Identification Of Potential Prognostic Biomarkers Research Articles

Potential Value of HSP90α in Prognosis of Triple-Negative Breast Cancer.

BACKGROUND Triple-negative breast cancer (TNBC) is a distinct subtype of breast cancer, accounting for 12-18% of all breast cancer cases. It exhibits high heterogeneity and aggressiveness, resulting in a poorer prognosis with a high risk of early recurrence and metastasis. Due to the lack of expression of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2), as well as insensitivity to endocrine therapy, determining a standard treatment for TNBC is challenging. The identification of potential prognostic biomarkers is crucial for developing personalized treatment strategies for patients. MATERIAL AND METHODS Our study investigated the potential value of HSP90a in TNBC prognosis. A retrospective analysis was conducted on 127 TNBC patients and 127 Healthy controls from March 1, 2019 to July 31, 2022. Venous blood was collected and tested for HSP90alpha, CEA, CA199, and CA125, and we recorded the clinical characteristics of the patients, including age, BMI, alcohol consumption status, surgical history, CEA level, CA199 level, CA125 level, HSP90alpha level, tumor size, distant metastases, lymph node metastasis, and TNM stage. Univariate and multivariate methods were used to screen independent risk factors for progression-free survival (PFS) and overall survival (OS). RESULTS HSP90alpha is not only upregulated in TNBC but is also highly correlated with lymph node metastasis and TNM stage. The results of multivariate analysis showed that distant metastasis, TNM stage and HSP90a level were independent factors associated with PFS. BMI, tumor size, TNM stage, surgical history, and HSP90a level were independent factors influencing OS. CONCLUSIONS Our research findings demonstrate a significant association between high HSP90alpha expression and adverse clinical features, suggesting a poorer prognosis for TNBC patients.

Identification of potential prognostic biomarkers among gene models for coiled-coil domain-containing family members in hepatocellular carcinoma elucidates their influence on the hypoxia pathway and immune microenvironment.

The family of coiled-coil domain-containing (CCDC) proteins participates in a wide range of physiological functions and plays a pivotal role in governing the invasion and metastasis of malignant tumor cells. Nonetheless, the precise mechanism governing the interaction among the immune microenvironment, hypoxia pathway, and proliferation in hepatocellular carcinoma (HCC) remains elusive. In this study, our objective was to identify the prognostic significance of CCDC family genes in HCC. We conducted an analysis of RNA-seq data from HCC patients sourced from The Cancer Genome Atlas (TCGA) database. Our analysis involved comparing the expression profiles of 168 CCDC family genes between tumor and normal tissues to identify differentially expressed genes (DEGs). The prognostic value of these genes was verified using overall survival (OS) data from TCGA-LIHC patients, employing Univariate and multivariate Cox proportional hazards regression models and Kaplan-Meier plots. Subsequently, we constructed a prognostic signature known as the CCDC score and validated it using additional datasets (ICGC-LIRI-JP and GSE14520). Additionally, we performed functional enrichment analysis and conducted an assessment of the tumor immune microenvironment (TIME). We identified 34 DEGs of the CCDC family. Among them, six DEGs (CCDC6/22/51/59/132/134) were upregulated and associated with poor prognosis. Higher CCDC score was an independent predictor of poor OS in TCGA-HCC patients (P<0.001, HR =2.37), which was validated in the ICGC-LIRI-JP (P=0.021, HR =2.15) and GSE14520 (P=0.002, HR =2.23) datasets. Functional enrichment analysis showed that hypoxia pathway genes were enriched in the high CCDC score group. Furthermore, immune microenvironment analysis demonstrated that high CCDC score was associated with a suppressed TIME caused by the extrinsic immune escape. The CCDC score, derived from six CCDC genes, exhibits remarkable expression levels in liver cancer and holds promise as an independent prognostic indicator. Our bioinformatics analysis revealed a high CCDC score is strongly associated with activation of the hypoxia pathway and an immunosuppressive tumor microenvironment in HCC. This profound finding may serve as a cornerstone for innovative targeted drug therapies and pave the way for further investigations into the underlying mechanisms of CCDC-related carcinogenesis in liver cancer.

