Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Frontiers in genetics ; h5-index 62.0

Background: Clear cell renal cell carcinoma (ccRCC) is a malignancy with a high incidence rate and poor prognosis worldwide. Copper ionophore-induced death (CID) plays an important role in cancer progression. Methods: One training and three validation datasets were acquired from TCGA, GEO and ArrayExpress. K-means clustering was conducted to identify the CID subtypes. The ESTIMATE and CIBERSORT algorithms were employed to illustrate the immune microenvironment of ccRCC. LASSO Cox regression was applied to construct the CID feature-based prognostic model. The immunotherapy cohort was acquired from the literature to explore the potential risk scores for predicting immunotherapy responsiveness. Results: Two CID-related cancer subtypes of ccRCC were identified that performed different immune microenvironment characteristics and prognosis. Based on the identified subtypes, we analyzed the biological heterogeneity and constructed a gene prognostic model. The prognostic model performed well in one training dataset, three validation datasets, and different clinical pathological groups. The prognostic model has a good potential for predicting cancer immune features and immunotherapy responsiveness. Conclusion: CID plays an important role in the tumor microenvironment progression of ccRCC. The robust gene prognostic model developed can help predict cancer prognosis, immune features, and immunotherapy responsiveness.

Xia Shunyao, Jia Haixing, Qian Zhipeng, Xiu Youcheng


CID, bioinformatics, ccRCC, immune microenvironment, machine-learning, precise medicine