MicroRNA, mRNA, and Proteomics Biomarkers and Therapeutic Targets for Improving Lung Cancer Treatment Outcomes

MicroRNA-99 family in cancer: molecular mechanisms for clinical applications

MicroRNAs (miRNAs) are a class of non-coding RNA sequences that regulate gene expression post-transcriptionally. The miR-99 family, which is highly evolutionarily conserved, comprises three homologs: miR-99a, miR-99b, and miR-100. Its members are under-expressed in most cancerous tissues, suggesting their cancer-repressing properties in multiple cancers; however, in some contexts, they also promote malignant lesion progression. MiR-99 family members target numerous genes involved in various tumor-related processes such as tumorigenesis, proliferation, cell-cycle regulation, apoptosis, invasion, and metastasis. We review the recent research on this family, summarize its implications in cancer, and explore its potential as a biomarker and cancer therapeutic target. This review contributes to the clinical translation of the miR-99 family members.

Interplay Between Traditional and Scientific Knowledge: Phytoconstituents and Their Roles in Lung and Colorectal Cancer Signaling Pathways

Natural plant products have been used for cancer treatment since ancient times and continue to play a vital role in modern anticancer drug development. However, only a small fraction of identified medicinal plants has been thoroughly investigated, particularly for their effects on cellular pathways in lung and colorectal cancers, two under-researched cancers with poor prognostic outcomes (lung cancers). This review focuses on the lung and colorectal cancer signaling pathways modulated by bioactive compounds from eleven traditional medicinal plants: Curcuma longa, Astragalus membranaceus, Glycyrrhiza glabra, Althaea officinalis, Echinacea purpurea, Sanguinaria canadensis, Codonopsis pilosula, Hydrastis canadensis, Lobelia inflata, Scutellaria baicalensis, and Zingiber officinale. These plants were selected based on their documented use in traditional medicine and modern clinical practice. Selection criteria involved cross-referencing herbs identified in a scoping review of traditional cancer treatments and findings from an international survey on herbal medicine currently used for lung and colorectal cancer management by our research group and the availability of existing literature on their anticancer properties. The review identifies several isolated phytoconstituents from these plants that exhibit anticancer properties by modulating key signaling pathways such as PI3K/Akt/mTOR, RAS/RAF/MAPK, Wnt/β-catenin, and TGF-β in vitro. Notable constituents include sanguinarine, berberine, hydrastine, lobeline, curcumin, gingerol, shogaol, caffeic acid, echinacoside, cichoric acid, glycyrrhizin, 18-β-glycyrrhetinic acid, astragaloside IV, lobetyolin, licochalcone A, baicalein, baicalin, wogonin, and glycyrol. Curcumin and baicalin show preclinical effectiveness but face bioavailability challenges, which may be overcome by combining them with piperine or using oral extracts to enhance gut microbiome conversion, integrating traditional knowledge with modern strategies for improved outcomes. Furthermore, herbal extracts from Echinacea, Glycyrrhiza, and Codonopsis, identified in traditional knowledge, are currently in clinical trials. Notably, curcumin and baicalin also modulate miRNA pathways, highlighting a promising intersection of modern science and traditional medicine. Thus, the development of anticancer therapeutics continues to benefit from the synergy of traditional knowledge, scientific innovation, and technological advancements.

Liquid biopsy in lung cancer: The role of circulating tumor cells in diagnosis, treatment, and prognosis

Despite numerous therapeutic advancements, such as immune checkpoint inhibitors, lung cancer continues to be the leading cause of cancer-related mortality. Therefore, the identification of cancer at an early stage is becoming a significant subject in contemporary oncology. Despite significant advancements in early detection tactics in recent decades, they continue to provide challenges because of the inconspicuous symptoms observed during the early stages of the primary tumor. Presently, tumor biomarkers and imaging techniques are extensively employed across different forms of cancer. Nevertheless, every approach has its own set of constraints. In certain instances, the detriments outweigh the advantages. Hence, there is an urgent need to enhance early detection methods. Currently, liquid biopsy is considered more flexible and not intrusive method in comparison to conventional test for early detection. Circulating tumor cells (CTCs) are crucial components of liquid biopsy and have a pivotal function in the spread and formation of secondary tumors. These indicators show great promise in the early identification of cancer. This study presents a comprehensive examination of the methodologies employed for the isolation and enrichment of circulating tumor cells (CTCs) in lung cancer. Additionally, it explores the formation of clusters of CTCs, which have a pivotal function in facilitating the effective dissemination of cancer to distant organs. In addition, we discuss the importance of CTCs in the detection, treatment, and prognosis of lung cancer.

Research and application of omics and artificial intelligence in cancer

Cancer has a high incidence and lethality rate, which is a significant threat to human health. With the development of high-throughput technologies, different types of cancer genomics data have been accumulated, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. A comprehensive analysis of various omics data is needed to understand the underlying mechanisms of tumor development. However, integrating such a massive amount of data is one of the main challenges today. Artificial intelligence (AI) techniques such as machine learning are now becoming practical tools for analyzing and understanding multi-omics data on diseases. Enabling great optimization of existing research paradigms for cancer screening, diagnosis, and treatment. In addition, intelligent healthcare has received widespread attention with the development of healthcare informatization. As an essential part of innovative healthcare, practical, intelligent prognosis analysis and personalized treatment for cancer patients are also necessary. This paper introduces the advanced multi-omics data analysis technology in recent years, presents the cases and advantages of the combination of both omics data and AI applied to cancer diseases, and finally briefly describes the challenges faced by multi-omics analysis and AI at the current stage, aiming to provide new perspectives for oncology research and the possibility of personalized cancer treatment.

