Hub genes and key pathways of non-small lung cancer identified using bioinformatics

Characterization of platelet-related genes and constructing signature combined with immune-related genes for predicting outcomes and immunotherapy response in lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is a highly malignant subtype of non-small cell lung cancer with poor prognosis. Platelets are known to play a critical role in cancer development and progression, and recent studies suggest that they can also regulate immune response in tumors. However, the relationship between platelet-related genes (PRGs) and LUSC prognosis and tumor microenvironments remains unclear. In this study, we used multiple bioinformatics algorithms to identify 25 dysregulated PRGs that were significantly associated with LUSC prognosis. We found that PRGs were involved in multiple biological processes, particularly in the tumor microenvironment, and that platelet-related scores (PRS) were a risk factor. Additionally, we established a 6-gene prognostic signature combining PRGs and immune-related genes that accurately predicted outcomes and immunotherapy efficacy in LUSC patients. Our study provides a comprehensive analysis of the biological functions and potential therapeutic targets of PRGs in LUSC, which may inform the development of new treatments for this disease.

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID-19 Using Bioinformatics Approaches

Several studies have been done to identify comorbidities of COVID-19. In this work, we developed an analytical bioinformatics framework to reveal COVID-19 comorbidities, their genomic associations, and molecular mechanisms accomplishing transcriptomic analyses of the RNA-seq datasets provided by the Gene Expression Omnibus (GEO) database, where normal and infected tissues were evaluated. Using the framework, we identified 27 COVID-19 correlated diseases out of 7,092 collected diseases. Analyzing clinical and epidemiological research, we noticed that our identified 27 diseases are associated with COVID-19, where hypertension, diabetes, obesity, and lung cancer are observed several times in COVID-19 patients. Therefore, we selected the above four diseases and performed assorted analyses to demonstrate the association between COVID-19 and hypertension, diabetes, obesity, and lung cancer as comorbidities. We investigated genomic associations with the cross-comparative analysis and Jaccard's similarity index, identifying shared differentially expressed genes (DEGs) and linking DEGs of COVID-19 and the comorbidities, in which we identified hypertension as the most associated illness. We also revealed molecular mechanisms by identifying statistically significant ten pathways and ten ontologies. Moreover, to understand cellular physiology, we did protein-protein interaction (PPI) analyses among the comorbidities and COVID-19. We also used the degree centrality method and identified ten biomarker hub proteins (IL1B, CXCL8, FN1, MMP9, CXCL10, IL1A, IRF7, VWF, CXCL9, and ISG15) that associate COVID-19 with the comorbidities. Finally, we validated our findings by searching the published literature. Thus, our analytical approach elicited interconnections between COVID-19 and the aforementioned comorbidities in terms of remarkable DEGs, pathways, ontologies, PPI, and biomarker hub proteins.

Looking for the Genes Related to Lung Cancer From Nasal Epithelial Cells by Network and Pathway Analysis

Previous studies have indicated that the airway epithelia of lung cancer-associated injury can extend to the nose and it was associated with abnormal gene expression. The aim of this study was to find the possible lung cancer-related genes from the nasal epithelium as bio-markers for lung cancer detection. WGCNA was performed to calculate the module-trait correlations of lung cancer based on the public microarray dataset, and their data were processed by statistics of RMA and t-test. Four specific modules associated with clinical features of lung cancer were constructed, including blue, brown, yellow, and light blue. Of which blue or brown module showed strong connection to genetic connectivity. From the brown module, it was found that HCK, NCF1, TLR8, EMR3, CSF2RB, and DYSF are the hub genes, and from the blue module, it was found that SPEF2, ANKFN1, HYDIN, DNAH5, C12orf55, and CCDC113 are the pivotal genes corresponding to the grade. These genes can be taken as the bio-markers to develop a noninvasive method of diagnosing early lung cancer.