FRI-404 - A systems medicine approach for the identification of potential prognostic biomarkers in patients with acute decompensation of cirrhosis

FRI-404 - A systems medicine approach for the identification of potential prognostic biomarkers in patients with acute decompensation of cirrhosis

Identification of potential prognostic biomarkers in vulval squamous cell carcinoma based on human papillomavirus infection Status-Analysis of GSE183454

This study aimed to elucidate the differences in vulval squamous cell carcinomas (VSCC) based on the HPV infection status. The sequencing data GSE183454 which contains 23 VSCC samples based on its HPV infection status was obtained from the Gene Expression Omnibus (GEO) database. We comprehensively dissected the differences of genomic and tumour microenvironment (TME) immune cell infiltration landscapes between HPV + and HPV- VSCC. The potential molecular mechanisms of prognostic genes were explored by functional enrichment analysis. Five novel key molecules (SYCP2, SMC1B, RNF212, MAJIN and C14orf39) with significantly up-regulated expression in HPV + VSCC were identified while protein-protein interaction (PPI) networks were created upon Cytoscape software. Additionally, VSCC with up-regulated expression of these key molecules exhibited a significantly decreased TME immune cell infiltration. SYCP2 is overexpressed in HPV + VSCC and could be a candidate therapy target for further research. IMPACT STATEMENT What is already known on this subject? VSCC are characterised by two aetiological pathways. The former occurs in the background of lichen sclerosus, while the latter is related to HPV infection. VSCC most commonly arises from the non-HPV related pathway portends worse prognosis than VSCC derived from HPV infection. What do the results of this study add? Five key molecules are identified and significantly up-regulated in HPV + VSCC. In which, SYCP2 is overexpressed in HPV + VSCC and exhibited a significantly decreased TME immune cell infiltration. SYCP2 constant expression could be a potential biomarker of neoplasms associated with HPV and could be a candidate therapy target in VSCC especially HPV + VSCC for further research. What are the implications of these findings for clinical practice and/or further research? SYCP2 could be a candidate therapy target in VSCC especially with HPV + for further research.

Molecular profiling of core immune-escape genes highlights LCK as an immune-related prognostic biomarker in melanoma.

The tumor microenvironment is complicated and continuously evolving. This study was devoted to the identification of potential prognostic biomarkers based on the tumor microenvironment associated with immunotherapy for melanoma. This study integrates a couple of melanoma single cell and transcriptome sequencing datasets and performs a series of silico analyses as nicely as validation of molecular biology techniques. A core set of immune escape related genes was identified through Lawson etal. and the ImmPort portal. The differential proteins were identified through the cBioPortal database. Regression analysis was used to profile independent prognostic factors. Correlation with the level of immune cell infiltration was evaluated by multiple algorithms. The capacity of LCK to predict response was assessed in two independent immunotherapy cohorts. High LCK expression is associated with better prognosis, high levels of TILs and better clinical staging. Pathway analysis showed that high expression of LCK was significantly associated with activation of multiple tumor pathways as well as immune-related pathways. LCK expression tends to be higher in immunotherapy-responsive patients and those with lower IC50s treated with chemotherapeutic agents. RT-qPCR detected that LCK expression was significantly upregulated in melanoma cell lines. Single-cell transcriptome analysis showed that LCK was specifically highly expressed on T cells. CellChat analysis confirmed that LCK in C2 subpopulations and T cell subpopulations exerted immune promotion between cells by binding to CD8 receptors. In conclusion, LCK is a reliable biomarker for melanoma and will contribute to its immunotherapy.

Identification of Potential Prognostic Biomarkers Associated with Monocyte Infiltration in Lung Squamous Cell Carcinoma

