Identification of potential therapeutic targets for lung cancer by bioinformatics analysis

Identification of potential regulatory mechanisms and therapeutic targets for lung cancer

Lung cancer poses a significant health threat globally, especially in regions like India, with 5-year survival rates remain alarmingly low. Our study aimed to uncover key markers for effective treatment and early detection. We identified specific genes related to lung cancer using the BioXpress database and delved into their roles through DAVID enrichment analysis. By employing network theory, we explored the intricate interactions within lung cancer networks, identifying ASPM and MKI67 as crucial regulator genes. Predictions of microRNA and transcription factor interactions provided additional insights. Examining gene expression patterns using GEPIA and KM Plotter revealed the clinical relevance of these key genes. In our pursuit of targeted therapies, Drug Bank pointed to methotrexate as a potential drug for the identified key regulator genes. Confirming this, molecular docking studies through Swiss Dock showed promising binding interactions. To ensure stability, we conducted molecular dynamics simulations using the AMBER 16 suite. In summary, our study pinpoints ASPM and MKI67 as vital regulators in lung cancer networks. The identification of hub genes and functional pathways enhances our understanding of molecular processes, offering potential therapeutic targets. Importantly, methotrexate emerged as a promising drug candidate, supported by robust docking and simulation studies. These findings lay a solid foundation for further experimental validations and hold promise for advancing personalized therapeutic strategies in lung cancer.Communicated by Ramaswamy H. Sarma.

Correlation of TIMP1-MMP2/MMP9 Gene Expression Axis Changes with Treatment Efficacy and Survival of NSCLC Patients

In the course of lung cancer, normal cells are transformed into cancerous ones, and changes occur in the microenvironment, including the extracellular matrix (ECM), which is not only a scaffold for cells, but also a reservoir of cytokines, chemokines and growth factors. Metalloproteinases (MMPs) are among the elements that enable ECM remodeling. The publication focuses on the problem of changes in the gene expression of MMP2, MMP9 and tissue inhibitor of metalloproteinases (TIMP1) in the blood of NSCLC patients during therapy (one year after surgical resection of the tumor). The paper also analyzes differences in the expression of the studied genes in the tumor tissue, as well as data collected in publicly available databases. The results of blood tests showed no differences in the expression of the tested genes during therapy; however, changes were observed in cancerous tissue, which was characterized by higher expression of MMP2 and MMP9, compared to non-cancerous tissue, and unchanged expression of TIMP1. Nevertheless, higher expression of each of the studied genes was associated with shorter patient survival. Interestingly, it was not only the increased expression of metalloproteinase genes, but also the increased expression of the metalloproteinase inhibitor (TIMP1) that was unfavorable for patients.

Protein degradation-associated mechanisms that are affected in Alzheimer´s disease

Alzheimer's disease (AD) is the most common type of dementia associated with age-related neurodegeneration. Alteration of several molecular mechanisms has been correlated with the progression of AD. In recent years, dysregulation of proteostasis-associated pathways has emerged as a potential risk factor for neurodegenerative diseases. This review investigated the ubiquitin-proteasome system, lysosome-associated degradation, endoplasmic-reticulum-associated degradation, and the formation of advanced glycation end products. These pathways involved in proteostasis have been reported to be altered in AD, suggesting that their study may be critical for identifying new biomarkers and target molecules for AD.

Brassica juncea (L.) Czern. leaves alleviate adjuvant-induced rheumatoid arthritis in rats via modulating the finest disease targets - IL2RA, IL18 and VEGFA

Brassica juncea (BJ) is a familiar edible crop, which has been used as a dietary ingredient and to prepare anti-inflammatory/anti-arthritic formulations in Ayurveda. But, the scientific validation or confirmation of its therapeutic properties is very limited. This study was performed to determine the efficiency of BJ leaves for the treatment of Rheumatoid arthritis using in vivo and in silico systems. Standard in vitro procedures was followed to study the total phenolic, flavonoid contents and free radical scavenging ability of the extracts of BJ. The effective extract was screened and the presence of bioactive chemicals was studied using HPLC. Further, the possible therapeutic actions of the BJ active principles against the disease targets were studied using PPI networking and docking analysis. IL2RA, IL18 and VEGFA are found to be the potential RA target and the compounds detected from BJ extract have shown great binding efficiency towards the target from molecular docking study. The resulting complexes were then subject to 100 ns molecular dynamics simulation studies with the GROMACS package to analyze the stability of docked protein-ligand complexes and to assess the fluctuation and conformational changes during protein-ligand interactions. To confirm the anti-arthritic activity of BJ, the extract was tested in CFA-induced arthritic Wistar rats. The test groups administered with BJ extract showed retrieval of altered hematological parameters and substantial recovery from inflammation and degeneration of rat hind paw.Communicated by Ramaswamy H. Sarma.

Identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis

Lung cancer is one of the most widespread neoplasms worldwide. To identify the key biomarkers in its carcinogenesis and development, the mRNA microarray datasets GSE102287, GSE89047, GSE67061 and GSE74706 were obtained from the Gene Expression Omnibus database. GEO2R was used to identify the differentially expressed genes (DEGs) in lung cancer. The Database for Annotation, Visualization and Integrated Discovery was used to analyze the functions and pathways of the DEGs, while the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape were used to obtain the protein-protein interaction (PPI) network. Kaplan Meier curves were used to analyze the effect of the hub genes on overall survival (OS). Module analysis was completed using Molecular Complex Detection in Cytoscape, and one co-expression network of these significant genes was obtained with cBioPortal. A total of 552 DEGs were identified among the four microarray datasets, which were mainly enriched in 'cell proliferation', 'cell growth', 'cell division', 'angiogenesis' and 'mitotic nuclear division'. A PPI network, composed of 44 nodes and 886 edges, was constructed, and its significant module had 16 hub genes in the whole network: Opa interacting protein 5, exonuclease 1, PCNA clamp-associated factor, checkpoint kinase 1, hyaluronan-mediated motility receptor, maternal embryonic leucine zipper kinase, non-SMC condensin I complex subunit G, centromere protein F, BUB1 mitotic checkpoint serine/threonine kinase, cyclin A2, thyroid hormone receptor interactor 13, TPX2 microtubule nucleation factor, nucleolar and spindle associated protein 1, kinesin family member 20A, aurora kinase A and centrosomal protein 55. Survival analysis of these hub genes revealed that they were markedly associated with poor OS in patients with lung cancer. In summary, the hub genes and DEGs delineated in the research may aid the identification of potential targets for diagnostic and therapeutic strategies in lung cancer.

Identification of differentially expressed genes and enriched pathways in lung cancer using bioinformatics analysis

Lung cancer is the leading cause of cancer‑associated mortality worldwide. The aim of the present study was to identify the differentially expressed genes (DEGs) and enriched pathways in lung cancer by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid microarray data of 31 pairs of lung cancer tissues and matched normal samples (GSE19804) were obtained from the Gene Expression Omnibus database. Significance analysis of the gene expression profile was used to identify DEGs between cancer tissues and normal tissues, and a total of 1,970 DEGs, which were significantly enriched in biological processes, were screened. Through the Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, 77 KEGG pathways associated with lung cancer were identified, among which the Toll‑like receptor pathway was observed to be important. Protein‑protein interaction network analysis extracted 1,770 nodes and 10,667 edges, and identified 10 genes with key roles in lung cancer with highest degrees, hub centrality and betweenness. Additionally, the module analysis of protein‑protein interactions revealed that 'chemokine signaling pathway', 'cell cycle' and 'pathways in cancer' had a close association with lung cancer. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the development and progression of lung cancer, and certain genes (including advanced glycosylation end‑product specific receptor and epidermal growth factor receptor) may be used as candidate target molecules to diagnose, monitor and treat lung cancer.

FOSB⁻PCDHB13 Axis Disrupts the Microtubule Network in Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is among the leading causes of human mortality. One reason for high rates of NSCLC mortality is that drug resistance is a major problem for both conventional chemotherapies and less-toxic targeted therapies. Thus, novel mechanistic insights into disease pathogenesis may benefit the development of urgently needed therapies. Here we show that FBJ murine osteosarcoma viral oncogene homolog B (FOSB) was induced by an antimicrobial peptide, tilapia piscidin-4 (TP4), through the dysregulation of mitochondrial Ca²⁺ homeostasis in NSCLC cells. Transcriptomic, chromatin immunoprecipitation quantitative PCR, and immunocytochemical studies reveal that protocadherin-β13 (PCDHB13) as a target of FOSB that was functionally associated with microtubule. Overexpression of either PCDHB13 or FOSB attenuated NSCLC growth and survival in vitro and in vivo. Importantly, downregulation of both FOSB and PCDHB13 was observed in NSCLC patients and was negatively correlated with pathological grade. These findings introduce the FOSB⁻PCDHB13 axis as a novel tumor suppressive pathway in NSCLC.

Adaptively capturing the heterogeneity of expression for cancer biomarker identification

Background

Identifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells.

Results

In this paper, we propose to adaptively capture the heterogeneity of expression across samples in a gene regulation space instead of in a gene expression space. Specifically, we transform gene expression profiles into gene regulation profiles and mathematically formulate gene regulation probabilities (GRPs)-based statistics for characterizing differential expression of genes between tumor and normal tissues. Finally, an unbiased estimator (aGRP) of GRPs is devised that can interrogate and adaptively capture the heterogeneity of gene expression. We also derived an asymptotical significance analysis procedure for the new statistic. Since no parameter needs to be preset, aGRP is easy and friendly to use for researchers without computer programming background. We evaluated the proposed method on both simulated data and real-world data and compared with previous methods. Experimental results demonstrated the superior performance of the proposed method in exploring the heterogeneity of expression for capturing subtle but consistent alterations of gene expression in cancer.

Conclusions

Expression heterogeneity largely influences the performance of cancer biomarker identification from transcriptomics data. Models are needed that efficiently deal with the expression heterogeneity. The proposed method can be a standalone tool due to its capacity of adaptively capturing the sample heterogeneity and the simplicity in use.

