Identification of key biomarkers and potential molecular mechanisms in 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.

NCAPH drives breast cancer progression and identifies a gene signature that predicts luminal a tumour recurrence

BACKGROUND

Luminal A tumours generally have a favourable prognosis but possess the highest 10-year recurrence risk among breast cancers. Additionally, a quarter of the recurrence cases occur within 5 years post-diagnosis. Identifying such patients is crucial as long-term relapsers could benefit from extended hormone therapy, while early relapsers might require more aggressive treatment.

METHODS

We conducted a study to explore non-structural chromosome maintenance condensin I complex subunit H's (NCAPH) role in luminal A breast cancer pathogenesis, both in vitro and in vivo, aiming to identify an intratumoural gene expression signature, with a focus on elevated NCAPH levels, as a potential marker for unfavourable progression. Our analysis included transgenic mouse models overexpressing NCAPH and a genetically diverse mouse cohort generated by backcrossing. A least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was performed on transcripts associated with elevated intratumoural NCAPH levels.

RESULTS

We found that NCAPH contributes to adverse luminal A breast cancer progression. The intratumoural gene expression signature associated with elevated NCAPH levels emerged as a potential risk identifier. Transgenic mice overexpressing NCAPH developed breast tumours with extended latency, and in Mouse Mammary Tumor Virus (MMTV)-NCAPHErbB2 double-transgenic mice, luminal tumours showed increased aggressiveness. High intratumoural Ncaph levels correlated with worse breast cancer outcome and subpar chemotherapy response. A 10-gene risk score, termed Gene Signature for Luminal A 10 (GSLA10), was derived from the LASSO analysis, correlating with adverse luminal A breast cancer progression.

CONCLUSIONS

The GSLA10 signature outperformed the Oncotype DX signature in discerning tumours with unfavourable outcomes, previously categorised as luminal A by Prediction Analysis of Microarray 50 (PAM50) across three independent human cohorts. This new signature holds promise for identifying luminal A tumour patients with adverse prognosis, aiding in the development of personalised treatment strategies to significantly improve patient outcomes.

A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer

Non-small cell lung cancer (NSCLC), the primary histological form of lung cancer, accounts for about 25%-the highest-of all cancer deaths. As NSCLC is often undetected until symptoms appear in the late stages, it is imperative to discover more effective tumor-associated biomarkers for early diagnosis. Topological data analysis is one of the most powerful methodologies applicable to biological networks. However, current studies fail to consider the biological significance of their quantitative methods and utilize popular scoring metrics without verification, leading to low performance. To extract meaningful insights from genomic data, it is essential to understand the relationship between geometric correlations and biological function mechanisms. Through bioinformatics and network analyses, we propose a novel composite selection index, the C-Index, that best captures significant pathways and interactions in gene networks to identify biomarkers with the highest efficiency and accuracy. Furthermore, we establish a 4-gene biomarker signature that serves as a promising therapeutic target for NSCLC and personalized medicine. The C-Index and biomarkers discovered were validated with robust machine learning models. The methodology proposed for finding top metrics can be applied to effectively select biomarkers and early diagnose many diseases, revolutionizing the approach to topological network research for all cancers.

Exonuclease 1 genetic variant is associated with clinical outcomes of pemetrexed chemotherapy in lung adenocarcinoma

Pemetrexed is an anti-folate agent which is one of the most frequently used chemotherapy agents for non-squamous non-small cell lung cancer (NSCLC) patients. However, clinical response to pemetrexed chemotherapy and survival outcome of patients varies significantly. We evaluated whether the genetic variants in miRNA target sites may affect the treatment outcome of pemetrexed chemotherapy in lung adenocarcinoma patients. One hundred SNPs in miRNA binding regions in cancer-related genes were obtained from the crosslinking, ligation, and sequencing of hybrids (CLASH) and CancerGenes database, and the associations with the response to pemetrexed chemotherapy and survival outcomes were investigated in 314 lung adenocarcinoma patients. Two polymorphisms, EXO1 rs1047840G>A and CAMKK2 rs1653586G>T, were significantly associated with worse chemotherapy response (adjusted odds ratio [aOR] = 0.41, 95% CI = 0.24-0.68, P = 0.001, under dominant model; and aOR = 0.33, 95% CI = 0.16-0.67, P = 0.002, under dominant model, respectively) and worse OS (adjusted hazard ratio [aHR] = 1.34, 95% CI = 1.01-1.77, P = 0.04, under dominant model; and aHR = 1.50, 95% CI = 1.06-2.13, P = 0.02, under dominant model, respectively) in multivariate analyses. Significantly increased luciferase activity was noted in EXO1 rs1047840 A allele compared to G allele. In conclusion, two SNPs in miRNA binding sites, especially EXO1 rs1047840G>A, were associated with the chemotherapy response and survival outcome in lung adenocarcinoma patients treated with pemetrexed.

