Identification of MX2 as a Novel Prognostic Biomarker for Sunitinib Resistance in Clear Cell Renal Cell Carcinoma

Wei, Yuang; Chen, Xinglin; Ren, Xiaohan; Wang, Bao; Zhang, Qian; Bu, Hengtao; Qian, Ji; Shao, Pengfei

doi:10.3389/fgene.2021.680369

Cited by 34 publications

(34 citation statements)

References 39 publications

(43 reference statements)

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“…However, despite the adoption of multiple treatment modes, no significant progress was made to improve the prognosis of patients with ccRCC during the 15-year study period, and drug intolerance and many side effects are still inevitable. 5 , 27 , 30 , 31 This highlights the urgency of continuing to develop new and more specific treatments to improve the prognosis of ccRCC.…”

Section: Discussionmentioning

confidence: 99%

Analysis and Prediction of the Survival Trends of Patients with Clear-Cell Renal Cell Carcinoma: A Model-Based Period Analysis, 2001-2015

Zhong

Zheng

et al. 2022

Cancer Control

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Background Clear-cell renal cell carcinoma (ccRCC) is one of the most common malignant tumors worldwide whose poor prognosis results in a serious disease burden on patients. The changing trend of the long-term relative survival rates (RSRs) of patients with ccRCC was analyzed in this study to evaluate their treatment results over a 15-year period. Methods This study is a retrospective study, which assessed and predicted the 1-, 3-, and 5-year survival rates of patients with ccRCC during 2001-2005, 2006-2010, 2011-2015, and 2016-2020 using data extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Period analysis was used in this study to analyze the data from the SEER database and to assess survival differences according to age, sex, race, and socioeconomic status (SES) during the 15-year study period by comparing Kaplan-Meier curves. Results During 2001-2015, the 5-year RSR of patients with ccRCC increased from 78.4% to 83.0%, and the generalized linear model predicted that the 5-year RSR increased to 85.7% during 2016-2020. The RSR of patients with ccRCC differed significantly with SES, race, sex, and age. Compared with male patients, the survival advantage of female patients decreased as their age increased. The RSR of all patients with ccRCC was also lower in patients with a lower SES and of black race. Conclusion This study found an improvement in the RSR of patients with ccRCC during 2001-2020. Understanding the change trend of the survival rate of patients with ccRCC is helpful to improve the design of clinical trials. It also provides basic data and a scientific basis for evaluating the harm of ccRCC on the health of affected patients and the effect of cancer prevention, and developing cancer prevention plans.

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Section: Discussionmentioning

confidence: 99%

Analysis and Prediction of the Survival Trends of Patients with Clear-Cell Renal Cell Carcinoma: A Model-Based Period Analysis, 2001-2015

Zhong

Zheng

et al. 2022

Cancer Control

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“…Control and knockdown plasmids were purchased from synthesized by Shanghai Jima Biotechnology. Cell transfection was conducted according to the standard process ( 24 ).…”

Section: Methodsmentioning

confidence: 99%

Machine learning identifies characteristics molecules of cancer associated fibroblasts significantly correlated with the prognosis, immunotherapy response and immune microenvironment in lung adenocarcinoma

Wang

Zhang²,

Du³

et al. 2022

Front. Oncol.

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BackgroundLung adenocarcinoma (LUAD) is a highly lethal disease with a dramatic pro-fibrocytic response. Cancer-associated fibroblasts (CAFs) have been reported to play a key role in lung adenocarcinoma.MethodsMarker genes of CAFs were obtained from the Cell Marker website. Single sample gene set enrichment analysis (ssGSEA) was used for CAFs quantification. R and GraphPad Prism software were utilized for all analysis. Quantitative real-time PCR (qRT-PCR) was utilized to detect the RNA level of specific molecules.ResultsBased on the ssGSEA algorithm and obtained CAFs markers, the LUAD patients with low- and high-CAFs infiltration were successfully identified, which had different response patterns to immunotherapy. Through the machine learning algorithm – LASSO logistic regression, we identified 44 characteristic molecules of CAFs. Furthermore, a prognosis signature consisting of seven characteristic genes was established, which showed great prognosis prediction ability. Additionally, we found that patients in the low-risk group might have better outcomes when receiving immunotherapy of PD-1, but not CTLA4. Also, the biological enrichment analysis revealed that immune response-related pathways were significantly associated with CAFs infiltration. Meanwhile, we investigated the underlying biological and microenvironment difference in patients with high- and low-risk groups. Finally, we identified that AMPD1 might be a novel target for LUAD immunotherapy. Patients with a high level of AMPD1 were correlated with worse responses to immunotherapy. Moreover, immunohistochemistry showed that the protein level of AMPD1 was higher in lung cancer. Results of qRT-PCR demonstrated that AMPD1 was upregulated in A549 cells compared with BEAS-2B. Meanwhile, we found that the knockdown of AMPD4 can significantly reduce the expression of CTLA4 and PDCD1, but not CD274 and PDCD1LG2.ConclusionWe comprehensively explored the role of CAFs and its characteristics molecules in LUAD immunotherapy and developed an effective signature to indicate patients prognosis and immunotherapy response. Moreover, AMPD1 was identified as a novel target for lung cancer immunotherapy.

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“…We used the R package “clusterprofiler” to conduct GSEA on hub genes using sequencing data [ 20 ]. Furthermore, the “GSVA” R program was utilized to identify the pathways most closely associated with hub genes [ 21 ].…”

Section: Identification Of Robust Degsmentioning

confidence: 99%

Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis

Fan

Jin

Wang

et al. 2021

BMC Cardiovasc Disord

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Background The pathogenic mechanisms of venous thromboembolism (VT) remain to be defined. This study aimed to identify differentially expressed genes (DEGs) that could serve as potential therapeutic targets for VT. Methods Two human datasets (GSE19151 and GSE48000) were analyzed by the robust rank aggregation method. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were conducted for the DEGs. To explore potential correlations between gene sets and clinical features and to identify hub genes, we utilized weighted gene coexpression network analysis (WGCNA) to build gene coexpression networks incorporating the DEGs. Then, the levels of the hub genes were analyzed in the GSE datasets. Based on the expression of the hub genes, the possible pathways were explored by gene set enrichment analysis and gene set variation analysis. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis in the GEO database. Results In this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples. After performing WGCNA, the magenta module was the module with the strongest negative correlation with the clinical characteristics. From the key module, FECH, GYPA, RPIA and XK were chosen for further validation. We found that these genes were upregulated in VT samples, and high expression levels were related to recurrent VT. Additionally, the four hub genes might be highly correlated with ribosomal and metabolic pathways. The ROC curves suggested a diagnostic value of the four genes for VT. Conclusions These results indicated that FECH, GYPA, RPIA and XK could be used as promising biomarkers for the prognosis and prediction of VT.

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Identification of MX2 as a Novel Prognostic Biomarker for Sunitinib Resistance in Clear Cell Renal Cell Carcinoma

Cited by 34 publications

References 39 publications

Analysis and Prediction of the Survival Trends of Patients with Clear-Cell Renal Cell Carcinoma: A Model-Based Period Analysis, 2001-2015

Analysis and Prediction of the Survival Trends of Patients with Clear-Cell Renal Cell Carcinoma: A Model-Based Period Analysis, 2001-2015

Machine learning identifies characteristics molecules of cancer associated fibroblasts significantly correlated with the prognosis, immunotherapy response and immune microenvironment in lung adenocarcinoma

Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis

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