Non-coding RNAs as Novel Biomarkers in Cancer Drug Resistance

FOXO1, a tiny protein with intricate interactions: Promising therapeutic candidate in lung cancer

Nowadays, lung cancer is the most common cause of cancer-related deaths in both men and women globally. Despite the development of extremely efficient targeted agents, lung cancer progression and drug resistance remain serious clinical issues. Increasing knowledge of the molecular mechanisms underlying progression and drug resistance will enable the development of novel therapeutic methods. It has been revealed that transcription factors (TF) dysregulation, which results in considerable expression modifications of genes, is a generally prevalent phenomenon regarding human malignancies. The forkhead box O1 (FOXO1), a member of the forkhead transcription factor family with crucial roles in cell fate decisions, is suggested to play a pivotal role as a tumor suppressor in a variety of malignancies, especially in lung cancer. FOXO1 is involved in diverse cellular processes and also has clinical significance consisting of cell cycle arrest, apoptosis, DNA repair, oxidative stress, cancer prevention, treatment, and chemo/radioresistance. Based on the critical role of FOXO1, this transcription factor appears to be an appropriate target for future drug discovery in lung cancers. This review focused on the signaling pathways, and molecular mechanisms involved in FOXO1 regulation in lung cancer. We also discuss pharmacological compounds that are currently being administered for lung cancer treatment by affecting FOXO1 and also point out the essential role of FOXO1 in drug resistance. Future preclinical research should assess combination drug strategies to stimulate FOXO1 and its upstream regulators as potential strategies to treat resistant or advanced lung cancers.

MIAT shuttled by tumor-secreted exosomes promotes paclitaxel resistance in esophageal cancer cells by activating the TAF1/SREBF1 axis

Chemoresistance remains a major obstacle to the treatment of esophageal cancer (EC). Exosome-mediated transfer of long noncoding RNAs (lncRNAs) has recently been unveiled to correlate with the regulation of drug resistance in EC. This study aimed to investigate the physiological mechanisms by which exosome-encapsulated lncRNA myocardial infarction-associated transcript (MIAT) derived from tumor cells might mediate the paclitaxel (PTX) resistance of EC cells. First, MIAT was experimentally determined to be upregulated in PTX nonresponders and PTX-resistant EC cells. Silencing of MIAT in PTX-resistant EC cells decreased cell viability and enhanced apoptosis, corresponding to a reduced half-maximal inhibitory concentration (IC50 ) value. Next, exosomes were isolated from EC109 and EC109/T cells, and EC109 cells were cocultured with EC109/T-cell-derived exosomes. Accordingly, MIAT was revealed to be transmitted through exosomes from EC109/T cells to EC109 cells. Tumor-derived exosomes carrying MIAT increased the IC50 value of PTX and suppressed apoptosis in EC109 cells to promote PTX resistance. Furthermore, MIAT promoted the enrichment of TATA-box binding protein-associated Factor 1 (TAF1) in the promoter region of sterol regulatory element binding transcription factor 1 (SREBF1), as shown by a chromatin immunoprecipitation assay. This might be the mechanism by which MIAT could promote PTX resistance. Finally, in vivo experiments further confirmed that the knockdown of MIAT attenuated the resistance of EC cells to PTX. Collectively, these results indicate that tumor-derived exosome-loaded MIAT activates the TAF1/SREBF1 axis to induce PTX resistance in EC cells, providing a potential therapeutic target for overcoming PTX resistance in EC.

RDRGSE: A Framework for Noncoding RNA-Drug Resistance Discovery by Incorporating Graph Skeleton Extraction and Attentional Feature Fusion

