic Value of CD248 in Renal Cell Carcinoma

Zhang

et al. 2021

Front. Oncol.

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“…As a component of eukaryotic membranes, SLs has a variety of critical functions in the growth and development of embryos and the maintenance of normal physiology (53). In a clear cell renal cell carcinoma (ccRCC) study, bioinformatics analysis revealed that 10 genes, including SPTSSA, significantly coregulated ccRCC with SPTLC1, and the low expression of SPTLC1 was significantly correlated with the disease progression and poor prognosis of ccRCC (54). In addition, another gene ARSF, which is located in Xp22.3 with ARSD and ARSE, has significant homology and highly similar intron/exon structure.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis

Wang

Xue

et al. 2020

Front. Oncol.

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Altered metabolism of glucose, lipid and glutamine is a prominent hallmark of cancer cells. Currently, cell heterogeneity is believed to be the main cause of poor prognosis of glioblastoma (GBM) and is closely related to relapse caused by therapy resistance. However, the comprehensive model of genes related to glucose-, lipid-and glutamine-metabolism associated with the prognosis of GBM remains unclear, and the metabolic heterogeneity of GBM still needs to be further explored. Based on the expression profiles of 1,395 metabolism-related genes in three datasets of TCGA/CGGA/GSE, consistent cluster analysis revealed that GBM had three different metabolic status and prognostic clusters. Combining univariate Cox regression analysis and LASSO-penalized Cox regression machine learning methods, we identified a 17-metabolism-related genes risk signature associated with GBM prognosis. Kaplan-Meier analysis found that obtained signature could differentiate the prognosis of high-and low-risk patients in three datasets. Moreover, the multivariate Cox regression analysis and receiver operating characteristic curves indicated that the signature was an independent prognostic factor for GBM and had a strong predictive power. The above results were further validated in the CGGA and GSE13041 datasets, and consistent results were obtained. Gene set enrichment analysis (GSEA) suggested glycolysis gluconeogenesis and oxidative phosphorylation were significantly enriched in high-and low-risk GBM. Lastly Connectivity Map screened 54 potential compounds specific to different subgroups of GBM patients. Our study identified a novel metabolism-related gene signature, in addition the existence of three different metabolic status and two opposite biological processes in GBM were recognized, which revealed the metabolic heterogeneity of GBM. Robust metabolic subtypes and powerful risk prognostic models contributed a new perspective to the metabolic exploration of GBM.

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“…It is the third most common cancer in the urinary system, and it has the maximum fatality rate. ccRCC is highly invasive, nearly 60% patients present metastasis at the procedure of diagnosis and therapy [19] . Meanwhile, metastatic cancer is not sensitive to chemotherapy and radiotherapy [20] .…”

Section: Discussionmentioning

confidence: 99%

A novel prognostic predictor for clear cell renal cell carcinoma constructed by weighted gene co-expression correlation network analysis

Li¹,

Jin²,

Guo³

et al. 2020

Preprint

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Background Clear cell renal cell carcinoma (ccRCC) accounts the largest proportion of all types of renal tumor, it is highly invasive leading to shorter survival time of patients suffer from tumor. This study aims at constructing a new prognostic signature to provide a new reference for clinic work to adjust treatment protocols. Material and Methods Comprehensive gene expression profiles of ccRCC were downloaded from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA), a genome-wide profiling analysis, was applied to investigate key genes associated with clinical traits. Cox regression analysis was conducted to screen prognostic gene in ccRCC. LASSO analysis was used to enhance the robust of correlated genes. Then, a prognostic signature was developed as an independent factor to predict outcomes of ccRCC patients. Results The co-expression network was constructed by WGCNA, 26 modules were identified in total, including a module (grey) showed no significance. By associating the features with clinic features, we selected three modules (pink, purple and turquoise) significantly associated with the overall survival(OS) of ccRCC patients. Univariate Cox analysis for genes from each module separately and then we used LASSO regression to screen the significant genes for selecting potential prognosis element-genes. 8 genes (CAPRIN2, IFI44, LTV1, ZNF320, MTHFR, XPOT, BCL3 and PAX2) finally reserved by multivariate COX regression. ScoreW was defined as a prognosis modelestablished by the final potential prognosis 8 genes. Multivariate regression suggested the ScoreW was an independent prognosis predicting element. ROC curve method was performed and the AUC value of ScoreW showed satisfied and attractive clinical prognosis importance. Conclusion Our findings provided the framework of co-expression gene modules of ccRCC and identified valuable prognostic method might be detection for ccRCC patients.

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Decreased SPTLC1 expression predicts worse outcomes in ccRCC patients

Cited by 23 publications

References 36 publications

The Diagnostic and Immunotherapeutic Value of CD248 in Renal Cell Carcinoma

The Diagnostic and Immunotherapeutic Value of CD248 in Renal Cell Carcinoma

Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis

A novel prognostic predictor for clear cell renal cell carcinoma constructed by weighted gene co-expression correlation network analysis

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