The Roles of EXO1 and RPA1 Polymorphisms in Prognosis of Lung Cancer Patients Treated with Platinum-Based Chemotherapy

Background. Lung cancer is one of the major causes of cancer-related mortality worldwide. DNA repair and damage response contribute to genomic instability that accompanies tumor progression. In this study, we focus on evaluating association between DNA repair polymorphisms of EXO1, RPA1, and prognosis in lung cancer patients whom received platinum-based chemotherapy. Methods. 593 lung cancer patients were recruited in this study. We performed genotyping of 19 single nucleotide polymorphisms (SNPs) by Sequenom MassARRAY. Cox regression analysis was used to assess overall survival (OS) and progression-free survival (PFS) among SNP genotypes. Results. Significant differences in PFS and OS were observed in RPA1 rs5030740, EXO1 rs1776148, and rs1047840. Results showed that patients with CC genotype in rs5030740 (recessive model: $P=0.034$ ) had a better PFS. Patients with AA or/and AG genotypes in rs1776148 (additive model: $P=0.004$ ; dominant model: $P=0.048$ ) and AA genotype in rs1047840 (recessive model: $P=0.023$ ) had longer OS. We also demonstrated differences in subgroup analysis between rs5030740, rs1776148, rs1047840, and prognosis. Conclusions. Our results indicated that EXO1 rs1776148, rs1047840, and RPA1 rs5030740 were significantly associated with prognosis of lung cancer. Rs1776148, rs1047840, and rs5030740 may act as prognosis markers in lung cancer patients with platinum-based chemotherapy.

The role of RHAMM in cancer: Exposing novel therapeutic vulnerabilities

Receptor for hyaluronic acid-mediated motility (RHAMM) is a cell surface receptor for hyaluronic acid that is critical for cell migration and a cell cycle protein involved in microtubule assembly and stability. These functions of RHAMM are required for cellular stress responses and cell cycle progression but are also exploited by tumor cells for malignant progression and metastasis. RHAMM is often overexpressed in tumors and is an independent adverse prognostic factor for a number of cancers such as breast and prostate. Interestingly, pharmacological or genetic inhibition of RHAMM in vitro and in vivo ablates tumor invasiveness and metastatic spread, implicating RHAMM as a potential therapeutic target to restrict tumor growth and improve patient survival. However, RHAMM’s pro-tumor activity is dependent on its subcellular distribution, which complicates the design of RHAMM-directed therapies. An alternative approach is to identify downstream signaling pathways that mediate RHAMM-promoted tumor aggressiveness. Herein, we discuss the pro-tumoral roles of RHAMM and elucidate the corresponding regulators and signaling pathways mediating RHAMM downstream events, with a specific focus on strategies to target the RHAMM signaling network in cancer cells.

Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer

Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein–protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20, AURKA, CDK1, EZH2, and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary, high mRNA expressions of CBL, FYN, LRKK2, and SOCS2 were associated with a significantly better prognosis. Furthermore, our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat, TGX-221, PHA-793887, AG-879, and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.

TMEM139 prevents NSCLC metastasis by inhibiting lysosomal degradation of E-cadherin

Non‐small‐cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases and has the highest mortality rate among all solid tumors. It is characterized by early metastasis, and investigations of the molecular mechanisms underlying the progression and metastasis of NSCLC are urgently needed for the development of therapeutic targets. Here, we report that the transmembrane protein TMEM139 is significantly downregulated in NSCLC and that reduced expression of TMEM139 is correlated with a poor prognosis in NSCLC patients. Mechanistically, we found that TMEM139 directly interacts with E‐cadherin at the plasma membrane and at focal adhesion sites. Moreover, TMEM139 can prevent the lysosomal degradation of E‐cadherin, which inhibits epithelial‐mesenchymal transition, migration and invasion of NSCLC cells both in vitro and in vivo. Our study not only expands our understanding of NSCLC metastasis but also provides a foundation to develop novel therapeutic strategies.

