Systematic Investigation of Key Drivers of Lung Adenocarcinoma: A Focus on Genes, Pathways, and miRNAs.

Abstract A065: The Role of miR-100 in lung cancer cell line: Relevance to cancer biology and potential therapy

Lung cancer remains the leading cause of cancer-related mortality worldwide, with approximately 1.8 million deaths annually. Despite advances in diagnostics and targeted therapies, survival remains poor due to late-stage diagnosis, tumor heterogeneity, and therapeutic resistance. MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression and play a key role in cancer biology. miR-100 has been reported as a tumor suppressor in several malignancies; however, its role and downstream regulatory network in lung cancer remain incompletely understood. This study aims to investigate the biological role of miR-100 in lung cancer and explore its therapeutic relevance. An integrative approach combining bioinformatics and experimental validation was used. Public datasets (TCGA, UALCAN, Linkedomics, and KM-Plotter) were analyzed to assess miR-100 expression, associated genes, and survival outcomes. Candidate genes were selected based on differential expression, prognostic relevance, and predicted interaction with miR-100. Functional validation was performed in A549 and BEAS-2B cell lines using qPCR following transfection with miR-100 mimics and inhibitors. Drug repurposing analysis was conducted using iLINCS. miR-100 was significantly downregulated in lung tumor tissues compared to normal tissues and was associated with improved overall survival (HR = 0.85, p = 0.033). ST6GALNAC4 was identified as a key gene associated with miR-100, showing differential expression, survival relevance, and predicted targeting. TXNDC11 was identified as a functionally related gene involved in oxidative stress pathways. In vitro validation confirmed reduced miR-100 expression in A549 cells compared to BEAS-2B cells. Modulation of miR-100 expression resulted in significant dysregulation of both ST6GALNAC4 and TXNDC11, supporting the computational findings. Drug signature analysis identified Vorinostat as a potential candidate capable of reversing the observed molecular profile. This study identifies a novel regulatory axis involving miR-100, ST6GALNAC4, and TXNDC11 in lung cancer. These findings highlight the role of miR-100 in glycosylation and oxidative stress pathways and support its potential as both a biomarker and therapeutic target. Additionally, Vorinostat emerges as a promising candidate for drug repurposing in this context. “Artificial intelligence (AI) tools were used solely to improve language clarity and readability. The scientific content, analysis, and conclusions are entirely the responsibility of the authors.” Basil Alotaibi, Arwa Alsubait. The Role of miR-100 in lung cancer cell line: Relevance to cancer biology and potential therapy [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A065.

Researching the role of ubiquitination and macrophage activation-related genes in hepatocellular carcinoma through a systematic study based on gene expression profiling.

The findings lay the groundwork for precision oncology paradigms, facilitating the translational trajectory of these targets to optimize clinical prognosis, and underscores the potential of UMARGs in advancing HCC treatment strategies and improving patient outcomes.

Exploring key genes and prognostic validation of prostate cancer through transcriptome-wide association study.

Prostate cancer (PC) is one of the most common malignant tumors among men worldwide. Although treatment methods for localized PC are relatively well-established, challenges remain due to difficulties in early diagnosis and issues with treatment resistance. This study combines genome-wide association study (GWAS) data with tissue-specific expression quantitative trait loci (eQTL) data through a transcriptome-wide association study (TWAS) to explore key genes associated with PC. The study also includes differential expression analysis and survival analysis to further verify these findings. A total of 46 candidate genes were identified, and survival analysis using the Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset revealed that the upregulation of asparagine synthetase (ASNS) is associated with poor prognosis, while the downregulation of sulfatase modifying factor 2 (SUMF2) is linked to adverse prognosis. The results suggest that ASNS and SUMF2 play important roles in the progression of PC and may serve as candidate prognostic biomarkers associated with PC progression.

Targetable genes associated with hematogenous metastasis in gastric cancer using the TCGA database

Purpose: Gastric cancer is a major global health issue, especially in advanced stages with metastasis. However, anti-angiogenic treatments such as ramucirumab target vascular endothelial growth factor, yet the exact mechanisms behind hematogenous metastasis remain unclear. This study analyzed RNA sequencing data from TCGA to identify angiogenesis-related genes in metastatic gastric cancer.Methods: Patients were categorized into four metastasis types (non-metastasis, hematogenous, locoregional, and lymphatic) based on clinical data. cBioPortal was used to identify frequently mutated genes across five metastatic cancer studies. RNA sequencing and clinical data were obtained from the TCGA-STAD project. RNA expression levels were compared across metastasis groups using independent-samples t-tests, followed by false discovery rate adjustment for multiple comparisons.Results: RNA expression analysis was performed using a 132-gene analytical panel in the TCGA-STAD cohort. Among the 95-gene angiogenesis/metastasis-related genes represented in this panel, RAF1, ARID1A, ERBB3, FGFR3, BAP1, TSC2, and KDR showed nominal expression differences in comparisons involving the hematogenous metastasis group. None of these differences remained statistically significant after false discovery rate correction.Conclusion: In this exploratory analysis, several candidate genes with prior literature support showed nominal expression differences in comparisons involving hematogenous metastasis in gastric cancer. These findings are hypothesis-generating and require validation in independent cohorts and functional studies.

Investigating the mechanisms of malignant progression in colorectal cancer using weighted gene co-expression network analysis and machine learning

Prognostic and immunological value of LINC01615-related genes in kidney clear cell carcinoma

LINC01615, a long noncoding RNA, plays a pivotal role in the progression of kidney renal clear cell carcinoma (KIRC). This study aimed to assess the prognostic value of LINC01615-associated genes in KIRC by developing a risk model. Differential expression analysis in the The Cancer Genome Atlas-KIRC dataset identified differentially expressed genes between high and low LINC01615 expression groups, as well as between KIRC and control groups. Signature genes were subsequently selected through protein-protein interaction (PPI) network analysis, while prognostic genes were identified via Cox regression. The risk model was then constructed and validated using the E-MTAB-1980 dataset. Furthermore, an independent prognostic analysis identified key risk factors, and a nomogram was created for clinical application. Additional analyses, including enrichment analysis, immune-related analysis, drug sensitivity evaluation, and regulatory network construction, were performed to explore the underlying mechanisms in high and low-risk groups. The GSE40435 dataset was employed for the validation of prognostic gene expression. Reverse transcription-quantitative PCR (RT-qPCR) was conducted to confirm the expression levels of prognostic genes and LINC01615 in clinical samples. LINC01615 expression was found to differ significantly between KIRC and control groups, with notable survival differences observed between high and low expression groups. A total of 757 candidate genes were identified. Among these, COL4A4, COL5A1, and COL15A1 were screened as prognostic genes, and a risk model with better accuracy was constructed. Age and risk score were recognized as independent risk factors, and the nomogram demonstrated enhanced predictive accuracy. Twelve drugs showed a significant negative correlation with risk scores. Additionally, the high-risk group exhibited an increased likelihood of immune escape. A regulatory relationship between hsa-miR-3163 and COL4A4/LINC01615 was identified. In both The Cancer Genome Atlas-KIRC and GSE40435 datasets, COL5A1 and COL15A1 were overexpressed in the KIRC group. RT-qPCR results for COL5A1 and COL4A4 were consistent with the above findings, while COL15A1 showed no significant differences in clinical samples, possibly due to the small sample size. COL4A4, COL5A1, and COL15A1 were identified as prognostic biomarkers through bioinformatics analysis. The developed risk model offers valuable insights for clinical prognostic prediction and immunotherapy in KIRC.