Elevated expression of LIF predicts a poor prognosis and promotes cell migration and invasion of clear cell renal cell carcinoma

Wenting Zhong; Hongxia Liu; Feng Li; Youyu Lin; Yan Ye; Luyun Xu; ShengZhao Li; Hui Chen; Chengcheng Li; Yuxuan Lin; Wei Zhuang; Yao Lin; Qingshui Wang

doi:10.3389/fonc.2022.934128

Elevated expression of LIF predicts a poor prognosis and promotes cell migration and invasion of clear cell renal cell carcinoma

Front Oncol. 2022 Aug 3:12:934128. doi: 10.3389/fonc.2022.934128. eCollection 2022.

Authors

Wenting Zhong^{1

2}, Hongxia Liu², Feng Li³, Youyu Lin², Yan Ye², Luyun Xu², ShengZhao Li², Hui Chen², Chengcheng Li², Yuxuan Lin², Wei Zhuang⁴, Yao Lin¹, Qingshui Wang^{1

2}

Affiliations

¹ Central Laboratory at the Second Affiliated Hospital of Fujian Traditional Chinese Medical University, Fujian-Macao Science and Technology Cooperation Base of Traditional Chinese Medicine-Oriented Chronic Disease Prevention and Treatment, Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
² College of Life Sciences, Fujian Normal University, Fuzhou, China.
³ Department of Pathology, Fujian Provincial Hospital, Fuzhou, China.
⁴ Department of Urology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.

Abstract

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 overexpressed 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 weighted gene correlation network analysis (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 the ICGC and E-MTAB-1980 databases. Then, a nomogram model was constructed for survival prediction and validation of ccRCC patients. To further explore the drug sensitivity between LIF-related genes, we also conducted a drug sensitivity analysis based on the GDSC database.

Results: The mRNA and protein expression levels of LIF were significantly increased in ccRCC patients. In addition, a 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 eight 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 eight LIF-related-gene predictive model had good performance in evaluating patients' prognosis in different subgroups 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.

Keywords: LIF; WGCNA; ccRCC; invasion; lasso; nomogram.