Identification of NMU as a potential gene conferring alectinib resistance in non-small cell lung cancer based on bioinformatics analyses

Neuromedin U receptor 1 deletion leads to impaired immunotherapy response and high malignancy in colorectal cancer

Colorectal cancer (CRC) exhibits significant heterogeneity, impacting immunotherapy efficacy, particularly in immune desert subtypes. Neuromedin U receptor 1 (NMUR1) has been reported to perform a vital function in immunity and inflammation. Through comprehensive multi-omics analyses, we have systematically characterized NMUR1 across various tumors, assessing expression patterns, genetic alterations, prognostic significance, immune infiltration, and pathway associations at both the bulk sequencing and single-cell scales. Our findings demonstrate a positive correlation between NMUR1 and CD8+ T cell infiltration, with elevated NMUR1 levels in CD8+ T cells linked to improved immunotherapy outcomes in patients with CRC. Further, we have validated the NMUR1 expression signature in CRC cell lines and patient-derived tissues, revealing its interaction with key immune checkpoints, including lymphocyte activation gene 3 and cytotoxic T-lymphocyte-associated protein 4. Additionally, NMUR1 suppression enhances CRC cell proliferation and invasiveness. Our integrated analyses and experiments open new avenues for personalized immunotherapy strategies in CRC treatment.

Identification of Crucial Genes and Signaling Pathways in Alectinib-Resistant Lung Adenocarcinoma Using Bioinformatic Analysis

Alectinib, a second-generation anaplastic lymphoma kinase (ALK) inhibitor, has been shown to be effective for patients with ALK-positive non-small cell lung cancer (NSCLC). However, alectinib resistance is a serious problem worldwide. To the best of our knowledge, little information is available on its molecular mechanisms using the Gene Expression Omnibus (GEO) database. In this study, the differentially expressed genes (DEGs) were selected from the gene expression profile GSE73167 between parental and alectinib-resistant human lung adenocarcinoma (LUAD) cell samples. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) annotation enrichment analyses were conducted using Database for Annotation, Visualization and Integrated Discovery (DAVID). The construction of protein-protein interaction (PPI) network was performed to visualize DEGs. The hub genes were extracted based on the analysis of the PPI network using plug-in cytoHubba of Cytoscape software. The functional roles of the key genes were investigated using Gene Expression Profiling Interactive Analysis (GEPIA), University of Alabama at Birmingham Cancer (UALCAN), Gene Set Enrichment Analysis (GSEA), and Tumor Immune Estimation Resource (TIMER) analysis. The networks of kinase, miRNA, and transcription-factor targets of SFTPD were explored using LinkedOmics. The drug sensitivity analysis of SFTPD was analyzed using the RNAactDrug database. Results showed a total of 144 DEGs were identified. Five hub genes were extracted, including mucin 5B (MUC5B), surfactant protein D (SFTPD), deleted in malignant brain tumors 1 (DMBT1), surfactant protein A2 (SFTPA2), and trefoil factor 3 (TFF3). The survival analysis using GEPIA displayed that low expression of SFTPD had a significantly negative effect on the prognosis of patients with LUAD. GSEA revealed that low expression of SFTPD was positively correlated with the pathways associated with drug resistance, such as DNA replication, cell cycle, drug metabolism, and DNA damage repair, including mismatch repair (MMR), base excision repair (BER), homologous recombination (HR), and nucleotide excision repair (NER). The SFTPD expression was negatively correlated with the drug sensitivity of alectinib according to RNAactDrug database. The expression of SFTPD was further validated in parental H3122 cells and alectinib-resistant H3122 cells by quantitative reverse transcription PCR (RT-qPCR). In conclusion, our study found that the five hub genes, especially low expression of SFTPD, are closely related to alectinib resistance in patients with LUAD.

Three Members of Transmembrane-4-Superfamily, TM4SF1, TM4SF4, and TM4SF5, as Emerging Anticancer Molecular Targets against Cancer Phenotypes and Chemoresistance

