Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis

Discovery of PELATON links to the INHBA gene in the TGF-β pathway in colorectal cancer using a combination of bioinformatics and experimental investigations

Colorectal cancer (CRC) is a major worldwide health issue, with high rates of both occurrence and mortality. Dysregulation of the transforming growth factor-beta (TGF-β) signaling pathway is recognized as a pivotal factor in CRC pathogenesis. Notably, the INHBA gene and long non-coding RNAs (lncRNAs) have emerged as key contributors to CRC progression. The aim of this research is to explore the immunological roles of INHBA and PELATON in CRC through a combination of computational predictions and experimental validations, with the goal of enhancing diagnostic and therapeutic strategies. In this study, we utilized bioinformatics analyses, which involved examining differential gene expression (DEG) in the TCGA-COAD dataset and exploring the INHBA gene in relation to the TGF-β pathway. Additionally, we analyzed mutations of INHBA, evaluated the microenvironment and tumor purity, investigated the INHBA's connection to immune checkpoint inhibitors, and measured its potential as an immunotherapy target using the TIDE score. Utilizing bioinformatics analyses of the TCGA-COAD dataset beside experimental methodologies such as RT-qPCR, our investigation revealed significant upregulation of INHBA in CRC. As results, our analysis of the protein-protein interaction network associated with INHBA showed 10 interacting proteins that play a role in CRC-associated processes. We observed a notable prevalence of mutations within INHBA and explored its correlation with the response to immune checkpoint inhibitors. Our study highlights INHBA as a promising target for immunotherapy in CRC. Moreover, our study identified PELATON as a closely correlated lncRNA with INHBA, with experimental validation confirming their concurrent upregulation in CRC tissues. Thus, these findings highlight the importance of INHBA and PELATON in driving CRC progression, suggesting their potential utility as diagnostic and prognostic biomarkers. By integrating computational predictions with experimental validations, this research enhances our understanding of CRC pathogenesis and uncovers prospects for personalized therapeutic interventions.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Network-based identification of key proteins and repositioning of drugs for non-small cell lung cancer

BACKGROUND

NSCLC is a lethal cancer that is highly prevalent and accounts for 85% of cases of lung cancer. Conventional cancer treatments, such as chemotherapy and radiation, frequently exhibit limited efficacy and notable adverse reactions. Therefore, a drug repurposing method is proposed for effective NSCLC treatment.

AIMS

This study aims to evaluate candidate drugs that are effective for NSCLC at the clinical level using a systems biology and network analysis approach.

METHODS

Differentially expressed genes in transcriptomics data were identified using the systems biology and network analysis approaches. A network of gene co-expression was developed with the aim of detecting two modules of gene co-expression. Following that, the Drug-Gene Interaction Database was used to find possible drugs that target important genes within two gene co-expression modules linked to non-small cell lung cancer (NSCLC). The use of Cytoscape facilitated the creation of a drug-gene interaction network. Finally, gene set enrichment analysis was done to validate candidate drugs.

RESULTS

Unlike previous research on repositioning drugs for NSCLC, which uses a gene co-expression network, this project is the first to research both gene co-expression and co-occurrence networks. And the co-occurrence network also accounts for differentially expressed genes in cancer cells and their adjacent normal cells. For effective management of non-small cell lung cancer (NSCLC), drugs that show higher gene regulation and gene affinity within the drug-gene interaction network are thought to be important. According to the discourse, NSCLC genes have a lot of control over medicines like vincristine, fluorouracil, methotrexate, clotrimazole, etoposide, tamoxifen, sorafenib, doxorubicin, and pazopanib.

CONCLUSION

Hence, there is a possibility of repurposing these drugs for the treatment of non-small-cell lung cancer.

Mitoxantrone and abacavir: An ALK protein-targeted in silico proposal for the treatment of non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is a type of lung cancer associated with translocation of the EML4 and ALK genes on the short arm of chromosome 2. This leads to the development of an aberrant protein kinase with a deregulated catalytic domain, the cdALK+. Currently, different ALK inhibitors (iALKs) have been proposed to treat ALK+ NSCLC patients. However, the recent resistance to iALKs stimulates the exploration of new iALKs for NSCLC. Here, we describe an in silico approach to finding FDA-approved drugs that can be used by pharmacological repositioning as iALK. We used homology modelling to obtain a structural model of cdALK+ protein and then performed molecular docking and molecular dynamics of the complex cdALK+-iALKs to generate the pharmacophore model. The pharmacophore was used to identify potential iALKs from FDA-approved drugs library by ligand-based virtual screening. Four pharmacophores with different atomistic characteristics were generated, resulting in six drugs that satisfied the proposed atomistic positions and coupled at the ATP-binding site. Mitoxantrone, riboflavin and abacavir exhibit the best interaction energies with 228.29, 165.40 and 133.48 KJoul/mol respectively. In addition, the special literature proposed these drugs for other types of diseases due to pharmacological repositioning. This study proposes FDA-approved drugs with ALK inhibitory characteristics. Moreover, we identified pharmacophores sites that can be tested with other pharmacological libraries.

