Elevated expression of LIF predicts a poor prognosis and promotes cell migration and invasion of clear cell renal cell carcinoma - Zhong, Wenting; Liu, Hongxia; Li, Feng; lin, Youyu; Ye, Yan; Xu, Luyun; Li, ShengZhao; Chen, Hui; Li, Chengcheng; Lin, Yuxuan; Zhuang, Wei; Lin, Yao; et al. (Frontiers in Oncology, 2022)
Background: Renal cell carcinoma (RCC) is the seventh most common cancer in humans, of which clear cell renal cell carcinoma (ccRCC) accounts for the majority. Recently, although there have been significant breakthroughs in the treatment of ccRCC, the prognosis of targeted therapy is still poor. Leukemia inhibitory factor (LIF) is a pleiotropic protein, which is over-expressed in many cancers and plays a carcinogenic role. In this study, we explored the expression and potential role of LIF in ccRCC. Methods: The expression levels and prognostic effects of the LIF gene in ccRCC were detected using TCGA, GEO, ICGC and ArrayExpress databases. The function of LIF in ccRCC was investigated using a series of cell function approaches. LIF-related genes were identified by WGCNA. GO and KEGG analyses were performed subsequently. Cox univariate and LASSO analyses were used to develop risk signatures based on LIF-related genes, and the prognostic model was validated in ICGC and E-MTAB-1980 database. Then, a Nomogram model is constructed for survival prediction and validation of ccRCC patients. To further explore the drug sensitivity between LIF-related genes, we also conducted a GDSC drug sensitivity analysis. Results: The mRNA and protein expression levels of LIF were significantly increased in ccRCC patients. In addition, high expression of LIF has a poor prognostic effect in ccRCC patients. LIF knockdown can inhibit the migration and invasion of ccRCC cells. By using WGCNA, 97 LIF-related genes in ccRCC were identified. Next, a prognostic risk prediction model including 8 LIF-related genes (TOB2, MEPCE, LIF, RGS2, RND3, KLF6, RRP12 and SOCS3) was developed and validated. Survival analysis and ROC curve analysis indicated that the 8 LIF-related genes predictive model had good performance in evaluating patients’ prognosis in different subgroup of ccRCC. Conclusion: Our study revealed that LIF plays a carcinogenic role in ccRCC. In addition, we firstly integrated multiple LIF-related genes to set up a risk predictive model. The model could accurately predict the prognosis of ccRCC, which offers clinical implications for risk stratification, drug screening and therapeutic decision.