Single-cell and whole-transcriptome sequencing of lymph node metastasis-related gene signature: A large-scale pan-cancer cohort, machine learning and experimental study

Jianguo Lai; Yan He; Xiaoqing Cai; Yuchen Cao; You Long; Zepeng Luo; Kaige Deng; Yiran Lin; Rijia Teng; Haoxuan Huang; Chao Zhu; D Q Cai; Ning Liao

doi:10.1097/JS9.0000000000001144

Single-cell and whole-transcriptome sequencing of lymph node metastasis-related gene signature: A large-scale pan-cancer cohort, machine learning and experimental study

Int J Surg. 2024 Feb 19. doi: 10.1097/JS9.0000000000001144. Online ahead of print.

Authors

Jianguo Lai¹, Yan He¹, Xiaoqing Cai², Yuchen Cao³, You Long², Zepeng Luo², Kaige Deng², Yiran Lin², Rijia Teng², Haoxuan Huang⁴, Chao Zhu⁵, D Q Cai¹, Ning Liao¹

Affiliations

¹ Department of Breast Cancer, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China.
² School of Medicine, South China University of Technology, Guangzhou, 510006, PR China.
³ Plastic Surgery Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100144, China.
⁴ Department of Urology, the Third Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
⁵ Department of Blood Transfusion, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.

PMID: 38385962
DOI: 10.1097/JS9.0000000000001144

Abstract

Background: Lymph node metastasis (LNM) is an independent prognostic factor in numerous types of cancer. Therefore, a LNM-related gene-based nomogram may precisely predict survival and drug sensitivity, and reveal the mechanism underlying LNM.

Materials and methods: Gene sequencing profiles of pan-cancer data (33 cancer types) were acquired from The Cancer Genome Atlas UCSC Xena database. Patients were classified into primary (N = 10,071) and testing (N = 5,036) cohorts. The lymph node score (LNscore) was established via single-cell RNA sequencing, whole-transcriptome sequencing, machine learning, and Cox regression analyses. A novel prognosis model, formulated by incorporating the LNscore and clinical characteristics, was evaluated using the concordance index, calibration curve, and decision curve analysis. Moreover, patients were assigned into high- and low-risk groups according to the median LNscore. We investigated these two groups for survival prognosis, functional enrichment, immune infiltration, and drug sensitivity. In addition, we silenced and overexpressed insulin like growth factor 2 mRNA binding protein 2 (IGF2BP2). We also analyzed the behavior of breast cancer (BRCA) cells regarding lymphatic metastasis and lymphangiogenesis in vitro. IGF2BP2 stimulated the proliferation of BRCA cells via 5-Ethynyl-2'-deoxyuridine and Cell Counting Kit-8 experiments.

Results: A LNM-related set of 12 genes was identified and utilized to determine the LNscore. The concordance-index of both cohorts in the LNscore-based model was >0.7. The immune landscape revealed that the sensitivity to immunotherapy might be better in the high-risk group versus the low-risk group. In addition, we discovered that IGF2BP2 was overexpressed in BRCA tissues and significantly associated with poor survival. Functional analysis indicated that IGF2BP2 promoted BRCA cell migration and proliferation. Additionally, IGF2BP2 accelerated lymphatic metastasis and lymphangiogenesis in vivo.

Conclusions: A novel LNscore-based model was established via comprehensive analysis of LNM-related genes. This model can accurately predict patient survival and drug sensitivity, and reveal the mechanism of LNM in the pan-cancer setting.