Identifying noncoding RNAs (ncRNAs)-drug resistance association computationally would have a marked effect on understanding ncRNA molecular function and drug target mechanisms and alleviating the screening cost of corresponding biological wet experiments. Although graph neural network-based methods have been developed and facilitated the detection of ncRNAs related to drug resistance, it remains a challenge to explore a highly trusty ncRNA-drug resistance association prediction framework, due to inevitable noise edges originating from the batch effect and experimental errors. Herein, we proposed a framework, referred to as RDRGSE (RDR association prediction by using graph skeleton extraction and attentional feature fusion), for detecting ncRNA-drug resistance association. Specifically, starting with the construction of the original ncRNA-drug resistance association as a bipartite graph, RDRGSE took advantage of a bi-view skeleton extraction strategy to obtain two types of skeleton views, followed by a graph neural network-based estimator for iteratively optimizing skeleton views aimed at learning high-quality ncRNA-drug resistance edge embedding and optimal graph skeleton structure, jointly. Then, RDRGSE adopted adaptive attentional feature fusion to obtain final edge embedding and identified potential RDRAs under an end-to-end pattern. Comprehensive experiments were conducted, and experimental results indicated the significant advantage of a skeleton structure for ncRNA-drug resistance association discovery. Compared with state-of-the-art approaches, RDRGSE improved the prediction performance by 6.7% in terms of AUC and 6.1% in terms of AUPR. Also, ablation-like analysis and independent case studies corroborated RDRGSE generalization ability and robustness. Overall, RDRGSE provides a powerful computational method for ncRNA-drug resistance association prediction, which can also serve as a screening tool for drug resistance biomarkers.

Chemotherapeutic Drug Resistance Associated with Differential miRNA Expression of miR-375 and miR-27 among Oral Cancer Cell Lines

Recent advances have suggested that non-coding miRNAs (such as miR-21, miR-27, miR-145, miR-155, miR-365, miR-375 and miR-494) may be involved in multiple aspects of oral cancer chemotherapeutic responsiveness. This study evaluated whether these specific miRNAs are correlated with oral cancer responsiveness to chemotherapies, including Paclitaxel, Cisplatin and Fluorouracil (5FU). Commercially available and well-characterized oral squamous cell carcinoma cell lines (SCC4, SCC9, SCC15, SCC25 and CAL27) revealed differing resistance and chemosensitivity to these agents-with SCC9 and SCC25 demonstrating the most resistance to all chemotherapeutic agents. SCC9 and SCC25 were also the only cell lines that expressed miR-375, and were the only cell lines that did not express miR-27. In addition, the expression of miR-375 was associated with the upregulation of Rearranged L-myc fusion (RLF) and the downregulation of Centriolar protein B (POC1), whereas lack of miR-27 expression was associated with Nucleophosmin 1 (NPM1) expression. These data have revealed important regulatory pathways and mechanisms associated with oral cancer proliferation and resistance that must be explored in future studies of potential therapeutic interventions.

ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA

In recent years, many studies have illustrated the significant role that non-coding RNA (ncRNA) plays in biological activities, in which lncRNA, miRNA and especially their interactions have been proved to affect many biological processes. Some in silico methods have been proposed and applied to identify novel lncRNA-miRNA interactions (LMIs), but there are still imperfections in their RNA representation and information extraction approaches, which imply there is still room for further improving their performances. Meanwhile, only a few of them are accessible at present, which limits their practical applications. The construction of a new tool for LMI prediction is thus imperative for the better understanding of their relevant biological mechanisms. This study proposed a novel method, ncRNAInter, for LMI prediction. A comprehensive strategy for RNA representation and an optimized deep learning algorithm of graph neural network were utilized in this study. ncRNAInter was robust and showed better performance of 26.7% higher Matthews correlation coefficient than existing reputable methods for human LMI prediction. In addition, ncRNAInter proved its universal applicability in dealing with LMIs from various species and successfully identified novel LMIs associated with various diseases, which further verified its effectiveness and usability. All source code and datasets are freely available at https://github.com/idrblab/ncRNAInter.

Noncoding RNAs Emerging as Drugs or Drug Targets: Their Chemical Modification, Bio-Conjugation and Intracellular Regulation

With the increasing understanding of various disease-related noncoding RNAs, ncRNAs are emerging as novel drugs and drug targets. Nucleic acid drugs based on different types of noncoding RNAs have been designed and tested. Chemical modification has been applied to noncoding RNAs such as siRNA or miRNA to increase the resistance to degradation with minimum influence on their biological function. Chemical biological methods have also been developed to regulate relevant noncoding RNAs in the occurrence of various diseases. New strategies such as designing ribonuclease targeting chimeras to degrade endogenous noncoding RNAs are emerging as promising approaches to regulate gene expressions, serving as next-generation drugs. This review summarized the current state of noncoding RNA-based theranostics, major chemical modifications of noncoding RNAs to develop nucleic acid drugs, conjugation of RNA with different functional biomolecules as well as design and screening of potential molecules to regulate the expression or activity of endogenous noncoding RNAs for drug development. Finally, strategies of improving the delivery of noncoding RNAs are discussed.

