中国标准化（英文版）2025，Vol.133Issue(3)：60-64,5.
基于遗传大数据的预后标志物挖掘标准流程构建初探
Preliminary exploration of constructing a standardized process for prognostic biomarker discovery based on genetic big data
摘要
Abstract
The paper utilized a standardized methodology to identify prognostic biomarkers in hepatocellular carcinoma(HCC)by analyzing transcriptomic and clinical data from The Cancer Genome Atlas(TCGA)database.The approach,which included stringent data preprocessing,differential gene expression analysis,and Kaplan-Meier survival analysis,provided valuable insights into the genetic underpinnings of HCC.The comprehensive analysis of a dataset involving 370 HCC patients uncovered correlations between survival status and pathological characteristics,including tumor size,lymph node involvement,and distant metastasis.The processed transcriptome dataset,comprising 420 samples and annotating 26,783 genes,served as a robust platform for identifying differential gene expression patterns.Among the significant differential expression genes,the key genes such as FBXO43,HAGLROS,CRISPLD1,LRRC3.DT,and ERN2,were pinpointed,which showed significant associations with patient survival outcomes,indicating their potential as novel prognostic biomarkers.This study can not only enhance the understanding of HCC's genetic landscape but also establish a blueprint for a standardized process to discover prognostic biomarkers of various diseases using genetic big data.Future research should focus on validating these biomarkers through independent cohorts and exploring their utility in the development of personalized treatment strategies.关键词
standardized process/genetic big data/prognostic biomarkers/Kaplan-Meier survival analysis/hepatocellular carcinomaKey words
standardized process/genetic big data/prognostic biomarkers/Kaplan-Meier survival analysis/hepatocellular carcinoma引用本文复制引用
王敏,杨永启,李夏伟..基于遗传大数据的预后标志物挖掘标准流程构建初探[J].中国标准化（英文版）,2025,133(3):60-64,5.基金项目
This research was funded by the 2023 Inner Mongolia Public Institution High-Level Talent Introduction Scientific Research Support Project with the start-up funding from Linyi Vocational College. （）
