Zhang Hongliang, Zhao Lei, Li Songyan, Wang Jing, Feng Cong, Li Tanshi, Du Xiaohui
Medical School of Chinese People's Liberation Army (PLA), Beijing, China.
Department of Emergency, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Front Oncol. 2021 Jun 11;11:697949. doi: 10.3389/fonc.2021.697949. eCollection 2021.
LncRNA dysregulation and the tumor microenvironment (TME) have been shown to play a vital role in the progression and prognosis of colon cancer (CC). We aim to reveal the potential molecular mechanism from the perspective of lncRNA in the TME and provide the candidate biomarkers for CC prognosis.
ESTIMATE analysis was used to divide the CC patients into high and low immune or stromal score groups. The expression array of lncRNA was re-annotated by Seqmap. Microenvironment-associated lncRNAs were filtered through differential analysis. The m6A-associated lncRNAs were screened by Pearson correlation analysis. Lasso Cox regression analyses were performed to construct the m6A- and tumor microenvironment-related lncRNA prognostic model (m6A-TME-LM). Survival analysis was used to assess the prognostic efficacy of candidate lncRNAs. Enrichment analyses annotated the candidate genes' functions.
We obtained 25 common differentially expressed lncRNAs (DELs) associated with immune microenvironment and m6A-related genes for subsequent lasso analysis. Four out of these DELs were selected for the m6A-TME-LM. All the four lncRNAs were related to overall survival, and a test set testified the result. Further stratification analysis of the m6A-TME-LM retained its ability to predict OS for male and chemotherapy adjuvant patients and performed an excellent prognostic efficacy in the TNM stage III and IV subgroups. Network analysis also found the four lncRNAs mediated co-expression network was associated with tumor development.
We constructed the m6A-TME-LM, which could provide a better prognostic prediction of CC.
长链非编码RNA(lncRNA)失调和肿瘤微环境(TME)已被证明在结肠癌(CC)的进展和预后中起重要作用。我们旨在从lncRNA在TME中的角度揭示潜在的分子机制,并为CC预后提供候选生物标志物。
使用ESTIMATE分析将CC患者分为高、低免疫或基质评分组。通过Seqmap对lncRNA的表达阵列进行重新注释。通过差异分析筛选与微环境相关的lncRNA。通过Pearson相关分析筛选与m6A相关的lncRNA。进行Lasso Cox回归分析以构建与m6A和肿瘤微环境相关的lncRNA预后模型(m6A-TME-LM)。生存分析用于评估候选lncRNA的预后效果。富集分析注释了候选基因的功能。
我们获得了25个与免疫微环境和m6A相关基因相关的常见差异表达lncRNA(DEL),用于后续的Lasso分析。从这些DEL中选择了4个用于构建m6A-TME-LM。所有这4个lncRNA均与总生存期相关,并且一个测试集验证了该结果。对m6A-TME-LM进行的进一步分层分析保留了其预测男性和化疗辅助患者总生存期的能力,并且在TNM III期和IV期亚组中具有出色的预后效果。网络分析还发现这4个lncRNA介导的共表达网络与肿瘤发展相关。
我们构建了m6A-TME-LM,它可以为CC提供更好的预后预测。