Progress in the studies on the molecular mechanisms associated with multidrug resistance in cancers

Chemotherapy is one of the important methods to treat cancer, and the emergence of multidrug resistance (MDR) is one major cause for the failure of cancer chemotherapy. Almost all anti-tumor drugs develop drug resistance over a period of time of application in cancer patients, reducing their effects on killing cancer cells. Chemoresistance can lead to a rapid recurrence of cancers and ultimately patient death. MDR may be induced by multiple mechanisms, which are associated with a complex process of multiple genes, factors, pathways, and multiple steps, and today the MDR-associated mechanisms are largely unknown. In this paper, from the aspects of protein-protein interactions, alternative splicing (AS) in pre-mRNA, non-coding RNA (ncRNA) mediation, genome mutations, variance in cell functions, and influence from the tumor microenvironment, we summarize the molecular mechanisms associated with MDR in cancers. In the end, prospects for the exploration of antitumor drugs that can reverse MDR are briefly discussed from the angle of drug systems with improved targeting properties, biocompatibility, availability, and other advantages.

Curcumin Targeting Non-Coding RNAs in Colorectal Cancer: Therapeutic and Biomarker Implications

Colorectal cancer is one of the most common gastrointestinal malignancies, with high incidence rates, a low rate of early diagnosis, and complex pathogenesis. In recent years, there has been progress made in its diagnosis and treatment methods, but tumor malignant proliferation and metastasis after treatment still seriously affect the survival and prognosis of patients. Therefore, it is an extremely urgent task of current medicine to find new anti-tumor drugs with high efficiency and safety and low toxicity. Curcumin has shown potent anti-tumor and anti-inflammatory effects and is considered a hot spot in the research and development of anti-tumor drugs due to its advantages of precise efficacy, lower toxic side effects, and less drug resistance. Recent studies have revealed that curcumin has anti-tumor effects exerted on the epigenetic regulation of tumor-promoting/tumor-suppressing gene expression through the alteration of expression levels of non-coding RNAs (e.g., lncRNAs, miRNAs, and circRNAs). Herein, we summarize the interaction between curcumin and non-coding RNAs on the occurrence and development of colorectal cancer. The information complied in this review will serve as a scientific and reliable basis and viewpoint for the clinical application of non-coding RNAs in colorectal cancer.

A Systematic Framework for Identifying Prognostic Genes in the Tumor Microenvironment of Colon Cancer

As one of the most common cancers of the digestive system, colon cancer is a predominant cause of cancer-related deaths worldwide. To investigate prognostic genes in the tumor microenvironment of colon cancer, we collected 461 colon adenocarcinoma (COAD) and 172 rectal adenocarcinoma (READ) samples from The Cancer Genome Atlas (TCGA) database, and calculated the stromal and immune scores of each sample. We demonstrated that stromal and immune scores were significantly associated with colon cancer stages. By analyzing differentially expressed genes (DEGs) between two stromal and immune score groups, we identified 952 common DEGs. The significantly enriched Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms for these DEGs were associated with T-cell activation, immune receptor activity, and cytokine–cytokine receptor interaction. Through univariate Cox regression analysis, we identified 22 prognostic genes. Furthermore, nine key prognostic genes, namely, HOXC8, SRPX, CCL22, CD72, IGLON5, SERPING1, PCOLCE2, FABP4, and ARL4C, were identified using the LASSO Cox regression analysis. The risk score of each sample was calculated using the gene expression of the nine genes. Patients with high-risk scores had a poorer prognosis than those with low-risk scores. The prognostic model established with the nine-gene signature was able to effectively predict the outcome of colon cancer patients. Our findings may help in the clinical decisions and improve the prognosis for colon cancer.