MicroRNA-486: a dual-function biomarker for diagnosis and tumor immune microenvironment characterization in non-small cell lung cancer

Abstract

Background: This investigation evaluates the clinical significance and molecular mechanisms of microRNA-486 (miR-486) as a potential biomarker in non-small cell lung cancer (NSCLC) through an integrative analytical approach.

Methods: We conducted systematic search and meta-analysis of diagnostic studies from major biomedical databases from inception through April 04, 2025, followed by comprehensive bioinformatics interrogation. Protein-protein interaction (PPI) networks were constructed using STRING to identify key hub genes regulated by miR-486. Validation of hub genes employed TCGA datasets, while immune infiltration analysis utilized TIMER2.0 platform.

Results: The meta-analysis indicated that miR-486, both individually and in combination, could be effective biomarkers for NSCLC detection. Afterwards, functional enrichment analyses of miR-486 target genes highlighted significant ontology terms and pathways crucial to the initiation and progression of NSCLC. PPI networks revealed key proteins and modules that participate in multiple essential pathways associated with NSCLC pathogenesis. Furthermore, the identified hub genes were validated for differential expression in cancerous versus normal tissues, suggesting their potential diagnostic utility, while subsequent survival analyses confirmed their prognostic value through significant associations with overall survival. Notably, these hub genes were found to be significantly associated with immune infiltration levels, immune microenvironment scores, and immune-related proteins in NSCLC.

Conclusions: This dual-modality investigation establishes miR-486 as a multi-functional biomarker in NSCLC, demonstrating both diagnostic utility and immunoregulatory potential through tumor microenvironment modulation.

Keywords: Biomarker; Immune-cell infiltration; MicroRNA-486; Non-small cell lung cancer.

Fig. 1

Schematic overview of the study design and analytical workflow

Fig. 2

The flowchart of database search and study identification

Fig. 3

Forest plots for pooled results in diagnosing NSCLC for sensitivity and specificity. A Single miR-486; B Combination marker containing miR-486

Fig. 4

SROC curve with confidence and prediction regions around mean operating sensitivity and specificity point. A SROC curve overall including the outliers for single miR-486; (B) SROC curve of circulating single miR-486; (C) SROC curve of single miR-486 based on large sample size; (D) SROC curve of single miR-486 based on small sample size; (E) SROC curve of outliers excluded for single miR-486; (F) SROC curve of combination biomarkers containing miR-486

Fig. 5

Sensitivity analysis results

Fig. 6

Functional enrichment analysis results. (A) Top 10 of the most significantly enriched GO items at the biological process level; (B) Top 10 of the most significantly enriched GO items at the cellular component level; (C) Top 10 of the most significantly enriched GO items at the molecular function level; (D) The significant pathways enriched by all the genes regulated by miR-486

Fig. 7

PPI network construction results. A Betweenness centrality distributions of nodes; (B) Closeness centrality distributions of nodes; (C) Degree distributions of nodes; (D) The sub-network reconstructed with the selected hub proteins and their first neighbor proteins; (E) Top 20 pathways enriched by the 10 hub genes of miR-486

Fig. 8

The expression of hub genes in LUAD and LUSC compared with unpaired adjacent tissues and pared adjacent tissues

Fig. 9

Receiver operating characteristic (ROC) curves for hub genes expression in LUAD and LUSC based on TCGA database

Fig. 10

Survival analysis results of the hub genes in NSCLC patients. A-B Survival maps of hub genes expression in LUAD: OS (A), and DFS (B); (C-D) Survival maps of hub genes expression in LUSC: OS (C), and DFS (D); (C-D) Kaplan–Meier survival curves of high and low expression of SNAI1 in LUAD and LUSC: OS in LUAD (E), DFS in LUAD (F), OS in LUSC (G), and DFS in LUSC (H)

Fig. 11

Survival analysis outcomes for the pivotal hub gene, SNAI1, in patients with NSCLC. (A-D) The prognostic significance of SNAI1 in LUAD is depicted through various analytical approaches: A Univariate Cox regression analysis reveals the influence of SNAI1 expression on OS in LUAD; (B) Multivariate Cox regression further substantiates this relationship, adjusting for additional variables; (C) A predictive nomogram, constructed from TCGA datasets, combines SNAI1 levels with other prognostic indicators to estimate survival probabilities in LUAD; (D) The calibration curve associated with the nomogram evaluates the accuracy of survival predictions. (E–H) The prognostic relevance of SNAI1 in LUSC: E Univariate Cox regression analysis assesses the effect of SNAI1 expression on OS in LUSC; (F) Multivariate analysis corroborates these findings, accounting for other prognostic factors; (G) Another nomogram, derived from TCGA data, integrates SNAI1 expression with additional prognostic markers to forecast survival outcomes in LUSC; (H) The corresponding calibration curve assesses the precision of the nomogram’s predictive capability

Fig. 12

The correlation between the infiltration of immune cells and the expression of the hub genes in LUAD and LUSC

Fig. 13

Correlations between the hub genes expression and immune microenvironment scores in LUAD and LUSC including stromal score, immune score, and estimate score

Fig. 14

Correlation of hub genes expression with immune regulator expression in LUAD and LUSC

Fig. 15

Correlation of hub genes expression with immune related genes in LUAD and LUSC