The five-year survival rate of lung squamous cell carcinoma is significantly lower than that of other cancer types. It is therefore urgent to discover novel prognosis biomarkers and therapeutic targets and understand their correction with infiltrating immune cells to improve the prognosis of patients with lung squamous cell carcinoma. In this study, we employed robust rank aggregation algorithms to overcome the shortcomings of small sizes and potential bias in each Gene Expression Omnibus dataset of lung squamous cell carcinoma and identified 513 robust differentially expressed genes including 220 upregulated and 293 downregulated genes from six microarray datasets. Functional enrichment analysis showed that these robust differentially expressed genes were obviously involved in the extracellular matrix and structure organization, epidermis development, cell adhesion molecule binding, p53 signaling pathway, and interleukin-17 signaling pathway to affect the progress of lung squamous cell carcinoma. We further identified six hub genes from 513 robust differentially expressed genes by protein-protein interaction network and 10 topological analyses. Moreover, the results of immune cell infiltration analysis from six integrated Gene Expression Omnibus datasets and our sequencing transcriptome data demonstrated that the abundance of monocytes was significantly lower in lung squamous cell carcinoma compared to controls. Immune correlation analysis and survival analysis of hub genes suggested that three hub genes, collagen alpha-1(VII) chain, mesothelin, and chordin-like protein 1, significantly correlated with tumor-infiltrating monocytes as well as may be potential prognostic biomarkers and therapy targets in lung squamous cell carcinoma. The investigation of the correlation of hub gene markers and infiltrating monocytes can also improve to well understand the molecular mechanisms of lung squamous cell carcinoma development.

Identification of potential prognostic biomarkers for hepatocellular carcinoma.

The incidence of liver cancer is increasing every year. Hepatocellular carcinoma (HCC) accounts for nearly 90% of liver cancer, and the overall 5-year survival rate of become of Hepatocellular carcinoma patients less than 20%. However, the molecular mechanism of HCC progression and prognosis still requires further exploration. In this study, we downloaded the gene expression data from the Cancer Genome Atlas (TCGA) Genomic Data and the official website of GEO database. Weighted gene co-expression network analysis (WGCNA) and Pearson's correlation coefficient were utilized to detect the gene modules. The shared differentially-expressed genes (DEGs) were screened out by a Venn diagram, and the hub genes were identified through protein-protein interaction (PPI) network analyses. GO and KEGG enrichment analyses were constructed for these hub genes. Overall survival (OS) and correlation analysis were conducted to investigate the relationship between the hub genes and clinical features. We screened out 27 shared DEGs, and the mainly enriched GO terms were mitotic nuclear division, chromosomal region, and tubulin binding. Furthermore, the top three enriched KEGG pathways were "cell cycle", "oocyte meiosis", and "p53 signaling pathway". According to the Maximal Clique Centrality (MCC) algorithm, the top 10 candidate hub genes were MYC, MCM3, CDC20, CCNB1, BIRC5, UBE2C, TOP2A, RRM2, TK1, and PTTG1, among which BIRC5, CDC20, and UBE2C showed a strong correlation with the OS. Three hub genes (BIRC5, CDC20, and UBE2C) were identified and found to be correlated to the progression and prognosis of HCC. These may become potential targets for HCC therapy.

Construction of survival-related co-expression modules and identification of potential prognostic biomarkers of osteosarcoma using WGCNA.

BackgroundOsteosarcoma (OS) is a primary malignant bone tumor. Patients with different immune characteristics respond differently to chemotherapy and have a lower chance of survival. The potential pathogenesis and therapeutic targets of OS must be investigated further.MethodsOS expression profile data and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and the Gene Expression Omnibus (GEO) databases. The immune-related gene set was obtained from the ImmPort database, and the immune-related gene expression profiles were used for non-negative matrix factorization (NMF) cluster analysis of patients in the 2 databases to find the best clustering number. In the TARGET database, OS patients were classified into low-risk and high-risk groups based on the differences in their survival rates. Weighted correlation network analysis (WGCNA) was applied to the low-risk and high-risk groups to determine the module with the lowest conservatism in order to differentiate the prognosis of the 2 groups.ResultsA total of 500 key genes associated with poor prognosis were identified. Gene Ontology (GO) enrichment analysis revealed that the biological processes of these genes were primarily focused on the regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, collagen-containing extracellular matrix, and Rho GTPase binding. A random survival forest identified EPHB3, TEAD1, and KRR1P1 as key genes. Their expression level was linked to overall survival. We discovered that the core genes were associated to immune cell infiltration. Simultaneously, paired survival analysis of two genes revealed differences in survival. We also reverse-predicted the main genes and developed their competitive endogenous RNA (ceRNA) network. Finally, utilizing the CellMiner database, we observed that the genes TEAD1 and EPHB3 were connected to drug sensitivity.ConclusionsIn this study, we identified the modules and key genes related to the poor prognosis of OS patients by using WGCNA, and verified their impact on the prognosis of OS patients and their role in the immune microenvironment of OS. In addition, targeted gene related antitumor drugs were screened out. The discoveries may lead to novel molecular targets and treatment methods for OS patients.KeywordsOsteosarcoma (OS); weighted gene co-expression network analysis (WGCNA); gene