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.

Open MoA: revealing the mechanism of action (MoA) based on network topology and hierarchy

MOTIVATION

Many approaches in systems biology have been applied in drug repositioning due to the increased availability of the omics data and computational biology tools. Using a multi-omics integrated network, which contains information of various biological interactions, could offer a more comprehensive inspective and interpretation for the drug mechanism of action (MoA).

RESULTS

We developed a computational pipeline for dissecting the hidden MoAs of drugs (Open MoA). Our pipeline computes confidence scores to edges that represent connections between genes/proteins in the integrated network. The interactions showing the highest confidence score could indicate potential drug targets and infer the underlying molecular MoAs. Open MoA was also validated by testing some well-established targets. Additionally, we applied Open MoA to reveal the MoA of a repositioned drug (JNK-IN-5A) that modulates the PKLR expression in HepG2 cells and found STAT1 is the key transcription factor. Overall, Open MoA represents a first-generation tool that could be utilized for predicting the potential MoA of repurposed drugs and dissecting de novo targets for developing effective treatments.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/XinmengLiao/Open_MoA.

Association between cumulative exposure periods of flupentixol or any antipsychotics and risk of lung cancer

BACKGROUND

Preclinical evidence suggests that certain antipsychotic medications may inhibit the development of lung cancer. This study aims to investigate the association between incident lung cancer and different cumulative exposure periods of flupentixol or any antipsychotics.

METHODS

Using electronic health records from the Hospital Authority in Hong Kong, this nested case-control study included case participants aged 18 years or older with newly diagnosed lung cancer after initiating antipsychotics between January 1, 2003, and August 31, 2022. Each case was matched to up to ten controls of the same sex and age, who were also antipsychotic users. Multivariable conditional logistic regression models were conducted to quantify the association between lung cancer and different cumulative exposure times of flupentixol (0-365 days [ref]; 366-1825 days; 1826+ days) and any antipsychotics (1-365 days [ref]; 366-1825 days; 1826+ days), separately.

RESULTS

Here we show that among 6435 cases and 64,348 matched controls, 64.06% are males, and 52.98% are aged 65-84 years. Compared to patients with less than 365 days of exposure, those with 366-1825 days of exposure to flupentixol (OR = 0.65 [95% CI, 0.47-0.91]) and any antipsychotics (0.42 [0.38-0.45]) have a lower risk of lung cancer. A decreased risk is observed in patients who have 1826+ days of cumulative use of any antipsychotics (0.54 [0.47-0.60]).

CONCLUSIONS

A reduced risk of lung cancer is observed in patients with more than one year of exposure to flupentixol or any antipsychotics. Further research on the association between lung cancer and other antipsychotic agents is warranted.

Identification of novel potential drugs and miRNAs biomarkers in lung cancer based on gene co-expression network analysis

Non-small cell lung cancer (NSCLC) is an important cause of cancer-associated deaths worldwide. Therefore, the exact molecular mechanisms of NSCLC are unidentified. The present investigation aims to identify the miRNAs with predictive value in NSCLC. The two datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEmiRNA) and mRNAs (DEmRNA) were selected from the normalized data. Next, miRNA-mRNA interactions were determined. Then, co-expression network analysis was completed using the WGCNA package in R software. The co-expression network between DEmiRNAs and DEmRNAs was calculated to prioritize the miRNAs. Next, the enrichment analysis was performed for DEmiRNA and DEmRNA. Finally, the drug-gene interaction network was constructed by importing the gene list to dgidb database. A total of 3,033 differentially expressed genes and 58 DE miRNA were recognized from two datasets. The co-expression network analysis was utilized to build a gene co-expression network. Next, four modules were selected based on the Zsummary score. In the next step, a bipartite miRNA-gene network was constructed and hub miRNAs (let-7a-2-3p, let-7d-5p, let-7b-5p, let-7a-5p, and let-7b-3p) were selected. Finally, a drug-gene network was constructed while SUNITINIB, MEDROXYPROGESTERONE ACETATE, DOFETILIDE, HALOPERIDOL, and CALCITRIOL drugs were recognized as a beneficial drug in NSCLC. The hub miRNAs and repurposed drugs may act a vital role in NSCLC progression and treatment, respectively; however, these results must validate in further clinical and experimental assessments.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.