There are six members of the transmembrane 4 superfamily (TM4SF) that have similar topology and sequence homology. Physiologically, they regulate tissue differentiation, signal transduction pathways, cellular activation, proliferation, motility, adhesion, and angiogenesis. Accumulating evidence has demonstrated, among six TM4SF members, the regulatory roles of transmembrane 4 L6 domain family members, particularly TM4SF1, TM4SF4, and TM4SF5, in cancer angiogenesis, progression, and chemoresistance. Hence, targeting derailed TM4SF for cancer therapy has become an emerging research area. As compared to others, this review aimed to present a focused insight and update on the biological roles of TM4SF1, TM4SF4, and TM4SF5 in the progression, metastasis, and chemoresistance of various cancers. Additionally, the mechanistic pathways, diagnostic and prognostic values, and the potential and efficacy of current anti-TM4SF antibody treatment were also deciphered. It also recommended the exploration of other interactive molecules to be implicated in cancer progression and chemoresistance, as well as potential therapeutic agents targeting TM4SF as future perspectives. Generally, these three TM4SF members interact with different integrins and receptors to significantly induce intracellular signaling and regulate the proliferation, migration, and invasion of cancer cells. Intriguingly, gene silencing or anti-TM4SF antibody could reverse their regulatory roles deciphered in different preclinical models. They also have prognostic and diagnostic value as their high expression was detected in clinical tissues and cells of various cancers. Hence, TM4SF1, TM4SF4, and TM4SF5 are promising therapeutic targets for different cancer types preclinically and deserve further investigation.

Identification of PTPN22 as a potential genetic biomarker for abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is a severe life-threatening disease that is generally asymptomatic and is diagnosed at a very late stage. The genetic component underpinning AAA is considerable, with an estimated heritability of up to 70%. Therefore, identifying genetic biomarkers for AAA is valuable for predicting high-risk populations. We used integrative bioinformatics and cellular AAA model-based validation to reveal that the gene encoding protein tyrosine phosphatase non-receptor type 22 (PTPN22) may be a potentially useful diagnostic biomarker for AAA. Integrative bioinformatics analyses of clinical specimens showed that PTPN22 expression was consistently upregulated in aortic tissues and peripheral blood mononuclear cells (PBMCs) derived from patients with AAA. Moreover, transcriptomics data revealed that PTPN22 is a potential biomarker for AAA with limited diagnostic value in patients with thoracic aortic aneurysm/dissection. Single-cell RNA sequencing-based findings further highlight PTPN22 expression in aortic immune cells and vascular smooth muscle cells (VSMCs) is consistently upregulated in patients with AAA. A cellular AAA model was eventually employed to verify the increase in PTPN22 expression. Collectively, the results indicate that PTPN22 could be a potentially useful diagnostic biomarker for AAA.

Identification and Validation of Afatinib Potential Drug Resistance Gene BIRC5 in Non-Small Cell Lung Cancer

Introduction

Resistance to second-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI), afatinib, is the most significant challenge in the clinical management of non-small cell lung cancer (NSCLC), and the underlying mechanisms remain unclear.

Methods

Genomic signatures that may confer afatinib resistance in NSCLC were identified via data mining of public databases and integrative bioinformatic analyses. Furthermore, acquired afatinib-resistant lung adenocarcinoma cell lines (HCC827 AR) were established by long-term exposure under afatinib in vitro for stepwise escalation. The expression of baculovirus IAP repeat protein 5 (BIRC5) was detected by western blot, and cellular viability of HCC827 AR was determined by CCK8.

Results

Through integrative bioinformatic analyses of public datasets, overexpression of baculovirus IAP repeat protein 5 (BIRC5) was identified in both afatinib-resistant NSCLC cells and tissues, and BIRC5 overexpression was positively correlated with lymph node metastasis as well as pathological stage in NSCLC. Furthermore, NSCLC patients with BIRC5 overexpression showed poor survival outcomes. Immune infiltration analysis suggested that BIRC5 expression was significantly inversely correlated with tumor-infiltrating cell numbers and immune biomarker expression in NSCLC. The functions of genes co-expressed with BIRC5 were mainly enriched in cell cycle mitotic phase transition, double-strand break repair, and negative regulation of the cell cycle process signaling pathway. In addition, overexpression of BIRC5 protein was detected in afatinib-resistant cells by western blot, while BIRC5-expressing cells treated with BIRC5 inhibitor, YM155, were sensitive to afatinib.

Conclusions

In this study, we showed that overexpression of BIRC5 resulted in resistance to afatinib in NSCLC and BIRC5-specific inhibitors may overcome the resistant phenotype, indicating that dysregulation of the apoptotic cell death pathway may be the key mechanism underlying TKI resistance in the development of NSCLC.

The Resistance Mechanisms and Treatment Strategies for ALK-Rearranged Non-Small Cell Lung Cancer

Anaplastic lymphoma kinase (ALK) is a validated molecular target for non-small-cell lung cancer (NSCLC). The use of tyrosine kinase inhibitors (TKIs) has led to significantly improved survival benefits. However, the clinical benefits of targeting ALK using TKIs are limited due to the emergence of drug resistance. The landscape of resistance mechanisms and treatment decisions has become increasingly complex. Therefore, continued research into new drugs and combinatorial therapies is required to improve outcomes in NSCLC. In this review, we explore the resistance mechanisms of ALK TKIs in advanced NSCLC in order to provide a theoretical basis and research ideas for solving the problem of ALK drug resistance.