An Integrative Transcriptomic Analysis Reveals EGFR Exon-19 E746-A750 Fragment Deletion Regulated miRNA, circRNA, mRNA and lncRNA Networks in Lung Carcinoma

Introduction

Competing endogenous RNA (ceRNA) appears to be an important post-transcriptional manner that regulates gene expression through a miRNA-mediated mechanism. Mutations in exon-19 of EGFR were frequently observed in lung cancer genes, which were associated with EGFR activity and EGFR-targeted therapies.

Methods

We explored the transcriptome regulated by mutation in EGFR exon-19 E746-A750 fragment via using a network modeling strategy. We applied transcriptome sequencing to detect the deletion process of EGFR exon-19 E746-A750 fragment. Bio-informatics analyses were used to predict the gene target pairs and explain their potential roles in tumorigenesis and progression of lung cancer.

Results

We conducted an explorative lncRNA/miRNA/circRNA and mRNA expression study with two groups of lung adenocarcinoma tissues, including EGFR exon-19 E746-A750 deletion group and EGFR exon-19 wild-type group. Meanwhile, we screen out the hub genes related to the EGFR-19-D patient. Significant pathways and biological functions potentially regulated by the deregulated 128 non-coding genes were enriched.

Conclusion

Our work provides an important theoretical, experimental and clinical foundation for further research on more effective targets for the diagnosis, therapy and prognosis of lung cancer.

Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics

Background

Ovarian cancer is one of the most common gynecological tumors, and among gynecological tumors, its incidence and mortality rates are fairly high. However, the pathogenesis of ovarian cancer is not clear. The present study aimed to investigate the differentially expressed genes and signaling pathways associated with ovarian cancer by bioinformatics analysis.

Methods

The data from three mRNA expression profiling microarrays (GSE14407, GSE29450, and GSE54388) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes between ovarian cancer tissues and normal tissues were identified using R software. The overlapping genes from the three GEO datasets were identified, and profound analysis was performed. The overlapping genes were used for pathway and Gene Ontology (GO) functional enrichment analysis using the Metascape online tool. Protein–protein interactions were analyzed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Subnetwork models were selected using the plugin molecular complex detection (MCODE) application in Cytoscape. Kaplan–Meier curves were used to analyze the univariate survival outcomes of the hub genes. The Human Protein Atlas (HPA) database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to validate hub genes.

Results

In total, 708 overlapping genes were identified through analyses of the three microarray datasets (GSE14407, GSE29450, and GSE54388). These genes mainly participated in mitotic sister chromatid segregation, regulation of chromosome segregation and regulation of the cell cycle process. High CCNA2 expression was associated with poor overall survival (OS) and tumor stage. The expression of CDK1, CDC20, CCNB1, BUB1B, CCNA2, KIF11, CDCA8, KIF2C, NDC80 and TOP2A was increased in ovarian cancer tissues compared with normal tissues according to the Oncomine database. Higher expression levels of these seven candidate genes in ovarian cancer tissues compared with normal tissues were observed by GEPIA. The protein expression levels of CCNA2, CCNB1, CDC20, CDCA8, CDK1, KIF11 and TOP2A were high in ovarian cancer tissues, which was further confirmed via the HPA database.

Conclusion

Taken together, our study provided evidence concerning the altered expression of genes in ovarian cancer tissues compared with normal tissues. In vivo and in vitro experiments are required to verify the results of the present study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00837-6.

A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study

Background

Intratumoural heterogeneity has been previously shown to be related to clonal evolution and genetic instability and associated with tumour progression. Phenotypically, it is reflected in the diversity of appearance and morphology within cell populations. Computer-extracted features relating to tumour cellular diversity on routine tissue images might correlate with outcome. This study investigated the prognostic ability of computer-extracted features of tumour cellular diversity (CellDiv) from haematoxylin and eosin (H&E)-stained histology images of non-small cell lung carcinomas (NSCLCs).

