Development and validation of hub genes for lymph node metastasis in patients with prostate cancer

Abstract

| INTRODUC TI ONProstate cancer (PCa) is the fifth principal cause of death and also the second most frequent cancer in males all over the world. 1 More than 15% of PCa patients harbour lymph node metastasis at radical prostatectomy. 2 Lymph node metastasis is a complicated process in which cancer cells leave the primary tumour site through the lymphatic system and then establish a secondary tumour site in lymph nodes. 3 These individuals have a higher risk of recurrence after primary treatment and usually suffer a poor prognosis. 4 Therefore, predicting the occurrence of lymph node metastasis is of vital clinical significance. At the present day, several nomograms and indexes AbstractLymph node metastasis is one of the most important independent risk factors that can negatively affect the prognosis of prostate cancer (PCa); however, the exact mechanisms have not been well studied. This study aims to better understand the underlying mechanism of lymph node metastasis in PCa by bioinformatics analysis.We analysed a total of 367 PCa cases from the cancer genome atlas database and performed weighted gene co-expression network analysis to explore some modules related to lymph node metastasis. Gene Ontology analysis and pathway enrichment analysis were conducted for functional annotation, and a protein-protein interaction network was built. Samples from the International Cancer Genomics Consortium database were used as a validation set. The turquoise module showed the most relevance with lymph node metastasis. Functional annotation showed that biological processes and pathways were mainly related to activation of the processes of cell cycle and mitosis. Four hub genes were selected: CKAP2L, CDCA8, ERCC6L and ARPC1A. Further validation showed that the four hub genes well-distinguished tumour and normal tissues, and they were good biomarkers for lymph node metastasis of PCa. In conclusion, the identified hub genes facilitate our knowledge of the underlying molecular mechanism for lymph node metastasis of PCa. K E Y W O R D Shub genes, lymph node metastasis, prostate cancer, weighted gene co-expression network analysis

Site-Specific and Common Prostate Cancer Metastasis Genes as Suggested by Meta-Analysis of Gene Expression Data

“…Meta-analysis on differential gene expression from primary tumors and metastasis are common for different cancer types. However, the analyses that stratify metastatic samples according to the secondary sites are rare with more interest shown in very recent years [ 29 , 30 , 31 , 32 , 33 ]. To the best of my knowledge, the work that is presented here is the first such study for prostate cancer and its most common metastasis sites.…”

Identification of HCG18 and MCM3AP-AS1 That Associate With Bone Metastasis, Poor Prognosis and Increased Abundance of M2 Macrophage Infiltration in Prostate Cancer

“…WGCNA 14 is a popular systems biology algorithm that not only could effectively identify highly-connected and function-shared genes (in the same module) from thousands of genes in the microarray or sequencing data based on the correlation between genes due to their similar expression profile, but also could associate co-expression modules with clinical characteristics. Thus, WGCNA is a widely applied method to screen specific phenotype-associated genes in many cancers, including prostate cancer (castration-resistant-related, 19 Gleason score-related 20 and lymph node metastasis-related 21 ). Similar to these studies, we also used the WGCNA package in R (version 1.61; https://cran.r-project.org/web/packages/WGCNA/index.html ) 14 to identify modules highly correlated with bone metastasis of PCa in the GSE32269 dataset.…”

Identification And validation of transcription factor genes involved in prostate cancer metastasis

“…Previous studies reported that different gene expression profiles existed between metastatic PCa and localized PCa either in a single cohort (LaTulippe et al 2002;Varambally et al 2005;Chandran et al 2007;Taylor et al 2010) or in an integrated series (Liu et al 2018;Guo et al 2019;Xu et al 2020).…”