Smoking-Mediated miR-301a/IRF1 Axis Controlling Immunotherapy Response in Lung Squamous Cell Carcinoma Revealed by Bioinformatic Analysis

Smoking is an established risk factor for a variety of malignant tumors, the most well-known of which is lung cancer. Various molecular interactions are known to link tobacco smoke exposure to lung cancer, but new data are still emerging on the effects of smoking on lung cancer development, progression, and tumor response to therapy. In this study, we reveal in further detail the previously established association between smoking and hsa-mir-301a activity in lung squamous cell carcinoma, LUSC. Using different bioinformatic tools, we identified IRF1 as a key smoking-regulated target of hsa-mir-301a in LUSC. We further confirmed this relationship experimentally using clinical LUSC tissue samples and intact lung tissue samples. Thus, increased hsa-mir-301a levels, decreased IRF1 mRNA levels, and their negative correlation were shown in LUSC tumor samples. Additional bioinformatic investigation for potential pathways impacted by such a mechanism demonstrated IRF1's multifaceted role in controlling the antitumor immune response in LUSC. IRF1 was then shown to affect tumor immune infiltration, the expression of immune checkpoint molecules, and the efficacy of immune checkpoint blockade therapy. As a result, here we suggest a smoking-regulated mir301a/IRF1 molecular axis that could modulate the antitumor immune response and immunotherapy efficacy in LUSC, opening up novel opportunities for future research.

A novel lipid metabolism-based risk model associated with immunosuppressive mechanisms in diffuse large B-cell lymphoma

BACKGROUND

The molecular diversity exhibited by diffuse large B-cell lymphoma (DLBCL) is a significant obstacle facing current precision therapies. However, scoring using the International Prognostic Index (IPI) is inadequate when fully predicting the development of DLBCL. Reprogramming lipid metabolism is crucial for DLBCL carcinogenesis and expansion, while a predictive approach derived from lipid metabolism-associated genes (LMAGs) has not yet been recognized for DLBCL.

METHODS

Gene expression profiles of DLBCL were generated using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The LASSO Cox regression was used to construct an effective predictive risk-scoring model for DLBCL patients. The Kaplan-Meier survival assessment was employed to compare a given risk score with the IPI score and its impact on the survival of DLBCL patients. Functional enrichment examination was performed utilizing the KEGG pathway. After identifying hub genes via single-sample GSEA (ssGSEA), immunohistochemical staining and immunofluorescence were performed on lymph node samples from control and DLBCL patients to confirm these identified genes.

RESULTS

Sixteen lipid metabolism- and survival-associated genes were identified to construct a prognostic risk-scoring approach. This model demonstrated robust performance over various datasets and emerged as an autonomous risk factor for predicting the development of DLBCL patients. The risk score could significantly distinguish the development of DLBCL patients from the low-risk and elevated-risk IPI classes. Results from the inhibitory immune-related pathways and lower immune scores suggested an immunosuppressive phenotype within the elevated-risk group. Three hub genes, MECR, ARSK, and RAN, were identified to be negatively correlated with activated CD8 T cells and natural killer T cells in the elevated-risk score class. Ultimately, it was determined that these three genes were expressed by lymphoma cells but not by T cells in clinical samples from DLBCL patients.

CONCLUSION

The risk level model derived from 16 lipid metabolism-associated genes represents a prognostic biomarker for DLBCL that is novel, robust, and may have an immunosuppressive role. It can compensate for the limitations of the IPI score in predicting overall survival and has potential clinical application value.

Generative Adversarial Matrix Completion Network based on Multi-Source Data Fusion for miRNA-Disease Associations Prediction

Numerous biological studies have shown that considering disease-associated micro RNAs (miRNAs) as potential biomarkers or therapeutic targets offers new avenues for the diagnosis of complex diseases. Computational methods have gradually been introduced to reveal disease-related miRNAs. Considering that previous models have not fused sufficiently diverse similarities, that their inappropriate fusion methods may lead to poor quality of the comprehensive similarity network and that their results are often limited by insufficiently known associations, we propose a computational model called Generative Adversarial Matrix Completion Network based on Multi-source Data Fusion (GAMCNMDF) for miRNA-disease association prediction. We create a diverse network connecting miRNAs and diseases, which is then represented using a matrix. The main task of GAMCNMDF is to complete the matrix and obtain the predicted results. The main innovations of GAMCNMDF are reflected in two aspects: GAMCNMDF integrates diverse data sources and employs a nonlinear fusion approach to update the similarity networks of miRNAs and diseases. Also, some additional information is provided to GAMCNMDF in the form of a 'hint' so that GAMCNMDF can work successfully even when complete data are not available. Compared with other methods, the outcomes of 10-fold cross-validation on two distinct databases validate the superior performance of GAMCNMDF with statistically significant results. It is worth mentioning that we apply GAMCNMDF in the identification of underlying small molecule-related miRNAs, yielding outstanding performance results in this specific domain. In addition, two case studies about two important neoplasms show that GAMCNMDF is a promising prediction method.