Methods

In this multicentre, retrospective study, we included 1057 patients with early-stage NSCLC with corresponding diagnostic histology slides and overall survival information from four different centres. CellDiv features quantifying local cellular morphological diversity from H&E-stained histology images were extracted from the tumour epithelium region. A Cox proportional hazards model based on CellDiv was used to construct risk scores for lung adenocarcinoma (LUAD; 270 patients) and lung squamous cell carcinoma (LUSC; 216 patients) separately using data from two of the cohorts, and was validated in the two remaining independent cohorts (comprising 236 patients with LUAD and 335 patients with LUSC). We used multivariable Cox regression analysis to examine the predictive ability of CellDiv features for 5-year overall survival, controlling for the effects of clinical and pathological parameters. We did a gene set enrichment and Gene Ontology analysis on 405 patients to identify associations with differentially expressed biological pathways implicated in lung cancer pathogenesis.

Findings

For prognosis of patients with early-stage LUSC, the CellDiv LUSC model included 11 discriminative CellDiv features, whereas for patients with early-stage LUAD, the model included 23 features. In the independent validation cohorts, patients predicted to be at a higher risk by the univariable CellDiv model had significantly worse 5-year overall survival (hazard ratio 1·48 [95% CI 1·06–2·08]; p=0·022 for The Cancer Genome Atlas [TCGA] LUSC group, 2·24 [1·04–4·80]; p=0·039 for the University of Bern LUSC group, and 1·62 [1·15–2·30]; p=0·0058 for the TCGA LUAD group). The identified CellDiv features were also found to be strongly associated with apoptotic signalling and cell differentiation pathways.

Interpretation

CellDiv features were strongly prognostic of 5-year overall survival in patients with early-stage NSCLC and also associated with apoptotic signalling and cell differentiation pathways. The CellDiv-based risk stratification model could potentially help to determine which patients with early-stage NSCLC might receive added benefit from adjuvant therapy.

Funding

National Institue of Health and US Department of Defense.

Screening of Hub Gene Targets for Lung Cancer via Microarray Data

BACKGROUND

Lung cancer is one of the malignancies exhibiting the fastest increase in morbidity and mortality, but the cause is not clearly understood. The goal of this investigation was to screen and identify relevant biomarkers of lung cancer.

METHODS

Publicly available lung cancer data sets, including GSE40275 and GSE134381, were obtained from the GEO database. The repeatability test for data was done by principal component analysis (PCA), and a GEO2R was performed to screen differentially expressed genes (DEGs), which were all subjected to enrichment analysis. Protein-protein interactions (PPIs), and the significant module and hub genes were identified via Cytoscape. Expression and correlation analysis of hub genes was done, and an overall survival analysis of lung cancer was performed. A receiver operating characteristic (ROC) curve analysis was performed to test the sensitivity and specificity of the identified hub genes for diagnosing lung cancer.

RESULTS

The repeatability of the two datasets was good and 115 DEGs and 10 hub genes were identified. Functional analysis revealed that these DEGs were associated with cell adhesion, the extracellular matrix, and calcium ion binding. The DEGs were mainly involved with ECM-receptor interaction, ABC transporters, cell-adhesion molecules, and the p53 signaling pathway. Ten genes including COL1A2, POSTN, DSG2, CDKN2A, COL1A1, KRT19, SLC2A1, SERPINB5, DSC3, and SPP1 were identified as hub genes through module analysis in the PPI network. Lung cancer patients with high expression of COL1A2, POSTN, DSG2, CDKN2A, COL1A1, SLC2A1, SERPINB5, and SPP1 had poorer overall survival times than those with low expression (p <0.05). The CTD database showed that 10 hub genes were closely related to lung cancer. Expression of POSTN, DSG2, CDKN2A, COL1A1, SLC2A1, SERPINB5, and SPP1 was also associated with a diagnosis of lung cancer (p<0.05). ROC analysis showed that SPP1 (AUC = 0.940, p = 0.000*, 95%CI = 0.930-0.973, ODT = 7.004), SLC2A1 (AUC = 0.889, p = 0.000*, 95%CI = 0.791-0.865, ODT = 7.123), CDKN2A (AUC = 0.730, p = 0.000*, 95%CI = 0.465-1.000, ODT = 6.071) were suitable biomarkers.

