Diagnostic and prognostic value of the BEX family in lung adenocarcinoma

A tumor microenvironment gene set–Based prognostic signature for non-small-cell lung cancer

Background: The tumor microenvironment (TME) is involved in the development and progression of lung carcinomas. A deeper understanding of TME landscape would offer insight into prognostic biomarkers and potential therapeutic targets investigation. To this end, we aimed to identify the TME components of lung cancer and develop a prognostic signature to predict overall survival (OS).

Methods: Expression data was retrieved from The Cancer Genome Atlas (TCGA) database and differentially expressed TME-related genes were calculated between tumor and normal tissues. Then nonnegative matrix factorization (NMF) clustering was used to identify two distinct subtypes.

Results: Our analysis yielded a gene panel consisting of seven TME-related genes as candidate signature set. With this panel, our model showed that the high-risk group experienced a shorter survival time. This model was further validated by an independent cohort with data from Gene Expression Omnibus (GEO) database (GSE50081 and GSE13213). Additionally, we integrated the clinical factors and risk score to construct a nomogram for predicting prognosis. Our data suggested less immune cells infiltration but more fibroblasts were found in tumor tissues derived from patients at high-risk and those patients exhibited a worse immunotherapy response.

Conclusion: The signature set proposed in this work could be an effective model for estimating OS in lung cancer patients. Hopefully analysis of the TME could have the potential to provide novel diagnostic, prognostic and therapeutic opportunities.

Characterization of Fatty Acid Metabolism in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Fatty acid metabolism takes part in malignancy progression. However, the roles fatty acid metabolism plays in LUAD are still unclear.

Methods: The transcriptomic and clinical data of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were extracted. ssGSEA, WGCNA, univariable Cox regression, and LASSO Cox regression analyses were performed to identify the fatty acid metabolism-related genes which influenced the overall survival (OS) and build a fatty acid-related risk score (FARS) model. A nomogram was established based on the FARS and other clinicopathological features, and ROC and calibration plots were used to validate the prediction accuracy. The tumor microenvironment (TME) of patients with high and low FARS was compared.

Results: A total of 38 genes were identified to be independently related to the survival outcome and put into a FARS model. High FARS patients exhibited significantly worse OS. The nomogram included the FARS and pathological stage, and the AUC of the nomogram predicting 1-, 2-, 3-, 4-, and 5-year OS was 0.789, 0.807, 0.798, 0.809, and 0.753, respectively. Calibration plots also indicated good accuracy. Moreover, the samples of the high FARS had higher expression of PDL1.

Conclusion: We constructed a FARS model which could accurately predict the survival outcome of the LUAD patients. The genes of the FARS are related to the tumor microenvironment and patients with high FARS can potentially benefit more from anti-PD1/PDL1 immunotherapy. In addition, the mechanisms of the genes in the FARS affecting prognosis are worthy of further research to develop new gene-targeted drugs.

CD8+ T Cell-Associated Gene Signature Correlates With Prognosis Risk and Immunotherapy Response in Patients With Lung Adenocarcinoma

The presence of infiltrating CD8⁺ T lymphocytes in the tumor microenvironment of lung adenocarcinoma (LUAD) is correlated with improved patient prognosis, but underlying regulatory mechanisms remain unknown. To identify biomarkers to improve early diagnosis and treatment of LUAD, we downloaded 13 immune cell line-associated datasets from the GEO database. We identified CD8⁺ T cell-associated genes via weighted correlation network analysis. We constructed molecular subtypes based on CD8⁺ T cell-associated genes and constructed a multi-gene signature. We identified 252 CD8⁺ T cell-associated genes significantly enriched in immune function-related pathways and two molecular subtypes of LUAD (immune cluster 1 [IC1] and IC2) using our CD8⁺ T cell-associated gene signature. Patients with the IC2 subtype had a higher tumor mutation burden and lower immune infiltration scores, whereas those with the IC1 subtype were more sensitive to immune checkpoint inhibitors. Prioritizing the top candidate genes to construct a 10-gene signature, we validated our model using independent GSE and TCGA datasets to confirm its robustness and stable prognostic ability. Our risk model demonstrated good predictive efficacy using the Imvigor210 immunotherapy dataset. Thus, we established a novel and robust CD8⁺ T cell-associated gene signature, which could help assess prognostic risk and immunotherapy response in LUAD patients.

BEX1 and BEX4 Induce GBM Progression through Regulation of Actin Polymerization and Activation of YAP/TAZ Signaling

GBM is a high-grade cancer that originates from glial cells and has a poor prognosis. Although a combination of surgery, radiotherapy, and chemotherapy is prescribed to patients, GBM is highly resistant to therapies, and surviving cells show increased aggressiveness. In this study, we investigated the molecular mechanism underlying GBM progression after radiotherapy by establishing a GBM orthotopic xenograft mouse model. Based on transcriptomic analysis, we found that the expression of BEX1 and BEX4 was upregulated in GBM cells surviving radiotherapy. We also found that upregulated expression of BEX1 and BEX4 was involved in the formation of the filamentous cytoskeleton and altered mechanotransduction, which resulted in the activation of the YAP/TAZ signaling pathway. BEX1- and BEX4-mediated YAP/TAZ activation enhanced the tumor formation, growth, and radioresistance of GBM cells. Additionally, latrunculin B inhibited GBM progression after radiotherapy by suppressing actin polymerization in an orthotopic xenograft mouse model. Taken together, we suggest the involvement of cytoskeleton formation in radiation-induced GBM progression and latrunculin B as a GBM radiosensitizer.

Interrupting Neuron-Tumor Interactions to Overcome Treatment Resistance

Neurons in the tumor microenvironment release neurotransmitters, neuroligins, chemokines, soluble growth factors, and membrane-bound growth factors that solid tumors leverage to drive their own survival and spread. Tumors express nerve-specific growth factors and microRNAs that support local neurons and guide neuronal growth into tumors. The development of feed-forward relationships between tumors and neurons allows tumors to use the perineural space as a sanctuary from therapy. Tumor denervation slows tumor growth in animal models, demonstrating the innervation dependence of growing tumors. Further in vitro and in vivo experiments have identified many of the secreted signaling molecules (e.g., acetylcholine, nerve growth factor) that are passed between neurons and cancer cells, as well as the major signaling pathways (e.g., MAPK/EGFR) involved in these trophic interactions. The molecules involved in these signaling pathways serve as potential biomarkers of disease. Additionally, new treatment strategies focus on using small molecules, receptor agonists, nerve-specific toxins, and surgical interventions to target tumors, neurons, and immune cells of the tumor microenvironment, thereby severing the interactions between tumors and surrounding neurons. This article discusses the mechanisms underlying the trophic relationships formed between neurons and tumors and explores the emerging therapies stemming from this work.