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. 2021 Sep 29:9:686907.
doi: 10.3389/fcell.2021.686907. eCollection 2021.

Development and Validation of an IL6/JAK/STAT3-Related Gene Signature to Predict Overall Survival in Clear Cell Renal Cell Carcinoma

Chuanchuan Zhan  1 , Chao Xu  1 , Jiajun Chen  1 , Chong Shen  1 , Jinkun Li  2 , Zichu Wang  2 , Xiangrong Ying  1 , Zhengang Luo  1 , Yu Ren  1 , Gangfeng Wu  1 , Haojie Zhang  1 , Manfei Qian  1
Affiliations

Development and Validation of an IL6/JAK/STAT3-Related Gene Signature to Predict Overall Survival in Clear Cell Renal Cell Carcinoma

Chuanchuan Zhan et al. Front Cell Dev Biol. .

Abstract

Background: Traditional clinicopathological features (TNM, pathology grade) are often insufficient in predictive prognosis accuracy of clear cell renal cell carcinoma (ccRCC). The IL6-JAK-STAT3 pathway is aberrantly hyperactivated in many cancer types, and such hyperactivation is generally associated with a poor clinical prognosis implying that it can be used as a promising prognosis indicator. The relation between the IL6-JAK-STAT3 pathway and ccRCC remains unknown. Methods: We evaluated the levels of various cancer hallmarks and filtered out the promising risk hallmarks in ccRCC. Subsequently, a prognosis model based on these hallmark-related genes was established via weighted correlation network analysis and Cox regression analysis. Besides, we constructed a nomogram based on the previous model with traditional clinicopathological features to improve the predictive power and accuracy. Results: The IL6-JAK-STAT3 pathway was identified as the promising risk hallmarks in ccRCC, and the pathway-related prognosis model based on five genes was built. Also, the nomogram we developed demonstrated the strongest and most stable survival predictive ability. Conclusion: Our study would provide new insights for guiding individualized treatment of ccRCC patients.

Keywords: IL6-JAK-STAT3; WGCNA; immune; nomogram; prognosis; renal cell carcinoma.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The main flowchart of the study. (A) Identified the candidate risk hallmarks in ccRCC. (B) Picked out the genes closely related to the candidate risk hallmarks. (C) Constructed and validated a candidate risk hallmarks-related signature. (D) A nomogram can perform well in predictive prognosis in ccRCC.
FIGURE 2
FIGURE 2
IL6-JAK-STAT3 pathway was identified as candidate risk hallmarks. (A) The results of multivariable Cox regression analysis. p-values were shown as: *p < 0.05; **p < 0.01; ***p < 0.001. (B) The high Z-score group had a worse outcome than the low Z-score group.(C) The enrichment score of the IL6-JAK-STAT3 pathway was significantly different between normal kidney tissue and ccRCC patients. The same difference was also found between alive and dead patients. (D) GSEA results between high Z-score and low Z-score groups.
FIGURE 3
FIGURE 3
Picked out candidate risk hallmark-related genes and constructed a prognosis model. (A) Genes with similar expression patterns were clustered into the same module. (B) The relationship between module and clinical traits. The MEcyan module was significant with the IL6-JAK-STAT3 pathway most with a score of 0.8 and a p-value of 4e-114. (C) A scatterplot of gene significance (GS) for the IL6-JAK-STAT3 pathway vs. Module membership (MM) in the MEcyan module. (D) LASSO Cox regression analysis for OS of five pathway-related genes in ccRCC. p-values were shown as: **p < 0.01; ***p < 0.001. (E) Genes with their coefficient score in LASSP Cox regression analysis.
FIGURE 4
FIGURE 4
Evaluating pathway-related prognosis model in ccRCC with different perspectives and cohorts. (A) A Survival analysis with different risk score groups in the training cohort. (B) The ROC curves in the training cohort. (C) The boxplot of risk score between Grade 1 to Grade 2 and Grade 3 to Grade 4 in the testing group. (D) The results of univariate Cox analysis for OS in ccRCC. (E) Survival analysis with different risk score groups in the validation cohort. (F) The ROC curves in the validation cohort. (G) GSEA results between high-risk score and low-risk score groups. (H) The results of multivariate Cox analysis for OS in ccRCC.
FIGURE 5
FIGURE 5
The relation between the IL6-JAK-STAT3 pathway and tumor microenvironment. p-values were shown as: ns, not significant; *p < 0.05; ***p < 0.001. (A) The boxplot of ssGSEA scores with 16 immune cells in different risk score groups. (B) The boxplot of ssGSEA scores with 13 immune-related functions in different risk score groups. (C) The boxplot of CD274 expression in different ssGSEA scores of IL6-JAK-STAT3 pathway groups. (D) The boxplot of the immune score in different ssGSEA scores of IL6-JAK-STAT3 pathway groups.
FIGURE 6
FIGURE 6
Combination of risk model and clinicopathological features (including APA of genes) improves risk stratification and survival prediction. (A) Different FMNL1-APA was closely related to prognosis in ccRCC. (B) tROC analysis demonstrated that the nomogram was the most stable and powerful predictor for OS in ccRCC among all clinical variables. (C) Calibration analysis of nomogram. (D) The univariate analysis revealed that FMNL1-APA was related to prognosis in ccRCC. (E) The nomogram was built based on all clinical variables.
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