CONCLUSION

Microarray technology represents an effective method for exploring genetic targets and molecular mechanisms of lung cancer. In addition, the identification of hub genes of lung cancer provides novel research insights for the diagnosis and treatment of lung cancer.

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.

Transcriptome analysis reveals gender-specific differences in overall metabolic response of male and female patients in lung adenocarcinoma

Background

Evidence from multiple studies suggests metabolic abnormalities play an important role in lung cancer. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. The present study aimed to explore differences in the global metabolic response between male and female patients in LUAD and to identify the metabolic genes associated with lung cancer susceptibility.

Methods

Transcriptome and clinical LUAD data were acquired from The Cancer Genome Atlas (TCGA) database. Information on metabolic genes and metabolic subsystems were collected from the Recon3D human metabolic model. Two validation datasets (GSE68465 and GSE72094) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis, gene set enrichment analysis and protein-protein interaction networks were used to identified key metabolic pathways and genes. Functional experiments were used to verify the effects of genes on proliferation, migration, and invasion in lung cancer cells in vitro.

Results

Samples of tumors and adjacent non-tumor tissue from both male and female patients exhibited distinct global patterns of gene expression. In addition, we found large differences in methionine and cysteine metabolism, pyruvate metabolism, cholesterol metabolism, nicotinamide adenine dinucleotide (NAD) metabolism, and nuclear transport between male and female LUAD patients. We identified 34 metabolic genes associated with lung cancer susceptibility in males and 15 in females. Most of the metabolic cancer-susceptibility genes had high prediction accuracy for lung cancer (AUC > 0.9). Furthermore, both bioinformatics analysis and experimental results showed that TAOK2 was down-regulated and ASAH1 was up-regulated in male tumor tissue and female tumor tissue in LUAD. Functional experiments showed that inhibiting ASAH1 suppressed the proliferation, migration, and invasion of lung cancer cells.

Conclusions

Metabolic cancer-susceptibility genes may be used alone or in combination as diagnostic markers for LUAD. Further studies are required to elucidate the functions of these genes in LUAD.

Identification of key genes in non-small cell lung cancer by bioinformatics analysis

Background

Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors in the world, and it has become the leading cause of death of malignant tumors. However, its mechanisms are not fully clear. The aim of this study is to investigate the key genes and explore their potential mechanisms involving in NSCLC.

Methods

We downloaded gene expression profiles GSE33532, GSE30219 and GSE19804 from the Gene Expression Omnibus (GEO) database and analyzed them by using GEO2R. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used for the functional and pathway enrichment analysis. We constructed the protein-protein interaction (PPI) network by STRING and visualized it by Cytoscape. Further, we performed module analysis and centrality analysis to find the potential key genes. Finally, we carried on survival analysis of key genes by GEPIA.

Results

In total, we obtained 685 DEGs. Moreover, GO analysis showed that they were mainly enriched in cell adhesion, proteinaceous extracellular region, heparin binding. KEGG pathway analysis revealed that transcriptional misregulation in cancer, ECM-receptor interaction, cell cycle and p53 signaling pathway were involved in. Furthermore, PPI network was constructed including 249 nodes and 1,027 edges. Additionally, a significant module was found, which included eight candidate genes with high centrality features. Further, among the eight candidate genes, the survival of NSCLC patients with the seven high expression genes were significantly worse, including CDK1, CCNB1, CCNA2, BIRC5, CCNB2, KIAA0101 and MELK. In summary, these identified genes should play an important role in NSCLC, which can provide new insight for NSCLC research.

Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma

This study aimed to select the feature genes of hepatocellular carcinoma (HCC) with the Fisher score algorithm and to identify hub genes with the Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to examine the enrichment of terms. Gene set enrichment analysis (GSEA) was used to identify the classes of genes that are overrepresented. Following the construction of a protein-protein interaction network with the feature genes, hub genes were identified with the MCC algorithm. The Kaplan–Meier plotter was utilized to assess the prognosis of patients based on expression of the hub genes. The feature genes were closely associated with cancer and the cell cycle, as revealed by GO, KEGG and GSEA enrichment analyses. Survival analysis showed that the overexpression of the Fisher score–selected hub genes was associated with decreased survival time (P < 0.05). Weighted gene co-expression network analysis (WGCNA), Lasso, ReliefF and random forest were used for comparison with the Fisher score algorithm. The comparison among these approaches showed that the Fisher score algorithm is superior to the Lasso and ReliefF algorithms in terms of hub gene identification and has similar performance to the WGCNA and random forest algorithms. Our results demonstrated that the Fisher score followed by the application of the MCC algorithm can accurately identify hub genes in HCC.

Identifying candidate diagnostic markers for early stage of non-small cell lung cancer

We performed a series of bioinformatics analysis on a set of important gene expression data with 76 samples in early stage of non-small cell lung cancer, including 40 adenocarcinoma samples, 16 squamous cell carcinoma samples and 20 normal samples. In order to identify the specific markers for diagnosis, we compared the two subtypes with the normal samples respectively to determine the gene expression characteristics. Through the multi-dimensional scaling classification, we found that the samples were clustered well according to the disease cases. Based on the classification results and using empirical Bayes moderation and treat method, 486 important genes associated with the disease were identified. We constructed gene functions and gene pathways to verify our result and explain the pathogenicity factor and process. We generated a protein-protein interaction network based on the mutual interaction between the selected genes and found that the top thirteen hub genes were highly associated with lung cancer or some other cancers including five newly found genes through our method. The results of this study indicated that contrast on the gene expression between different subtypes and normal samples provides important information for the detection of non-small cell lung cancer and helps exploration of the disease pathogenesis.

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.

Survival-based bioinformatics analysis to identify hub genes and key pathways in non-small cell lung cancer

BACKGROUND

Lung cancer is one of the leading causes of cancer mortality worldwide. Here, we performed an integrative bioinformatics analysis to screen hub genes and critical pathways in non-small cell lung cancer (NSCLC) based on the overall survival rate of differentially expressed genes (DEGs).

METHODS

Four datasets from the gene expression omnibus (GEO) were used to identify the DEGs. To obtain robust DEGs in NSCLC, only the DEGs that co-existed in the four datasets were selected for subsequent analysis. To identify the genes correlated with overall survival, the overall survival of these genes was then analyzed using the Kaplan-Meier plotter database. The genes significantly correlated with survival were used to perform gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis; next, these genes were used to construct a protein-protein interaction network. MCODE and CytoHubba were used to identify the clusters and hub genes. Finally, the hub genes were validated in the Cancer Genome Atlas (TCGA) and the Human Protein Atlas (HPA).

RESULTS

We found 522 up-regulated DEGs, and 989 down-regulated DEGs between the NSCLC and normal lung tissue, and 895 of them were correlated with a higher overall survival. GO analysis showed that the DEGs that were associated with a higher overall survival were enriched in cell division, cell cycle, DNA replication, angiogenesis, and cell migration. KEGG analysis was consistent with GO analysis and showed that p53 signaling pathway, pyrimidine metabolism, cGMP-PKG signaling pathway and renin secretion pathway were associated with overall survival in NSCLC. In the protein-protein analysis, we identified seven clusters and six hub genes which were BUB1B, CCNB1, CENPE, KIF18A, NDC10, and MAD2L1. Of these genes, CENPE and KIF18A had not been reported until now. Finally, the dysregulated expression of the six hub genes was validated by the data from the TCGA and HPA.

CONCLUSIONS

We identified the hub genes and potential mechanisms of NSCLC based on multiple-microarray analysis and overall survival; then, validated the hub genes in the TCGA and HPA database. These hub genes may serve as potential therapeutic targets.

