Background: Six2, a transcription factor, exerts an oncogenic role in clear cell renal cell carcinoma (ccRCC). Increased Six2 expression could enhance cancer metastasis. However, the regulatory mechanism of Six2 in promoting metastasis remains unclear. The purpose of this study is to analyze the regulatory pattern of Six2 and the potential role of Six2 in the tumor immune microenvironment. Materials and Methods: Firstly, transcriptional data in TCGA-KIRC cohorts was used to analyze the relationship between Six2 expression and clinical information. Secondly, we detect the association between Six2 and the tumor immune microenvironment in ccRCC. Then, we analyzed Six2-related differentially expressed genes (DEGs) and constructed a prognostic model using the Lasso-Cox algorithm by integrating Six2 ChIP data and co-expressed genes. Next, we analyzed the clinical significance and immunotherapy sensitivity of this model. Results: Six2 was overexpressed in RCC cells compared with normal kidney cells and upregulated Six2 was positively linked with clinical stage, grade, T stage, M stage, and poor survival. And Six2 was correlated with the remodeling of the tumor microenvironment. Potential downstream effectors and biological functions regulated by Six2 were identified using in silico analysis. Meanwhile, a risk model based on 8 Six2 target genes was established to classify ccRCC patients into high-and-low risk groups. This risk model showed a reliable ability to forecast the overall survival of ccRCC patients and predict the susceptibility to immunotherapy. Conclusions: Our findings provide a promising prognostic indicator for ccRCC patients and help better understand the transcriptional and immunological role of Six2 in ccRCC.

ObjectiveWe aimed to construct and validate nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for patients with chromophobe renal cell carcinoma (chRCC) after nephrectomy.DesignThis study is a retrospective cohort study.Setting and participantsThere were 2810 patients with chRCC from Surveillance, Epidemiology and End Results database diagnosed between 2010 and 2015 included in the study who were randomly divided into a training cohort (n=1970) and a validation cohort (n=840). Another single-centre external validation cohort containing 124 patients from our hospital was also involved in our study.Primary and secondary outcome measuresOS and CSS.ResultsNomograms for OS and CSS include four and five variables, respectively, from the result of least absolute shrinkage and selection operator regression analyses. Nomograms reveal the accurate discrimination by the area under the curve of receiver operating characteristic (ROC) curves and C-indexes, with a C-index value of 0.777 (95% CI 0.728 to 0.826), 0.810 (95% CI 0.747 to 0.873) and 0.863 (95% CI 0.773 to 0.953) for the training cohort, the internal validation cohort and the external validation cohort in the nomogram for OS; and a C-index value of 0.884 (95% CI 0.829 to 0.939), 0.868 (95% CI 0.772 to 0.964) and 0.862 (95% CI 0.760 to 0.964) for the training cohort, the internal validation cohort and the external validation cohort in the nomogram for CSS. It was also proven that there was a high degree of conformance between the predicted and observation results by calibration plots. In addition, the comparison of ROC curves and C-indexes between nomograms and seventh tumour, node and metastasis stage demonstrated that nomograms were better in accuracy and efficacy ability.ConclusionsWe successfully constructed two accurate and effective nomograms to predict OS and CSS for patients with chRCC after nephrectomy, which can help clinical doctors choose individual treatment strategies for chRCC patients.