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein-protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.

Identification of molecular biomarkers and pathways of NSCLC: insights from a systems biomedicine perspective

BACKGROUND

Worldwide, more than 80% of identified lung cancer cases are associated to the non-small cell lung cancer (NSCLC). We used microarray gene expression dataset GSE10245 to identify key biomarkers and associated pathways in NSCLC.

RESULTS

To collect Differentially Expressed Genes (DEGs) from the dataset GSE10245, we applied the R statistical language. Functional analysis was completed using the Database for Annotation Visualization and Integrated Discovery (DAVID) online repository. The DifferentialNet database was used to construct Protein-protein interaction (PPI) network and visualized it with the Cytoscape software. Using the Molecular Complex Detection (MCODE) method, we identify clusters from the constructed PPI network. Finally, survival analysis was performed to acquire the overall survival (OS) values of the key genes. One thousand eighty two DEGs were unveiled after applying statistical criterion. Functional analysis showed that overexpressed DEGs were greatly involved with epidermis development and keratinocyte differentiation; the under-expressed DEGs were principally associated with the positive regulation of nitric oxide biosynthetic process and signal transduction. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway investigation explored that the overexpressed DEGs were highly involved with the cell cycle; the under-expressed DEGs were involved with cell adhesion molecules. The PPI network was constructed with 474 nodes and 2233 connections.

CONCLUSIONS

Using the connectivity method, 12 genes were considered as hub genes. Survival analysis showed worse OS value for SFN, DSP, and PHGDH. Outcomes indicate that Stratifin may play a crucial role in the development of NSCLC.

REG4 is a Potential Biomarker for Radiochemotherapy Sensitivity in Colorectal Cancer

Purpose

Colorectal cancer (CRC) is one of the most common types of malignancies, and radiochemotherapy (RCT) followed by surgery is the recommended approach for CRC treatment. However, some cases do not respond to first-line conventional chemotherapy or even progress further after treatment. Moreover, there is a risk of severe side effects, such as radiodermatitis. Therefore, identifying predictors for RCT sensitivity is an essential step toward predicting and eventually overcoming resistance.

Materials and Methods

We used integrative bioinformatics analysis and experimental validation to show that regenerating family member 4 (REG4) may be a potential biomarker for RCT sensitivity in CRC.

Results

REG4, whose expression is upregulated in some CRC tissues and downregulated in RCT-sensitive CRC cells, was identified as a potential genetic marker for RCT sensitivity in CRC. Immunohistochemistry-based tissue microarray of human CRC was used to experimentally validate REG4 data obtained from the bioinformatics analysis.

Conclusion

Collectively, these results indicate that REG4 may be a potential biomarker for RCT sensitivity in CRC.

Analysis of genes associated with prognosis of lung adenocarcinoma based on GEO and TCGA databases

BACKGROUNDS

Lung adenocarcinoma (LUAD) is one of the most common malignancies, and is a serious threat to human health. The aim of the present study was to assess potential biomarkers for the prognosis of LUAD through the analysis of gene expression microarrays.

METHODS

The gene expression data for GSE118370 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between normal lung and LUAD samples were screened using the R language. The DAVID database was used to analyze the functions and pathways of DEGs. The STRING database was used to the map protein-protein interaction (PPI) networks, and these were visualized with the Cytoscape software. Finally, the prognostic analysis of the hub gene in the PPI network was performed using the Kaplan-Meier tool.

RESULTS

A total of 406 downregulated and 203 upregulated DEGs were identified. The GO analysis results revealed that downregulated DEGs were significantly enriched in angiogenesis, calcium ion binding and cell adhesion. The upregulated DEGs were significantly enriched in the extracellular matrix disassembly, collagen catabolic process, chemokine-mediated signaling pathway and endopeptidase inhibitor activity. The KEGG pathway analysis revealed that downregulated DEGs were enriched in neuroactive ligand-receptor interaction, hematopoietic cell lineage and vascular smooth muscle contraction, while upregulated DEGs were enriched in phototransduction. In addition, the top 10 hub genes and the most closely interacting modules of the top 3 proteins in the PPI network were screened. Finally, the independent prognostic value of each hub gene in LUAD patients was analyzed through the Kaplan-Meier plotter. Seven hub genes (ADCY4, S1PR1, FPR2, PPBP, NMU, PF4, and GCG) were closely correlated to overall survival time.