Transcriptomic analysis to affirm the regulatory role of long non-coding RNA in horn cancer of Indian zebu cattle breed Kankrej (Bos indicus)

Long non-coding RNA (lncRNA) was previously considered as a non-functional transcript, which now established as part of regulatory elements of biological events such as chromosome structure, remodeling, and regulation of gene expression. The study presented here showed the role of lncRNA through differential expression analysis on cancer-related coding genes in horn squamous cell carcinoma of Indian zebu cattle. A total of 10,360 candidate lncRNAs were identified and further analyzed for its coding potential ability using three tools (CPC, CPAT, and PLEK) that provide 8862 common lncRNAs. Pfam analysis of these common lncRNAs gave 8612 potential candidates for lncRNA differential expression analysis. Differential expression analysis showed a total of 59 significantly differentially expressed genes and 19 lncRNAs. Pearson's correlation analysis was used to identify co-expressed mRNA-lncRNAs to established relation of the regulatory role of lncRNAs in horn cancer. We established a positive relation of seven upregulated (XLOC_000016, XLOC_002198, XLOC_002851, XLOC_ 007383, XLOC_010701, XLOC_010272, and XLOC_011517) and one downregulated (XLOC_011302) lncRNAs with eleven genes that are related to keratin family protein, keratin-associated protein family, cornifelin, corneodesmosin, serpin family protein, and metallothionein that have well-established role in squamous cell carcinoma through cellular communication, cell growth, cell invasion, and cell migration. These biological events were found to be related to the MAPK pathway of cell cycle regulation indicating the role of lncRNAs in manipulating cell cycle regulation during horn squamous cell carcinomas that will be useful in identifying molecular portraits related to the development of horn cancer.

Signaling pathway network alterations in human ovarian cancers identified with quantitative mitochondrial proteomics

RELEVANCE

Molecular network changes are the hallmark of the pathogenesis of ovarian cancers (OCs). Network-based biomarkers benefit for the effective treatment of OC.

PURPOSE

This study sought to identify key pathway-network alterations and network-based biomarkers for clarification of molecular mechanisms and treatment of OCs.

METHODS

Ingenuity Pathway Analysis (IPA) platform was used to mine signaling pathway networks with 1198 human tissue mitochondrial differentially expressed proteins (mtDEPs) and compared those pathway network changes between OCs and controls. The mtDEPs in important cancer-related pathway systems were further validated with qRT-PCR and Western blot in OC cell models. Moreover, integrative analysis of mtDEPs and Cancer Genome Atlas (TCGA) data from 419 patients was used to identify hub molecules with molecular complex detection method. Hub molecule-based survival analysis and multiple multivariate regression analysis were used to identify survival-related hub molecules and hub molecule signature model.

RESULTS

Pathway network analysis revealed 25 statistically significant networks, 192 canonical pathways, and 5 significant molecular/cellular function models. A total of 52 canonical pathways were activated or inhibited in cancer pathogenesis, including antigen presentation, mitochondrial dysfunction, GP6 signaling, EIF2 signaling, and glutathione-mediated detoxification. Of them, mtDEPs (TPM1, CALR, GSTP1, LYN, AKAP12, and CPT2) in those canonical pathway and molecular/cellular models were validated in OC cell models at the mRNA and protein levels. Moreover, 102 hub molecules were identified, and they were regulated by post-translational modifications and functioned in multiple biological processes. Of them, 62 hub molecules were individually significantly related to OC survival risk. Furthermore, multivariate regression analysis of 102 hub molecules identified significant seven hub molecule signature models (HIST1H2BK, ALB, RRAS2, HIBCH, EIF3E, RPS20, and RPL23A) to assess OC survival risks.

CONCLUSION

These findings provided the overall signaling pathway network profiling of human OCs; offered scientific data to discover pathway network-based cancer biomarkers for diagnosis, prognosis, and treatment of OCs; and clarify accurate molecular mechanisms and therapeutic targets. These findings benefit for the discovery of effective and reliable biomarkers based on pathway networks for OC predictive and personalized medicine.

