Natural products: Potential targets of TME related long non-coding RNAs in lung cancer

Modulation of long noncoding RNAs by polyphenols as a novel potential therapeutic approach in lung cancer: A comprehensive review

Lung cancer stands as a formidable global health challenge, necessitating innovative therapeutic strategies. Polyphenols, bioactive compounds synthesized by plants, have garnered attention for their diverse health benefits, particularly in combating various cancers, including lung cancer. The advent of whole-genome and transcriptome sequencing technologies has illuminated the pivotal roles of long noncoding RNAs (lncRNAs), operating at epigenetic, transcriptional, and posttranscriptional levels, in cancer progression. This review comprehensively explores the impact of polyphenols on both oncogenic and tumor-suppressive lncRNAs in lung cancer, elucidating on their intricate regulatory mechanisms. The comprehensive examination extends to the potential synergies when combining polyphenols with conventional treatments like chemotherapy, radiation, and immunotherapy. Recognizing the heterogeneity of lung cancer subtypes, the review emphasizes the need for the integration of nanotechnology for optimized polyphenol delivery and personalized therapeutic approaches. In conclusion, we collect the latest research, offering a holistic overview of the evolving landscape of polyphenol-mediated modulation of lncRNAs in lung cancer therapy. The integration of polyphenols and lncRNAs into multidimensional treatment strategies holds promise for enhancing therapeutic efficacy and navigating the challenges associated with lung cancer treatment.

The hidden messengers: cancer associated fibroblasts-derived exosomal miRNAs as key regulators of cancer malignancy

Cancer-associated fibroblasts (CAFs), a class of stromal cells in the tumor microenvironment (TME), play a key role in controlling cancer cell invasion and metastasis, immune evasion, angiogenesis, and resistance to chemotherapy. CAFs mediate their activities by secreting soluble chemicals, releasing exosomes, and altering the extracellular matrix (ECM). Exosomes contain various biomolecules, such as nucleic acids, lipids, and proteins. microRNA (miRNA), a 22-26 nucleotide non-coding RNA, can regulate the cellular transcription processes. Studies have shown that miRNA-loaded exosomes secreted by CAFs engage in various regulatory communication networks with other TME constituents. This study focused on the roles of CAF-derived exosomal miRNAs in generating cancer malignant characteristics, including immune modulation, tumor growth, migration and invasion, epithelial-mesenchymal transition (EMT), and treatment resistance. This study thoroughly examines miRNA's dual regulatory roles in promoting and suppressing cancer. Thus, changes in the CAF-derived exosomal miRNAs can be used as biomarkers for the diagnosis and prognosis of patients, and their specificity can be used to develop newer therapies. This review also discusses the pressing problems that require immediate attention, aiming to inspire researchers to explore more novel avenues in this field.

Targeting tumor-associated macrophage: an adjuvant strategy for lung cancer therapy

The emergence of immunotherapy has revolutionized the treatment landscape for various types of cancer. Nevertheless, lung cancer remains one of the leading causes of cancer-related mortality worldwide due to the development of resistance in most patients. As one of the most abundant groups of immune cells in the tumor microenvironment (TME), tumor-associated macrophages (TAMs) play crucial and complex roles in the development of lung cancer, including the regulation of immunosuppressive TME remodeling, metabolic reprogramming, neoangiogenesis, metastasis, and promotion of tumoral neurogenesis. Hence, relevant strategies for lung cancer therapy, such as inhibition of macrophage recruitment, TAM reprograming, depletion of TAMs, and engineering of TAMs for drug delivery, have been developed. Based on the satisfactory treatment effect of TAM-targeted therapy, recent studies also investigated its synergistic effect with current therapies for lung cancer, including immunotherapy, radiotherapy, chemotherapy, anti-epidermal growth factor receptor (anti-EGFR) treatment, or photodynamic therapy. Thus, in this article, we summarized the key mechanisms of TAMs contributing to lung cancer progression and elaborated on the novel therapeutic strategies against TAMs. We also discussed the therapeutic potential of TAM targeting as adjuvant therapy in the current treatment of lung cancer, particularly highlighting the TAM-centered strategies for improving the efficacy of anti-programmed cell death-1/programmed cell death-ligand 1 (anti-PD-1/PD-L1) treatment.