Software availability

The source code of aGRP can be downloaded from https://github.com/hqwang126/aGRP.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2437-2) contains supplementary material, which is available to authorized users.

Polygonatum sibiricum polysaccharides play anti-cancer effect through TLR4-MAPK/NF-κB signaling pathways

OBJECTIVE

To investigate the anti-cancer effect of Polygonatum sibiricum polysaccharides (PSP) and the underlying mechanism.

METHODS

Tumor-bearing mice were randomly divided into normal saline (NS) group, adriamycin (ADM) group, PSP group and lipopolysaccharide (LPS) group. RAW264.7 cells were pre-treated with or without TLR4 inhibitor or MyD88 inhibitor. Quantitative RT-PCR and Western blot were performed to detect the mRNA and protein expressions, respectively. ELISA and Griess reaction was used to measure cytokines and NO levels. Flow cytometry was employed to examine T-lymphocyte subset and CCK-8 assay was used for cell viability.

RESULTS

The in vivo experiment found that PSP inhibited tumor growth and improved the spleen index, thymus index, the cytokines secretion and CD4⁺/CD8⁺ lymphocytes ratio. Compared with the NS group, the mRNA and protein expressions of the critical nodes inTLR4-MAPK/NF-κB signaling pathways (except TRAM) significantly increased in PSP group, as well as the NO and cytokines levels. Nevertheless, PSP had no obvious effects on TRAM. Further analysis showed that PSP effects on the critical nodes in TLR4-MAPK/NF-κB signaling pathways were suppressed by inhibitor in vitro.

CONCLUSION

The immunoenhancement effect of PSP against lung cancer is mediated by TLR4-MAPK/NF-κB signaling pathways.

Comparative Network Analysis of Patients with Non-Small Cell Lung Cancer and Smokers for Representing Potential Therapeutic Targets

Cigarette smoking is the leading cause of lung cancer worldwide. In this study, we evaluated the serum autoantibody (AAb) repertoires of non-small cell lung cancer (NSCLC) patients and smokers (SM), leading to the identification of overactivated pathways and hubs involved in the pathogenesis of NSCLC. Surface- and solution-phase biopanning were performed on immunoglobulin G purified from the sera of NSCLC and SM groups. In total, 20 NSCLC- and 12 SM-specific peptides were detected, which were used to generate NSCLC and SM protein datasets. NSCLC- and SM-related proteins were visualized using STRING and Gephi, and their modules were analyzed using Enrichr. By integrating the overrepresented pathways such as pathways in cancer, epithelial growth factor receptor, c-Met, interleukin-4 (IL-4) and IL-6 signaling pathways, along with a set of proteins (e.g. phospholipase D (PLD), IL-4 receptor, IL-17 receptor, laminins, collagens, and mucins) into the PLD pathway and inflammatory cytokines network as the most critical events in both groups, two super networks were made to elucidate new aspects of NSCLC pathogenesis and to determine the influence of cigarette smoking on tumour formation. Taken together, assessment of the AAb repertoires using a systems biology approach can delineate the hidden events involved in various disorders.

A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification

BACKGROUND

Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal.

RESULTS

This paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united gene regulation profile across studies by mathematically defining two gene regulation events between two conditions and estimating their occurring probabilities in a sample. Finally, a novel differential expression statistic is established based on the gene regulation profiles, realizing accurate and flexible identification of DEGs in gene regulation space. We evaluated the proposed method on simulation data and real-world cancer datasets and showed the effectiveness and efficiency of jGRP in identifying DEGs identification in the context of meta-analysis.

CONCLUSIONS

Data heterogeneity largely influences the performance of meta-analysis of DEGs identification. Existing different meta-analysis methods were revealed to exhibit very different degrees of sensitivity to study heterogeneity. The proposed method, jGRP, can be a standalone tool due to its united framework and controllable way to deal with study heterogeneity.

The clinical value and biological function of PTTG1 in colorectal cancer

Pituitary tumor transforming gene-1 (PTTG1) has been suggested to serve as an oncogene in several types of human tumors, but little is known about the biological function of PTTG1 in colorectal cancer. PTTG1 mRNA and protein expressions in colorectal cancer tissues and cell lines were measured by qRT-PCR, western blot or immunohistochemistry. The association between PTTG1 protein expression and clinicopathological features was analyzed. The function of PTTG1 on colorectal cancer cell proliferation and metastasis were explored through MTT, colony formation, migration and invasion assays. In our results, PTTG1 mRNA and protein expressions were increased in colorectal cancer tissues and cell lines compared with normal colonic tissues and colon epithelial cell line. PTTG1 overexpression positively associated with clinical stage, T classification, N classification, M classification and differentiation. The univariate and multivariate analyses suggested PTTG1 overexpression was an independent poor prognostic factor for colorectal cancer patients. The in vitro experiments showed knocking down PTTG1 inhibited colorectal cancer growth and metastasis. In conclusion, PTTG1 is an independent prognostic factor and acts as an oncogene in colorectal cancer.