bims-meluca 2024-09-29 papers

Technol Health Care. 2024 Sep 12.

Validation of endoplasmic reticulum stress-related gene signature to predict prognosis and immune landscape of patients with non-small cell lung cancer.

Yingying Cui, Xiaoli Zhou, Dan Zheng, Yumei Zhu.

BACKGROUND: Lung cancer is one of the most common cancers worldwide, with the incidence increasing each year. It is crucial to improve the prognosis of patients who have lung cancer. Non-Small Cell Lung Cancer (NSCLC) accounts for the majority of lung cancer. Though its prognostic significance in NSCLC has not been often documented, Endoplasmic Reticulum (ER) stress has been identified to be implicated in tumour malignant behaviours and resistance to treatment.
OBJECTIVE: This work aimed to develop a gene profile linked to ER stress that could be applied to predictive and risk assessment for non-small cell lung cancer.
METHODS: Data from 1014 NSCLC patients were sourced from The Cancer Genome Atlas (TCGA) database, integrating clinical and Ribonucleic Acid (RNA) information. Diverse analytical techniques were utilized to identify ERS-associated genes associated with patients' prognoses. These techniques included Kaplan-Meier analysis, univariate Cox regression, Least Absolute Shrinkage and Selection Operator regression analysis (LASSO) regression, and Pearson correlation analysis. Using a risk score model obtained from multivariate Cox analysis, a nomogram was created and validated to classify patients into high- and low-risk groups. The study employed the CIBERSORT algorithm and Single-Sample Gene Set Eenrichment Analysis (ssGSEA) to investigate the tumour immune microenvironment. We used the Genomics of Drug Sensitivity in Cancer (GDSC) database and R tools to identify medicines that could be responsive.
RESULTS: Four genes - FABP5, C5AR1, CTSL, and LTA4H - were chosen to create the risk model. Overall Survival (OS) was considerably lower (P< 0.05) in the high-risk group. When it came to predictive accuracy, the risk model outperformed clinical considerations. Several medication types that are sensitive to high-risk groups were chosen.
CONCLUSION: Our study has produced a gene signature associated with ER stress that may be employed to forecast the prognosis and therapeutic response of non-small cell lung cancer patients.

Keywords: Non-small cell lung cancer; endoplasmic reticulum stress; immunotherapy; predictive model; single-cell sequencing

DOI: https://doi.org/10.3233/THC-241059

Diseases. 2024 Sep 18. pii: 221. [Epub ahead of print]12(9):

Circulating Interleukins as Biomarkers in Non-Small Cell Lung Cancer Patients: A Pilot Study Compared to Normal Individuals.

Wei-Wen Lim, Jason H Leung, Chen Xie, Angelina W T Cheng, Liping Su, Luh-Nah Lum, Aishah Toh, Siew-Ching Kong, Angela M Takano, Derek J Hausenloy, Yang C Chua.

Identifying biomarkers in non-small cell lung cancer (NSCLC) can improve diagnosis and patient stratification. We evaluated plasmas and sera for interleukins (IL)-11, IL-6, IL-8, IL-17A, and IL-33 as biomarkers in primary NSCLC patients undergoing surgical treatment against normal volunteers. Exhaled-breath condensates (EBCs), a potential source without invasive procedures, were explored in normal individuals. Due to separate recruitment criteria and intrinsic cohort differences, the NSCLC and control cohorts were not well matched for age (median age: 65 vs. 40 years; p < 0.0001) and smoking status (p = 0.0058). Interleukins were first assessed through conventional ELISA. IL-11 was elevated in NSCLC plasma compared to controls (49.71 ± 16.90 vs. 27.67 ± 14.06 pg/mL, respectively, p < 0.0001) but undetectable in sera and EBCs by conventional ELISA. Therefore, high-sensitivity PCR-based IL-11 ELISA was repeated, albeit with concentration discrepancies. IL11 gene and protein upregulation by RT-qPCR and immunohistochemistry, respectively, were validated in NSCLC tumors. The lack of detection sensitivity across IL-6, IL-8, IL-17A, and IL-33 suggests the need for further, precise assays. Surprisingly, biomarker concentrations can be dissimilar across paired plasmas and sera. Our results identified a need to optimize detection limits for biomarker detection and caution against over-reliance on just one form of blood sample for biomarker assessment.

Keywords: biomarkers; cytokines; enzyme-linked immunosorbent assay; interleukin-11; non-small cell lung cancer; plasma; serum

DOI: https://doi.org/10.3390/diseases12090221

Oncol Res. 2024 ;32(10): 1637-1648

Metformin promotes anti-tumor immunity in STK11 mutant NSCLC through AXIN1-dependent upregulation of multiple nucleotide metabolites.

Zhiguo Wang, Kunlin Li, Conghua Lu, Mingxia Feng, Caiyu Lin, Guofang Yin, Dan Luo, Wenyi Liu, Kaiyu Jin, Yuanyao Dou, D I Wu, Jie Zheng, Kejun Zhang, L I Li, Xianming Fan.

Background: Metformin has pleiotropic effects beyond glucose reduction, including tumor inhibition and immune regulation. It enhanced the anti-tumor effects of programmed cell death protein 1 (PD-1) inhibitors in serine/threonine kinase 11 (STK11) mutant non-small cell lung cancer (NSCLC) through an axis inhibition protein 1 (AXIN1)-dependent manner. However, the alterations of tumor metabolism and metabolites upon metformin administration remain unclear.
Methods: We performed untargeted metabolomics using liquid chromatography (LC)-mass spectrometry (MS)/MS system and conducted cell experiments to verify the results of bioinformatics analysis.
Results: According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, most metabolites were annotated into metabolism, including nucleotide metabolism. Next, the differentially expressed metabolites in H460 (refers to H460 cells), H460_met (refers to metformin-treated H460 cells), and H460_KO_met (refers to metformin-treated Axin1 -/- H460 cells) were distributed into six clusters based on expression patterns. The clusters with a reversed expression pattern upon metformin treatment were selected for further analysis. We screened out metabolic pathways through KEGG pathway enrichment analysis and found that multiple nucleotide metabolites enriched in this pathway were upregulated. Furthermore, these metabolites enhanced the cytotoxicity of activated T cells on H460 cells in vitro and can activate the stimulator of the interferon genes (STING) pathway independently of AXIN1.
Conclusion: Relying on AXIN1, metformin upregulated multiple nucleotide metabolites which promoted STING signaling and the killing of activated T cells in STK11 mutant NSCLC, indicating a potential immunotherapeutic strategy for STK11 mutant NSCLC.

Keywords: Axis inhibition protein 1 (AXIN1); Lung cancer; Metformin; Nucleotide metabolites; Serine/threonine kinase 11 (STK11)

DOI: https://doi.org/10.32604/or.2024.052664