CONCLUSION

The discovery of these candidate genes and pathways reveals the etiology and molecular mechanisms of LUAD, providing ideas and guidance for the development of new therapeutic approaches to LUAD.

Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets

Background

The molecular mechanisms and genetic markers of thyroid cancer are unclear. In this study, we used bioinformatics to screen for key genes and pathways associated with thyroid cancer development and to reveal its potential molecular mechanisms.

Methods

The GSE3467, GSE3678, GSE33630, and GSE53157 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 164 tissue samples (64 normal thyroid tissue samples and 100 thyroid cancer samples). The four datasets were integrated and analyzed by the RobustRankAggreg (RRA) method to obtain differentially expressed genes (DEGs). Using these DEGs, we performed gene ontology (GO) functional annotation, pathway analysis, protein-protein interaction (PPI) analysis and survival analysis. Then, CMap was used to identify the candidate small molecules that might reverse thyroid cancer gene expression.

Results

By integrating the four datasets, 330 DEGs, including 154 upregulated and 176 downregulated genes, were identified. GO analysis showed that the upregulated genes were mainly involved in extracellular region, extracellular exosome, and heparin binding. The downregulated genes were mainly concentrated in thyroid hormone generation and proteinaceous extracellular matrix. Pathway analysis showed that the upregulated DEGs were mainly attached to ECM-receptor interaction, p53 signaling pathway, and TGF-beta signaling pathway. Downregulation of DEGs was mainly involved in tyrosine metabolism, mineral absorption, and thyroxine biosynthesis. Among the top 30 hub genes obtained in PPI network, the expression levels of FN1, NMU, CHRDL1, GNAI1, ITGA2, GNA14 and AVPR1A were associated with the prognosis of thyroid cancer. Finally, four small molecules that could reverse the gene expression induced by thyroid cancer, namely ikarugamycin, adrenosterone, hexamethonium bromide and clofazimine, were obtained in the CMap database.

Conclusion

The identification of the key genes and pathways enhances the understanding of the molecular mechanisms for thyroid cancer. In addition, these key genes may be potential therapeutic targets and biomarkers for the treatment of thyroid cancer.

Bioinformatics analysis reveals 6 key biomarkers associated with non-small-cell lung cancer

Objective

Non-small-cell lung cancer (NSCLC) accounts for >85% of lung cancers, and its incidence is increasing. We explored expression differences between NSCLC and normal cells and predicted potential target sites for detection and diagnosis of NSCLC.

Methods

Three microarray datasets from the Gene Expression Omnibus database were analyzed using GEO2R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were conducted. Then, the String database, Cytoscape, and MCODE plug-in were used to construct a protein–protein interaction (PPI) network and screen hub genes. Overall and disease-free survival of hub genes were analyzed using Kaplan-Meier curves, and the relationship between expression patterns of target genes and tumor grades were analyzed and validated. Gene set enrichment analysis and receiver operating characteristic curves were used to verify enrichment pathways and diagnostic performance of hub genes.

Results

In total, 293 differentially expressed genes were identified and mainly enriched in cell cycle, ECM–receptor interaction, and malaria. In the PPI network, 36 hub genes were identified, of which 6 were found to play significant roles in carcinogenesis of NSCLC: CDC20, ECT2, KIF20A, MKI67, TPX2, and TYMS.

Conclusion

The identified target genes can be used as biomarkers for the detection and diagnosis of NSCLC.

TIMP1 Is A Potential Key Gene Associated With The Pathogenesis And Prognosis Of Ulcerative Colitis-Associated Colorectal Cancer

Purpose

Colorectal cancer (CRC) is the third most frequently diagnosed cancer worldwide. As a high-risk factor for CRC, ulcerative colitis (UC) has been demonstrated to lead to epithelial dysplasia, DNA damage, and eventually cancer. There are approximately 18% of patients with UC may develop CRC.

Patients and methods

The gene expression profiles were retrieved from the Gene Expression Omnibus. The Database for Annotation, Visualization and Integrated Discovery was employed to conduct gene annotations. Protein-protein interaction network was constructed by the Search Tool for the Retrieval of Interacting Genes, and further analysed by the Molecular Complex Detection. The correlation between TIMP1 and prognosis was evaluated by the Gene Expression Profiling Interactive Analysis. To predict the potential functions of TIMP1, the GeneMANIA, Coremine, and FunRich were employed. After transfection with small interfering RNA targeting TIMP1, cell proliferation, migration, and apoptosis were determined by CCK-8, scratch wound, and Annexin V-FITC/PI assays, respectively.