Demethylation at enhancer upregulates MCM2 and NUP37 expression predicting poor survival in hepatocellular carcinoma patients

BackgroundIdentification of novel biomarker is important for development of molecular-targeted therapy agents for patients with hepatocellular carcinoma (HCC). This study aims to identify potential prognostic biomarkers and investigate epigenetic mechanism of HCC development.MethodsPublic bulk-RNA seq datasets and proteomic dataset were screened for identification of potential prognostic biomarkers for HCC patients. Public methylomic datasets were analyzed for deciphering the epigenetic mechanism regulating HCC-associated gene expression. Immunoblotting, immunohistochemistry, real-time PCR, and pyrosequencing were used to validate the findings from bioinformatic analyses.ResultsMinichromosome maintenance complex component 2 (MCM2) and nucleoporin 37 (NUP37) were overexpressed in human HCC tissues and hepatoma cell lines. MCM2 significantly positively correlated with NUP37 expression. Higher expression of MCM2 or NUP37 was significantly associated with advanced tumor stage and worse overall survival in 3 large independent HCC cohorts (n = 820). MCM2 and NUP37 overexpression are independent prognostic risk factors for HCC patients. Demethylation at an enhancer of MCM2 gene was a common event in patients with HCC, which significantly negatively correlated with MCM2 and NUP37 mRNA expression.ConclusionsDemethylation at enhancer regulates MCM2 and NUP37 expression in HCC. MCM2 and NUP37 are promising prognostic biomarkers and potential targets for epigenetic therapy in HCC patients.

Identification of Potential Prognostic Biomarkers Associated With Macrophage M2 Infiltration in Gastric Cancer.

Gastric cancer is a common cancer afflicting people worldwide. Although incremental progress has been achieved in gastric cancer research, the molecular mechanisms underlying remain unclear. In this study, we conducted bioinformatics methods to identify prognostic marker genes associated with gastric cancer progression. Three hundred and twenty-seven overlapping DEGs were identified from three GEO microarray datasets. Functional enrichment analysis revealed that these DEGs are involved in extracellular matrix organization, tissue development, extracellular matrix–receptor interaction, ECM-receptor interaction, PI3K-Akt signaling pathway, focal adhesion, and protein digestion and absorption. A protein–protein interaction network (PPI) was constructed for the DEGs in which 25 hub genes were obtained. Furthermore, the turquoise module was identified to be significantly positively coexpressed with macrophage M2 infiltration by weighted gene coexpression network analysis (WGCNA). Hub genes of COL1A1, COL4A1, COL12A1, and PDGFRB were overlapped in both PPI hub gene list and the turquoise module with significant association with the prognosis in gastric cancer. Moreover, functional analysis demonstrated that these hub genes play pivotal roles in cancer cell proliferation and invasion. The investigation of the gene markers can help deepen our understanding of the molecular mechanisms of gastric cancer. In addition, these genes may serve as potential prognostic biomarkers for gastric cancer diagnosis.

Prevalence of HPV in Mexican Patients with Head and Neck Squamous Carcinoma and Identification of Potential Prognostic Biomarkers.