The underlying molecular mechanism and potential drugs for treatment in papillary renal cell carcinoma: A study based on TCGA and Cmap datasets

Papillary renal cell carcinoma (PRCC) accounts for 15–20% of all kidney neoplasms and continually attracts attention due to the increase in the incidents in which it occurs. The molecular mechanism of PRCC remains unclear and the efficacy of drugs that treat PRCC lacks sufficient evidence in clinical trials. Therefore, it is necessary to investigate the underlying mechanism in the development of PRCC and identify additional potential anti-PRCC drugs for its treatment. The differently expressed genes (DEGs) of PRCC were identified, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses for functional annotation. Then, potential drugs for PRCC treatment were predicted by Connectivity Map (Cmap) based on DEGs. Furthermore, the latent function of query drugs in PRCC was explored by integrating drug-target, drug-pathway and drug-protein interactions. In total, 627 genes were screened as DEGs, and these DEGs were annotated using KEGG pathway analyses and were clearly associated with the complement and coagulation cascades, amongst others. Then, 60 candidate drugs, as predicted based on DEGs, were obtained from the Cmap database. Vorinostat was considered as the most promising drug for detailed discussion. Following protein-protein interaction (PPI) analysis and molecular docking, vorinostat was observed to interact with C3 and ANXN1 proteins, which are the upregulated hub genes and may serve as oncologic therapeutic targets in PRCC. Among the top 20 metabolic pathways, several significant pathways, such as complement and coagulation cascades and cell adhesion molecules, may greatly contribute to the development and progression of PRCC. Following the performance of the PPI network and molecular docking tests, vorinostat exhibited a considerable and promising application in PRCC treatment by targeting C3 and ANXN1.

Up-regulation of Polo-like Kinase 1 in nasopharyngeal carcinoma tissues: a comprehensive investigation based on RNA-sequencing, gene chips, and in-house tissue arrays

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a highly invasive malignancy which has unique characteristics when found among individuals from certain ethnic or geographic populations. The role and molecular mechanism of Polo-like kinase 1 (PLK1) in NPC remain yet to be clarified. Hence, the aim of this study is to identify the clinical implications of PLK1 in NPC based on gene chip, tissue microarray, and other silico approaches.

METHODS

Relevant data related to PLK1 levels in NPC was screened for by searching in SRA, GEO, ArrayExpress, Oncomine and throughout the existing literature on this topic. The raw data about gene chips were normalized by using an RMA algorithm provided by "Limma" package. Furthermore, the "SVA" package of R software was used to remove the batch effect and data from the same platform were merged into one part. The differential expression levels of PLK1 between NPC and non-NPC tissues were extracted and analyzed with the Student's t-test. Meta-analyses were used to calculate the standard mean difference and sROC. Furthermore, in-house immunohistochemistry was performed with tissue microarrays. Weighted correlation network analysis was used to identify the PLK1-related genes. Several bioinformatic evaluations, including the Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interactions, were also performed to assess the PLK1-related pathways.

RESULTS

The tissue microarray and gene chips indicated that the PLK1 levels clearly had an up-regulating trend as compared to the non-cancerous controls. These trends were observed in both the single study and the comprehensive meta-analysis. The area under the sROC curve in the NPC tissues was 0.87, with pooled sensitivity and specificity at 0.950 and 0.710, respectively, based on 393 NPC tissues and 83 non-cancerous controls. A total of 144 genes were identified as co-expressed genes of PLK1 in NPC and were mainly enriched in the "cell cycle" pathway. Among the genes related to the cell cycle, CDK1, CCNA2 and CCNB2 were all closely related to PLK1 expression level.

CONCLUSIONS

PLK1 may play a potential oncogenic role in the tumorigenesis and development of NPC. Since several PLK1 inhibitors have been developed, it is believed that the PLK1 inhibitors have great therapeutic potential in clinic applications for NPC patients.