Results

TIMP1, consistently overexpressed in the initiation and progression of UC-associated CRC (ucaCRC), was identified to be a potential biomarker for the prognosis of patients with CRC. Experimental results showed knockdown of TIMP1 could increase the migration, while did not affect the proliferation and apoptosis of RKO cells. The role of TIMP1 in the malignant transformation of ucaCRC was confirmed by using the protein/gene interactions and biological process annotation and validated by analysing the transcription factors targeting TIMP1.

Conclusion

TIMP1 is consistently upregulated in the pathological process of ucaCRC and can be a potential biomarker for the worse prognosis of CRC.

Two Receptors, Two Isoforms, Two Cancers: Comprehensive Analysis of KIT and TrkA Expression in Neuroblastoma and Acute Myeloid Leukemia

Pediatric cancers represent a wide variety of different tumors, though they have unique features that distinguish them from adult cancers. Receptor tyrosine kinases KIT and TrkA functions in AML and NB, respectively, are well-characterized. Though expression of these receptors is found in both tumors, little is known about KIT function in NB and TrkA in AML. By combining gene enrichment analysis with multidimensional scaling we showed that pediatric AMLs with t(8;21) or inv16 and high KIT expression levels stand out from other AML subtypes as they share prominent transcriptomic features exclusively with KIT-overexpressing NBs. We showed that AML cell lines had a predominant expression of an alternative TrkAIII isoform, which reportedly has oncogenic features, while NB cell lines had dominating TrkAI-II isoforms. NB cells, on the other hand, had an abnormal ratio of KIT isoforms as opposed to AML cells. Both SCF and NGF exerted protective action against doxorubicin and cytarabine for t(8;21) AML and NB cells. We identified several gene sets both unique and common for pediatric AML and NB, and this expression is associated with KIT or TrkA levels. NMU, DUSP4, RET, SUSD5, NOS1, and GABRA5 genes are differentially expressed in NBs with high KIT expression and are associated with poor survival in NB. We identified HOXA10, BAG3, and MARCKS genes that are connected with TrkA expression and are marker genes of poor outcome in AML. We also report that SLC18A2, PLXNC1, and MRPL33 gene expression is associated with TrkA or KIT expression levels in both AML and NB, and these genes have a prognostic value for both cancers. Thus, we have provided a comprehensive characterization of TrkA and KIT expression along with the oncogenic signatures of these genes across two pediatric tumors.

Neuromedin U: A Small Peptide in the Big World of Cancer

Neuromedin U (NMU), a neuropeptide isolated from porcine spinal cord and named because of its activity as a rat uterus smooth muscle contraction inducer, is emerging as a new player in the tumorigenesis and/or metastasis of many types of cancers. Expressed in a variety of tissues, NMU has been shown to possess many important activities in the central nervous system as well as on the periphery. Along with the main structural and functional features of NMU and its currently known receptors, we summarized a growing number of recently published data from different tissues and cells that associate NMU activity with cancer development and progression. We ask if, based on current reports, NMU can be included as a marker of these processes and/or considered as a therapeutic target.

Bioinformatics analysis of molecular genetic targets and key pathways for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the second leading cause of death among cancers worldwide. In this study, we aimed to identify the molecular target genes and detect the key mechanisms of HCC. Three gene expression profiles (GSE84006, GSE14323, GSE14811) and two miRNA expression profiles (GSE40744, GSE36915) were analyzed to determine the molecular target genes, microRNAs (miRNAs) and the potential molecular mechanisms in HCC.

Methods

All profiles were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs) was analyzed by the GEO2R method. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) enrichment analysis performed database for Integrated Discovery, Visualization and Annotation. The miRNA-gene network and protein–protein interaction (PPI) network were correlated by the Cytoscape software. The key target genes were identified by the CytoHubba plugin, Molecular Complex Detection (MCODE) plugin and miRNA-gene network. The identified hub genes were testified for survival curve using the Kaplan–Meier plotter database.

Results

Expression profiles had 592 overlapped DEGs. The majority of the DEGs were enriched in membrane-bounded organelles and intracellular membrane-bounded organelles. These DEGs were significantly enriched in metabolic, protein processing in the endoplasmic reticulum and thyroid cancer pathways. PPI network analysis showed these genes were mostly involved in the pathogenic Escherichia coli infection and the regulation of actin cytoskeleton pathways. Combining these results, we identified 10 key genes involving in the progression of HCC. Finally, PLK1, PRCC, PRPF4 and PSMA7 exhibited higher expression levels in HCC patients with poor prognosis than those for lower expression via Kaplan–Meier plotter database.

Conclusion

PLK1, PRCC, PRPF4 and PSMA7 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, protein processing in the endoplasmic reticulum and the thyroid cancer pathway may play vital roles in the progression of HCC.