Simple SummaryHead and neck squamous cell carcinomas (HNSCC) are a heterogeneous group of neoplasms that show diverse clinical and biological characteristics associated with human papillomavirus (HPV). Biological and clinical characterization is essential to stratify patients based on prognostic and predictive factors. The biological features of HNSCC may change according to geography and population characteristics. Studies on the molecular biology of HNSCC in Mexico are scarce. In the present study, we analyzed 414 Mexican patients with HNSCC and determined the presence and genotype of HPV, p16 expression, and global gene expression profiles. Twenty-two percent of total cases were HPV+, and 32% were p16+. We identified genes associated with survival, such as SLIRP, KLF10, AREG, ACT1, and LIMA. In addition, CSF1R, MYC, and SRC genes were identified as potential therapeutic targets. This study offers information that may be relevant for our understanding of the biology of HNSCC and the development of therapeutic strategies.Head and neck squamous cell carcinomas (HNSCC) show a variety of biological and clinical characteristics that could depend on the association with the human papillomavirus (HPV). Biological and clinical characterization is essential to stratify patients based on prognostic and predictive factors. Reports on HNSCC are scarce in Mexico. Herein, we analyzed 414 Mexican patients with HNSCC, including oropharynx (OPSCC), larynx (LASCC), and oral cavity (OCSCC), and identified HPV DNA and p16 expression. Global gene expression profiles were analyzed in 25 HPV+/p16+ vs. HPV−/p16− cases. We found 32.3% p16+ and 22.3% HPV+ samples, HPV 16, 18, 39, 52, and 31 being the most frequent genotypes. For OPSCC, LASCC and OCSCC, 39.2, 14.7, and 9.6% were HPV+/p16+, respectively. High expression of SLIRP, KLF10, AREG, and LIMA was associated with poor survival; in contrast, high expression of MYB and SYCP2 correlated with better survival. In HPV+ cases, high expression of SLC25A39 and GJB2 was associated with poor survival. Likewise, EGFR, IL-1, IL-6, JAK-STAT, WNT, NOTCH, and ESR1 signaling pathways were downregulated in HPV+ cases. CSF1R, MYC, and SRC genes were identified as key hubs and therapeutic targets. Our study offers information regarding the molecular and clinical characteristics of HNSCC in Mexican patients.

Identification of Potential Prognostic Biomarkers Associated With Cancerometastasis in Skin Cutaneous Melanoma.

Skin cutaneous melanoma (SKCM) is a highly aggressive tumor. The mortality and drug resistance among it are high. Thus, exploring predictive biomarkers for prognosis has become a priority. We aimed to find immune cell-based biomarkers for survival prediction. Here 321 genes were differentially expressed in immune-related groups after ESTIMATE analysis and differential analysis. Two hundred nineteen of them were associated with the metastasis of SKCM via weighted gene co-expression network analysis. Twenty-six genes in this module were hub genes. Twelve of the 26 genes were related to overall survival in SKCM patients. After a multivariable Cox regression analysis, we obtained six of these genes (PLA2G2D, IKZF3, MS4A1, ZC3H12D, FCRL3, and P2RY10) that were independent prognostic signatures, and a survival model of them performed excellent predictive efficacy. The results revealed several essential genes that may act as significant prognostic factors of SKCM, which could deepen our understanding of the metastatic mechanisms and improve cancer treatment.

Long Intergenic Non-Coding RNA LINC00885 Promotes Tumorigenesis of Cervical Cancer by Upregulating MACC1 Expression Through Serving as a Competitive Endogenous RNA for microRNA-432-5p.

PurposeLong intergenic non-protein coding RNA 885 (LINC00885) has been well studied in breast cancer; however, its contribution in cervical cancer remains unclear. In this study, we aimed to determine the detailed functions of LINC00885 in cervical cancer and elucidate the underlying molecular regulation mechanism.MethodsThe expression status of LINC00885 in cervical cancer was determined using reverse transcription-quantitative polymerase chain reaction and by searching The Cancer Genome Atlas database. The detailed functions of LINC00885 in cervical cancer cells were confirmed using Cell Counting Kit 8 assay, flow cytometry analysis, Transwell cell migration and invasion assays, and tumor xenograft assay. Mechanistic experiments included bioinformatics prediction, RNA immunoprecipitation, luciferase reporter assay, and rescue experiments.ResultsLINC00885 was clearly overexpressed in cervical cancer, which was linked with unfavorable clinical outcomes. Functionally, LINC00885 deficiency suppressed cervical cancer cell proliferation, migration, and invasion but stimulated cell apoptosis in vitro. Furthermore, loss of LINC00885 restricted the growth of cervical cancer cells in vivo. Mechanistically, LINC00885 functioned as a competitive endogenous RNA for microRNA-432-5p (miR-432-5p) in cervical cancer. Furthermore, metastasis-associated colon cancer 1 (MACC1) was confirmed as the direct target of miR-432-5p, and LINC00885 could enhance MACC1 expression by sequestering miR-432-5p. Rescue experiments revealed that silencing of miR-432-5p or upregulation of MACC1 expression could effectively counteract the restrained aggressive properties of cervical cancer cells induced by LINC00885 deficiency.ConclusionLINC00885 upregulated MACC1 expression in cervical cancer cells by sponging miR-432-5p, thereby promoting cancer progression. The LINC00885/miR-432-5p/MACC1 pathway may help in the identification of potential prognostic biomarkers and therapeutic targets in cervical cancer.