An Update on Potential Molecular Biomarkers of Dietary Phytochemicals Targeting Lung Cancer Interception and Prevention

Since ancient times, dietary phytochemicals are known for their medicinal properties. They are broadly classified into polyphenols, terpenoids, alkaloids, phytosterols, and organosulfur compounds. Currently, there is considerable interest in their potential health effects against various diseases, including lung cancer. Lung cancer is the leading cause of cancer deaths with an average of five-year survival rate of lung cancer patients limited to just 14%. Identifying potential early molecular biomarkers of pre-malignant lung cancer cells may provide a strong basis to develop early cancer detection and interception methods. In this review, we will discuss molecular changes, including genetic alterations, inflammation, signal transduction pathways, redox imbalance, epigenetic and proteomic signatures associated with initiation and progression of lung carcinoma. We will also highlight molecular targets of phytochemicals during lung cancer development. These targets mainly consist of cellular signaling pathways, epigenetic regulators and metabolic reprogramming. With growing interest in natural products research, translation of these compounds into new cancer prevention approaches to medical care will be urgently needed. In this context, we will also discuss the overall pharmacokinetic challenges of phytochemicals in translating to humans. Lastly, we will discuss clinical trials of phytochemicals in lung cancer patients.

Screening and identifying a novel M-MDSCs-related gene signature for predicting prognostic risk and immunotherapeutic responses in patients with lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) shows intratumoral heterogeneity, a highly complex phenomenon that known to be a challenge during cancer therapy. Considering the key role of monocytic myeloid-derived suppressor cells (M-MDSCs) in the tumor microenvironment (TME), we aimed to build a prognostic risk model using M-MDSCs-related genes. Methods: M-MDSCs-related genes were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Utilized univariate survival analysis and random forest algorithm to screen candidate genes. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was selected to build the risk model. Patients were scored and classified into high- and low-risk groups based on the median risk scores. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis along with R packages "estimate" and "ssGSEA" were performed to reveal the mechanism of risk difference. Prognostic biomarkers and tumor mutation burden (TMB) were combined to predict the prognosis. Nomogram was carried out to predict the survival probability of patients in 1, 3, and 5 years. Results: 8 genes (VPREB3, TPBG, LRFN4, CD83, GIMAP6, PRMT8, WASF1, and F12) were identified as prognostic biomarkers. The GEO validation dataset demonstrated the risk model had good generalization effect. Significantly enrichment level of cell cycle-related pathway and lower content of CD8⁺ T cells infiltration in the high-risk group when compared to low-risk group. Morever, the patients were from the intersection of high-TMB and low-risk groups showed the best prognosis. The nomogram demonstrated good consistency with practical outcomes in predicting the survival rate over 1, 3, and 5 years. Conclusion: The risk model demonstrate good prognostic predictive ability. The patients from the intersection of low-risk and high-TMB groups are not only more sensitive response to but also more likely to benefit from immune-checkpoint-inhibitors (ICIs) treatment.

An innovative pyroptosis-related long-noncoding-RNA signature predicts the prognosis of gastric cancer via affecting immune cell infiltration landscape

Background: Gastric cancer (GC) is a worldwide popular malignant tumor. However, the survival rate of advanced GC remains low. Pyroptosis and long non-coding RNAs (lncRNAs) are important in cancer progression. Thus, we aimed to find out a pyroptosis-related lncRNAs (PRLs) signature and use it to build a practical risk model with the purpose to predict the prognosis of patients with GC. Methods: Univariate Cox regression analysis was used to identify PRLs linked to GC patient's prognosis. Subsequently, to construct a PRLs signature, the least absolute shrinkage and selection operator regression, and multivariate Cox regression analysis were used. Kaplan-Meier analysis, principal component analysis, and receiver operating characteristic curve analysis were performed to assess our novel lncRNA signature. The correlation between risk signature and clinicopathological features was also examined. Finally, the relationship of pyroptosis and immune cells were evaluated through the CIBERSORT tool and single-sample lncRNA set enrichment analysis (ssGSEA). Results: A PRLs signature comprising eight lncRNAs was discerned as a self-determining predictor of prognosis. GC patients were sub-divided into high-risk and low-risk groups via this risk-model. Stratified analysis of different clinical factors also displayed that the PRLs signature was a good prognosis factor. According to the risk score and clinical characteristics, a nomogram was established. Moreover, the difference between the groups is significance in immune cells and immune pathways. Conclusion: This study established an effective prognostic signature consist of eight PRLs in GC, and constructed an efficient nomogram model. Further, the PRLs correlated with immune cells and immune pathways.