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 transcriptomic profiling of cancers^5,6. Integrative cross‑cohort bioinformatics improves the reliability of candidate biomarker discovery⁵.

Many genes and pathways have been implicated in LUAD, including cell proliferation, EGFR signaling, and immune escape⁷. However, few have been translated into clinical use. Risk models combining gene signatures and clinicopathologic features—especially nomograms—improve prognostic accuracy in LUAD⁸. While B3GNT3, FERMT1, and SPP1 have been individually linked to cancer progression, their combined diagnostic, prognostic, and immune‑microenvironment regulatory value in LUAD has not been systematically validated across independent cohorts. This study provides the first integrated cross‑platform analysis of these three genes as a unified biomarker panel for LUAD, with a clinically applicable prognostic nomogram.

B3GNT3 encodes a glycosyltransferase that stabilizes PD‑L1 and promotes immune evasion^9,10. FERMT1 (kindlin‑1) regulates integrin activation and drives metastasis in non‑small cell lung cancer (NSCLC)^11,12. SPP1 (osteopontin) mediates extracellular matrix remodeling, epithelial‑mesenchymal transition (EMT), and chemoresistance^13,14,15. Circadian clock-related genes have also been shown to predict LUAD prognosis and diagnosis¹⁶, while sex differences in LUAD have been uncovered via multi-omics integrative protein signaling networks¹⁷. B3GNT3 and SPP1 are secreted or membrane‑localized, supporting potential use as minimally invasive biomarkers. Effective LUAD classification and biomarker identification can also be achieved through overlapping feature selection methods¹⁸, and multi-omic interactions play important functional roles in lung cancer progression¹⁹. Mitochondrial gene signatures, identified via comprehensive multi-omics integration, also hold value for LUAD prognosis and personalized therapy²⁰. B3GNT3 and SPP1 are secreted or membrane‑localized, supporting potential use as minimally invasive biomarkers. This study aimed to identify robust LUAD biomarkers using integrative bioinformatics, evaluate their diagnostic and prognostic performance, explore their biological functions and immune associations, and build a clinically useful prognostic nomogram.

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, downloaded August 7, 2025). TCGA-LUAD included 535 tumors and 59 normal samples. GSE115002 included 52 tumors and 52 matched normal samples.

2. Identification of differentially expressed genes

1. Use DESeq2 (version 1.36.0) for TCGA RNA-seq and limma (version 3.52.3) for GSE115002 for differential expression analysis. Calculate adjusted P‑values (FDR) using the Benjamini–Hochberg method.
2. To ensure cross‑dataset comparability, a unified |log₂FC| ≥ 1.0 was applied for both cohorts. DEGs were defined as FDR < 0.05 and |log₂FC| ≥ 1.0. Overlapping DEGs were identified using VennDiagram (version 1.7.3). B3GNT3, FERMT1, and SPP1 were selected as consistently upregulated candidates with known cancer relevance.

3. Evaluation of diagnostic value

1. Construct ROC curves for each candidate gene.
2. Determine optimal cutoff values using the Youden index.
3. Calculate AUC, sensitivity, and specificity for each gene.
4. Build a combined diagnostic panel using multivariate logistic regression.
   NOTE: The pROC package v1.18.0 was used for ROC analysis. The glm function with the binomial family was used to construct the diagnostic model.

4. Survival analysis

1. Stratify patients into high-and low-expression groups using median expression.
2. Generate Kaplan–Meier survival curves for each gene.
3. Perform log‑rank tests to compare survival differences.
4. Conduct univariate Cox regression analysis.
5. Conduct multivariate Cox regression analysis.
6. Include clinical covariates in regression models.
7. Verify the proportional hazards assumption using Schoenfeld residuals.
8. Calculate a three‑gene risk score.
   NOTE: Survival v3.3.1 and survminer v0.4.9 were used. Covariates included age, sex, T stage, N stage, and M stage. The risk score was calculated as:
   Risk score = (0.328 × B3GNT3) + (0.331 × FERMT1) + (0.321 × SPP1). (1)

5. Gene set enrichment and functional annotation

1. Perform GO enrichment analysis using DEGs.
2. Perform KEGG pathway enrichment analysis using DEGs.
3. Conduct gene set enrichment analysis (GSEA).
4. Rank genes by Pearson correlation with candidate gene expression.
5. Identify significant terms using adjusted P < 0.05.
   NOTE: clusterProfiler v4.6.2 was used for GO and KEGG analyses. FGSEA v1.22.0 and MSigDB Hallmark v7.5 were used for GSEA.

6. Correlation and network analysis

NOTE: Pearson correlation was used for normally distributed gene expression; Spearman correlation for immune cell fractions. PPI networks were generated using STRING (version 11.5, confidence > 0.7) and visualized in Cytoscape (version 3.9.1). Immune infiltration was estimated using CIBERSORT (absolute mode, 100 permutations). Single-cell RNA sequencing has been shown to reveal niche transitions in the NSCLC microenvironment, which is relevant to immune infiltration analysis^21,22, and integrative single-cell analysis can further dissect the roles of immune cells, such as CD8+ memory cells, in LUAD^23,24,25.

7. Nomogram construction and validation

NOTE: Variables for the nomogram were selected based on multivariate Cox significance (P < 0.05): T stage, N stage, B3GNT3, FERMT1, and SPP1. The nomogram was built using rms (version 6.5.0). Internal validation used 1000 bootstrap resampling with replacement. Calibration curves and decision curve analysis (DCA) were performed using rmda (version 1.7). The computational environment included R 4.1.3, Windows 10 Pro, and Bioconductor 3.15. Analysis scripts are available at https://github.com/[redacted]/LUAD‑biomarker-2025 upon reasonable request.

8. Statistical analysis

NOTE: All statistical tests were two-sided; P < 0.05 was considered significant.

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