Upregulation of CENPF is linked to aggressive features of osteosarcoma

Centromere protein F (CENPF) plays a key role in the regulation of the cell cycle. The present study revealed that CENPF was overexpressed in a variety of tumors and associated with the poor prognosis of osteosarcoma. The mRNA expression levels of CENPF were analyzed using the Gene Expression Profiling Interactive Analysis database and the protein levels of CENPF were detected in the specimens from patients with osteosarcoma using immunohistochemistry. Cell proliferation, cell cycle and flow cytometry assays were performed after the transfection of control or CENPF plasmids into osteosarcoma cells. A xenografts assay was used to determine the effects of CENPF on tumor growth in vivo. The results showed that CENPF was upregulated in osteosarcoma tissues and associated with high-grade tumor stage (P=0.023) and intraglandular dissemination (P=0.046). The transfection-induced depletion of CENPF in human osteosarcoma MG-63 and U-2 OS cell lines inhibited cell proliferation, stimulated apoptosis and induced cell cycle arrest. Induced CENPF depletion in MG-63 cells inhibited tumor growth of osteosarcoma cells in mice. These findings suggested that elevated CENPF levels contributed to increased cell proliferation by mediating apoptosis and cell cycle in osteosarcoma. Therefore, CENPF might be a potential biomarker for poor prognosis of osteosarcoma.

Exonuclease 1 (EXO1) is a Potential Prognostic Biomarker and Correlates with Immune Infiltrates in Lung Adenocarcinoma

Background

Exonuclease 1 (EXO1) has been identified to be highly expressed in different human malignancies, but its expression and prognostic role in lung adenocarcinoma (LUAD) remain unknown.

Materials and Methods

Two independent cohorts extracted from public databases and one cohort from our center were analyzed in this study. Expression levels of EXO1 in LUAD tissues and paired para-cancer tissues were detected. The prognostic value of EXO1 in LUAD patients was evaluated in the three cohorts. Enrichment analyses were performed to explore the possible underlying biological pathways. Moreover, we also explored the correlations between EXO1 and tumor-infiltrating immune cells and evaluated the impact of EXO1 knock-down on the migration of lung cancer cells.

Results

In this study, we found that EXO1 was highly expressed in LUAD tissues compared with para-cancerous tissues in public databases (p < 0.01), which was consistent with our data (p < 0.01). Survival analysis indicated that high expression of EXO1 was associated with poor prognosis in LUAD (p < 0.01). Enrichment analyses indicated that biological pathways like cell cycle regulation, DNA damage and repair, immune response, neuroactive ligand-receptor interaction, may be associated with EXO1 aberrant expression. Moreover, high expression of EXO1 was correlated with decreased infiltrating B cells (p < 0.01) and CD4+ T cells (p < 0.01) levels, and low infiltrating levels of B cells (p < 0.01) and dendritic cells (DCs) (p < 0.05) indicated poor overall survival (OS) in LUAD. Additionally, in vitro experiments suggested that knockdown of EXO1 may inhibit the migratory ability of lung cancer cells.

Conclusion

In conclusion, EXO1 is a potential prognostic biomarker in LUAD, and correlates with infiltrating levels of immune cells in the tumor microenvironment. Further prospective validation of EXO1 in lung cancer is warranted.

Maternal embryonic leucine zipper kinase: A novel biomarker and a potential therapeutic target in lung adenocarcinoma

Maternal embryonic leucine zipper kinase (MELK), is an adenosine monophosphate-activated protein kinase-related kinase that serves important roles in tumourigenesis in multiple malignant tumours. However, to the best of our knowledge, the effect of MELK in lung adenocarcinoma (LUAD) has not been elucidated. The present study aimed to explore the clinical significance of MELK in the prognosis of LUAD. Data from Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) were selected to predict the differential mRNA expression levels of MELK mRNA in LUAD and normal tissues. Subsequently, LUAD and adjacent normal tissue samples were collected from 75 patients with the disease, and immunohistochemistry was used to detect the protein expression of MELK. In addition, the Kaplan-Meier Plotter database, GEPIA and TCGA were used to verify the effect of MELK expression on clinical prognosis in patients with LUAD. MELK was significantly upregulated in LUAD tissues compared with that in normal tissues based on Oncomine, GEPIA and TCGA data (P<0.05). In addition, the results from immunohistochemistry demonstrated that the MELK protein level in LUAD tissues was significantly higher compared with that in matched normal tissues (P<0.05). Prognostic analysis performed using the Kaplan-Meier plotter, GEPIA and TCGA suggested that the expression of MELK was negatively associated with the overall survival time of patients with LUAD (P<0.05). In conclusion, MELK was highly expressed in LUAD based on bioinformatics and immunohistochemistry analysis, and increased expression of MELK was associated with a poor patient prognosis. MELK may serve as a potential diagnostic marker and therapeutic target for LUAD.

