Integrated Bioinformatics Analysis of Human Transcriptomic Data Identifies Three Key Diagnostic and Prognostic Biomarkers in Lung Adenocarcinoma

Summary

This study identified diagnostic and prognostic biomarkers for lung adenocarcinoma using TCGA-LUAD and GEO GSE115002 transcriptomic data. B3GNT3, FERMT1, and SPP1 were upregulated, distinguishing tumors from normal tissue. These genes are linked to epithelial-mesenchymal transition and immune suppression. A nomogram combining gene expression with TNM stage showed reliable predictive value.

Abstract

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. Despite advances in surgery, targeted therapy, and immunotherapy, the 5-year survival rate of advanced LUAD remains below 20%, indicating an urgent need for reliable molecular biomarkers for early detection and prognosis. In this study, the authors hypothesized that three consistently upregulated genes could act as effective diagnostic and prognostic biomarkers for LUAD. The authors analyzed transcriptomic data from two independent cohorts, TCGA-LUAD (535 tumors, 59 normal samples) and GSE115002 (52 tumors, 52 matched normal samples), to screen differentially expressed genes. Three core genes—B3GNT3, FERMT1, and SPP1—were consistently overexpressed in LUAD tumors in both datasets. These genes showed excellent diagnostic performance, with AUC values above 0.95 in TCGA-LUAD and high accuracy in GSE115002. Survival analysis showed that high expression of each gene was significantly associated with shorter overall and disease-free survival, and multivariate Cox regression verified their independent prognostic value. Functional enrichment analysis indicated that these three genes participate in epithelial-mesenchymal transition, extracellular matrix remodeling, and immune suppression, all of which are closely related to LUAD invasion and metastasis. The authors further constructed a prognostic nomogram combining the three genes and TNM stage, achieving a concordance index of 0.743 and demonstrating good predictive performance. These findings confirm that B3GNT3, FERMT1, and SPP1 are promising diagnostic and prognostic biomarkers for LUAD, supporting the clinical application in risk stratification and management.

Introduction

Lung cancer is the primary cause of global cancer mortality, accounting for approximately 1.8 million deaths in 2020¹. Lung adenocarcinoma (LUAD) represents nearly 40% of all lung cancer cases². Despite advances in surgery, targeted therapy, and immunotherapy, the 5-year survival rate for advanced LUAD remains below 20%^3,4. Reliable molecular biomarkers for early detection and precise prognostication are urgently needed. High-throughput sequencing and public databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) enable systematic trans....

Protocol

1. Data sources and preprocessing

1. Process raw data in R (version 4.1.3; Windows 10 Pro).
2. For GSE115002, apply quantile normalization using limma (version 3.52.3).
3. Filter low-expression genes for TCGA: retain genes with CPM > 0.5 in ≥50% of samples.
4. Filter low-expression genes for GSE115002: retain genes with average signal >50.
5. log₂Transform expression values with a pseudocount of +1.
   NOTE: LUAD gene expression and clinical data were obtained from TCGA-LUAD (version 33.0, GDC Portal, downloaded August 7, 2025) and GSE115002 (Agilent microarray, GEO, do....

Results

Global gene expression alterations in LUAD

Transcriptomic comparisons between lung adenocarcinoma tissues and normal lung tissues identified widespread gene expression changes. Figure 1A shows volcano plots of differentially expressed genes in the TCGA-LUAD dataset, and Figure 1B shows those in the GSE115002 dataset. In the TCGA-LUAD cohort (Figure 1A), 1865 genes were significantly upregulated, and 1247 genes were do.......

Discussion

The nomogram was constructed using multivariate Cox regression analysis based on the TCGA-LUAD cohort. Predictors include pathologic T stage, pathologic N stage, and gene expression status of B3GNT3, FERMT1, and SPP1 (categorized as High vs. Low based on median expression). For each patient, the individual scores for each variable are summed to generate a “Total Points” value, which corresponds to estimated 1-, 2-, and 3-year overall survival probabilities. Higher total scores indic.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Publicly Available Datasets	TCGA-LUAD Dataset	The Cancer Genome Atlas (TCGA) Portal (https://portal.gdc.cancer.gov/); 535 LUAD tumor samples, 59 adjacent normal lung tissue samples (RNA-sequencing count/FPKM values + clinical data: survival, TNM staging)	Transcriptomic and clinical data for differential expression, survival, and nomogram analysis; primary study cohort
	GSE115002 Dataset	Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115002); Agilent microarray, 52 LUAD tumor tissues, 52 matched adjacent normal lung tissues (treatment-naïve primary tumors)	Independent validation cohort for differential expression, diagnostic performance, and immune infiltration analysis
Bioinformatics Software & Programming Environment	R Programming Language	Version 4.1	Core platform for all transcriptomic, statistical, and graphical analyses
	R Packages (Differential Expression)	DESeq2, limma	DESeq2: TCGA RNA-seq raw count differential expression analysis; limma: GSE115002 microarray normalization and differential expression analysis (Benjamini–Hochberg FDR correction)
	R Packages (Diagnostic Analysis)	pROC	Construction of ROC curves, calculation of AUC (95% CI), optimal cutoff determination (Youden’s index) for diagnostic performance assessment
	R Packages (Survival Analysis)	survival, survminer	Kaplan–Meier survival curve generation, log-rank test, univariate/multivariate Cox proportional hazards regression (HR + 95% CI); patient stratification by median gene expression
	R Packages (Functional Enrichment)	clusterProfiler, fgsea	clusterProfiler: GO (BP/CC/MF) and KEGG pathway enrichment analysis (adjusted P < 0.05); fgsea: GSEA for MSigDB Hallmark/KEGG gene sets (FDR < 0.25)
	R Packages (Nomogram Construction & Validation)	rms	Development of prognostic nomogram (integration of gene expression + TNM stage); Harrell’s C-index calculation, bootstrap resampling (1000 repetitions) for bias correction, calibration plot generation
	R Packages (Statistical & Visualization)	ggplot2, ComplexHeatmap, corrplot	Generation of volcano plots, bubble plots (enrichment), heatmaps (immune infiltration correlation), scatter plots (gene co-expression); Pearson/Spearman correlation analysis
Bioinformatics Databases & Tools (Network/Immune Analysis)	STRING Database	Confidence score > 0.7	Construction of protein–protein interaction (PPI) networks for B3GNT3/FERMT1/SPP1 and first-degree interactors
	Cytoscape	-	Visualization of PPI and gene co-expression networks (edge weighting by correlation strength, hub gene identification)
	Immune Deconvolution Algorithm	CIBERSORT	Estimation of immune cell infiltration abundance (M2 macrophages, CD8+ T cells, neutrophils, NK cells, etc.) in LUAD samples; correlation with candidate gene expression
Other Tools	Microsoft Office/LaTeX	-	Manuscript preparation, figure assembly, and table formatting; statistical result compilation