Cancer Biomark. 2020;27(2):243-250. doi: 10.3233/CBM-190222.
Prognostic biomarkers are promising targets for cancer prevention and treatment.
We try to filtrate survival-related genes for non-small cell lung cancer (NSCLC) via transcriptome analysis.
Transcriptome data and clinical information of Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), mainly subtypes of NSCLC, were obtained from The Cancer Genome Atlas (TCGA) program. Differentially expressed genes (DEGs) analyzed by DESeq2 package were regarded as candidate genes. For survival analysis, univariate and multivariate Cox regression were applied to select biomarkers for overall survival (OS) and progression-free survival (PFS), where univariate analysis was for preliminary filtration and multivariate analysis considering age, gender, TNM parameters and clinical stage was for ultimate determination. Gene ontology (GO) analysis and pathway enrichment were used for biological annotation.
We ultimately acquired a series of genes closely related to prognosis. For LUAD, we determined 314 OS-related genes and 275 PFS-related genes, while 54 OS-related genes and 78 PFS-related genes were chosen for LUSC. The final biological analysis indicated important function of proliferative signaling in LUAD but for LUSC, only cornification process had statistical meaning.
We strictly determined prognostic genes of NSCLC, which would contribute to its carcinogenesis investigation and therapeutic methods improvement.
预后生物标志物是癌症预防和治疗的有前途的靶点。
我们试图通过转录组分析筛选非小细胞肺癌（NSCLC）的生存相关基因。
从癌症基因组图谱（TCGA）计划中获取肺腺癌（LUAD）和肺鳞状细胞癌（LUSC）的转录组数据和临床信息，主要是 NSCLC 的亚型。差异表达基因（DEGs）由 DESeq2 包分析，被视为候选基因。对于生存分析，单变量和多变量 Cox 回归用于选择总生存期（OS）和无进展生存期（PFS）的生物标志物，单变量分析用于初步筛选，而多变量分析考虑年龄、性别、TNM 参数和临床分期用于最终确定。基因本体（GO）分析和通路富集用于生物学注释。
我们最终获得了一系列与预后密切相关的基因。对于 LUAD，我们确定了 314 个与 OS 相关的基因和 275 个与 PFS 相关的基因，而对于 LUSC，则选择了 54 个与 OS 相关的基因和 78 个与 PFS 相关的基因。最终的生物学分析表明，增殖信号在 LUAD 中具有重要功能，但对于 LUSC，只有角化过程具有统计学意义。
我们严格确定了 NSCLC 的预后基因，这将有助于对其致癌机制的研究和治疗方法的改进。