Identification of candidate genes and prognostic value analysis in patients with PDL1-positive and PDL1-negative lung adenocarcinoma

Background

Increasing bodies of evidence reveal that targeting a programmed cell death protein 1 (PD-1) monoclonal antibody is a promising immunotherapy for lung adenocarcinoma. Although PD receptor ligand 1 (PDL1) expression is widely recognized as the most powerful predictive biomarker for anti-PD-1 therapy, its regulatory mechanisms in lung adenocarcinoma remain unclear. Therefore, we conducted this study to explore differentially expressed genes (DEGs) and elucidate the regulatory mechanism of PDL1 in lung adenocarcinoma.

Methods

The GSE99995 data set was obtained from the Gene Expression Omnibus (GEO) database. Patients with and without PDL1 expression were divided into PDL1-positive and PDL1-negative groups, respectively. DEGs were screened using R. The Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed using the Database for Annotation, Visualization and Integrated Discovery. Protein–protein interaction (PPI) networks of DEGs was visualized using Cytoscape, and the MNC algorithm was applied to screen hub genes. A survival analysis involving Gene Expression Profiling Interactive Analysis was used to verify the GEO results. Mutation characteristics of the hub genes were further analyzed in a combined study of five datasets in The Cancer Genome Atlas (TCGA) database.

Results

In total, 869 DEGs were identified, 387 in the PDL1-positive group and 482 in the PDL1-negative group. GO and KEGG analysis results of the PDL1-positive group mainly exhibited enrichment of biological processes and pathways related to cell adhesion and the peroxisome proliferators-activated receptors (PPAR) signaling pathway, whereas biological process and pathways associated with cell division and repair were mainly enriched in the PDL1-negative group. The top 10 hub genes were screened during the PPI network analysis. Notably, survival analysis revealed BRCA1, mainly involved in cell cycle and DNA damage responses, to be a novel prognostic indicator in lung adenocarcinoma. Moreover, the prognosis of patients with different forms of lung adenocarcinoma was associated with differences in mutations and pathways in potential hub genes.

Conclusions

PDL1-positive lung adenocarcinoma and PDL1-negative lung adenocarcinoma might be different subtypes of lung adenocarcinoma. The hub genes might play an important role in PDL1 regulatory pathways. Further studies on hub genes are warranted to reveal new mechanisms underlying the regulation of PDL1 expression. These results are crucial for understanding and applying precision immunotherapy for lung adenocarcinoma.

Identification of drug targets and potential molecular mechanisms for Wantong Jingu Tablet extract in treatment of rheumatoid arthritis: bioinformatics analysis of fibroblast-like synoviocytes

Background

Rheumatoid arthritis-fibroblast-like synoviocytes (RA-FLSs) play important roles in pathogenesis of rheumatoid arthritis (RA). Wantong Jingu Tablet (WJT), a mixture of traditional Chinese medicine, is a potentially effective therapy for RA, but its underlying mechanism is unclear. In this study, we explore the effects of WJT on human RA-FLSs and the underlying molecular mechanism.

Methods

The major components of WJT were determined using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS). Cell proliferative ability was evaluated by CCK-8, colony formation assay, and EdU incorporation assay. Cell apoptotic capacity was examined by caspase-3 and caspase-9 activity test. Protein levels of Bax and Bcl-2 were investigated by western blotting. High-throughput sequencing and bioinformatics analysis were conducted to screen and identify targeted genes, followed by identification by qRT-PCR and western blotting.

Results

In this study, we have identified 346 compounds in WJT. Our results showed that WJT inhibited the RA-FLSs proliferation, and promoted apoptosis in a dose- and time-dependent manner. More importantly, 184 differentially expressed genes (DEGs) has been screened after WJT treatment based on DEGSeq2 and 278 DEGs was identified by DEGSeq2 combined with WGCNA. Then, 10 hub genes were identified based on two different analyses, while the expression levels of only SMC3, THOC1, BUB1, and STAG2 were decreased after WJT treatment, which was identical to the sequencing profiles.

Conclusions

WJT exerted its anti-proliferation and pro-apoptosis effects possibly through suppressing the expression of SMC3, THOC1, BUB1, and STAG2 in RA-FLSs. Thus, therapeutics targeting these genes may be a promising strategy for rescuing RA.