Research Background Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer, with 5-year survival rates below 20% for advanced disease. Identifying reliable prognostic biomarkers and understanding molecular mechanisms of tumor progression remain critical research priorities.
Role of Angiogenesis Angiogenesis - the formation of new blood vessels from existing ones - is a hallmark of cancer progression. LUAD tumors require new vasculature to sustain growth and facilitate metastasis. Targeting angiogenic pathways with drugs like bevacizumab has clinical benefit, but reliable biomarkers to predict which patients will respond are lacking.
LncRNA Biology Long non-coding RNAs (LncRNAs) are RNA molecules longer than 200 nucleotides that do not encode proteins but regulate gene expression through diverse mechanisms. They can promote or suppress tumor angiogenesis by modulating VEGF signaling, HIF-1alpha activity, and endothelial cell function.
Study Objective This bioinformatics study uses publicly available TCGA and GEO datasets to identify angiogenesis-related LncRNAs with prognostic significance in LUAD, construct a risk signature, and evaluate its relationship to immune infiltration and drug treatment response.
Data Sources LUAD gene expression and clinical data were obtained from The Cancer Genome Atlas (TCGA), which contains RNA-seq data and survival information for hundreds of LUAD patients. Angiogenesis-related gene sets were sourced from the MSigDB database, providing a curated list of genes involved in blood vessel formation.
LncRNA Identification The authors correlated all expressed LncRNAs with angiogenesis-related gene sets using Pearson correlation analysis, identifying LncRNAs whose expression levels track with angiogenic gene activity. This co-expression approach identified candidate angiogenesis-related LncRNAs without requiring direct mechanistic evidence.
LASSO and Cox Regression To build a parsimonious prognostic model, least absolute shrinkage and selection operator (LASSO) penalized regression combined with univariate and multivariate Cox proportional hazards regression was used. This approach identifies LncRNAs with independent prognostic value while preventing model overfitting.
The Six-LncRNA Signature The final risk model comprises six LncRNAs: AL157388.1, AL590428.1, LINC02057, AC245041.1, AC068228.1, and AL365181.2. Patients are stratified into high-risk and low-risk groups based on a weighted risk score, with high-risk patients showing significantly shorter overall survival.
Risk Score Distribution The six-LncRNA risk score was calculated for all TCGA-LUAD patients. The distribution showed a bimodal pattern, with high-risk patients having significantly worse overall survival (OS) than low-risk patients. Kaplan-Meier curves demonstrated clearly separated survival curves between risk groups.
Time-Dependent ROC Analysis Receiver operating characteristic (ROC) curves at 1, 3, and 5 years assessed the model's discriminative ability. AUC values above 0.60 at multiple time points indicate moderate but clinically meaningful predictive accuracy for survival, comparable to or better than established clinical staging variables.
Independent Prognostic Value Multivariate Cox regression confirmed that the six-LncRNA risk score was an independent predictor of OS after adjusting for age, sex, smoking status, and pathological TNM stage. This independence from stage is particularly valuable, as it provides prognostic information beyond what staging alone can offer.
Nomogram Construction A nomogram integrating the LncRNA risk score with clinical variables was constructed to predict 1-year, 3-year, and 5-year survival probabilities for individual patients. Calibration plots showed good agreement between nomogram predictions and observed outcomes, supporting its potential for individualized clinical use.
CIBERSORT Deconvolution Immune cell composition of TCGA-LUAD tumors was estimated using CIBERSORT, an algorithm that deconvolutes bulk RNA-seq data into proportions of 22 immune cell types. This allows comparison of immune microenvironments between high-risk and low-risk patients defined by the LncRNA signature.
Immune Cell Differences High-risk patients showed significantly different immune infiltration patterns compared to low-risk patients. Specifically, high-risk tumors tended to have lower cytotoxic CD8+ T cell infiltration and higher proportions of immunosuppressive cell types, consistent with a tumor microenvironment less capable of mounting effective anti-tumor responses.
Immune Checkpoint Expression Expression of immune checkpoint molecules including PD-1, PD-L1, CTLA-4, and TIM-3 was analyzed across risk groups. High-risk patients generally showed higher expression of these checkpoint molecules, suggesting that while these tumors may evade immune killing, they could potentially benefit from immune checkpoint inhibitor therapy.
TME Scoring ESTIMATE algorithm scores for immune score, stromal score, and tumor purity showed significant differences between risk groups. High-risk tumors had altered stromal composition that may contribute to immune exclusion, providing a mechanistic link between angiogenic LncRNA expression and immune microenvironment dysfunction.
GDSC Drug Response Analysis Drug sensitivity was estimated using the Genomics of Drug Sensitivity in Cancer (GDSC) database, which provides IC50 values for hundreds of compounds across cancer cell lines. The RIDME algorithm correlated LncRNA risk scores with predicted drug sensitivities to identify compounds more effective in high-risk or low-risk patients.
Targeted Therapy Predictions High-risk patients showed predicted sensitivity to several targeted agents including compounds targeting the PI3K/mTOR and RAS/MAPK pathways, consistent with the known pro-angiogenic functions of these signaling cascades. These predictions suggest potential therapeutic vulnerabilities in the high-risk patient subgroup.
Chemotherapy Sensitivity Low-risk patients showed greater predicted sensitivity to standard LUAD chemotherapy regimens including pemetrexed and cisplatin. This differential chemosensitivity provides another potential clinical application of the risk score - guiding chemotherapy selection in patients who are not candidates for targeted therapy.
Immunotherapy Implications The tumor immune dysfunction and exclusion (TIDE) algorithm predicted higher immune evasion scores in high-risk patients, suggesting they would respond less well to PD-1/PD-L1 blockade despite higher checkpoint expression. This paradox highlights the complexity of predicting immunotherapy response from molecular signatures alone.
Computational Study Limitations As a retrospective bioinformatics study, all findings are based on TCGA data and require prospective experimental and clinical validation. The LncRNAs in the signature are not well-characterized functionally, and their roles in angiogenesis must be experimentally confirmed.
Lack of Functional Validation None of the six LncRNAs have been experimentally validated as direct regulators of angiogenesis in LUAD. Future studies should use gene knockdown/overexpression in LUAD cell lines and endothelial cell co-culture assays to confirm mechanistic roles before the signature is advanced clinically.
Clinical Applicability The nomogram shows calibration in TCGA data but must be validated in independent prospective cohorts before clinical use. RNA-based biomarker assays also require development of robust clinical-grade testing platforms that can reliably measure LncRNA expression from routine biopsy specimens.
Integration with Multi-Omics Future research should integrate the angiogenesis-LncRNA signature with proteomic, epigenomic, and spatial transcriptomic data to better characterize the biological mechanisms linking these LncRNAs to patient outcomes. Such integration could identify drug targets within the angiogenic pathways regulated by these LncRNAs.