Glycolysis-Related Genes Serve as Potential Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma.

Metabolic rearrangement is a marker of cancer that has been widely studied in recent years. One of the major metabolic characteristics of tumor cells is the high levels of glycolysis, even under aerobic conditions, a phenomenon that is called the “Warburg effect.” We investigated the expression and copy number variation (CNV) frequency of all glycolysis-related genes in multiple cancer types and found many differentially expressed genes, particularly in clear cell renal cell carcinoma (ccRCC). Single nucleotide variants (SNVs) showed that the overall average mutation frequency for all genes was low. The purpose of this study was to establish a predictive model by studying glycolysis-related genes in ccRCC. We compared the expression of glycolysis-related genes in 539 ccRCC tissues and 72 normal renal tissues from The Cancer Genome Atlas dataset and identified 17 upregulated and 26 downregulated genes. Pathway analysis revealed that PSAT1 and SDHB could activate the cell cycle, RPIA could activate the DNA damage response, and HK3 could activate apoptosis and EMT signaling, while PDK2 could inhibit apoptosis. The results of the drug sensitivity analysis suggested that some of these differentially expressed genes were positively correlated with drug sensitivity. Thirteen genes were selected from the gene coexpression network and the LASSO regression analysis. The Kaplan-Meier overall survival curves showed that the expression of upregulated genes in ccRCC patients was associated with lower overall survival. We established a predictive model consisting of 13 genes (RPIA, G6PD, PSAT1, ENO2, HK3, IDH1, PDK4, PGM2, PGK1, FBP1, OGDH, SUCLA2, and SUCLG2). This predictive model correlated well with the development and progression of ccRCC. Thus, it is of great value in the diagnosis and prognostic evaluation of ccRCC and may aid the identification of potential prognostic biomarkers and drug targets.

Construction of co-expression modules related to survival by WGCNA and identification of potential prognostic biomarkers in glioblastoma.

Glioblastoma (GBM) is a malignant brain tumour with poor prognosis. The potential pathogenesis and therapeutic target are still need to be explored. Herein, TCGA expression profile data and clinical information were downloaded, and the WGCNA was conducted. Hub genes which closely related to poor prognosis of GBM were obtained. Further, the relationship between the genes of interest and prognosis of GBM, and immune microenvironment were analysed. Patients from TCGA were divided into high‐ and low‐risk group. WGCNA was applied to the high‐ and low‐risk group and the black module with the lowest preservation was identified which could distinguish the prognosis level of these two groups. The top 10 hub genes which were closely related to poor prognosis of patients were obtained. GO analysis showed the biological process of these genes mainly enriched in: Cell cycle, Progesterone‐mediated oocyte maturation and Oocyte meiosis. CDCA5 and CDCA8 were screened out as the genes of interest. We found that their expression levels were closely related to overall survival. The difference analysis resulted from the TCGA database proved both CDCA5 and CDCA8 were highly expressed in GBM. After transfection of U87‐MG cells with small interfering RNA, it revealed that knockdown of the CDCA5 and CDCA8 could influence the biological behaviours of proliferation, clonogenicity and apoptosis of GBM cells. Then, single‐gene analysis was performed. CDCA5 and CDCA8 both had good correlations with genes that regulate cell cycle in the p53 signalling pathway. Moreover, it revealed that high amplification of CDCA5 was correlated with CD8+ T cells while CDCA8 with CD4+ T cells in GBM. These results might provide new molecular targets and intervention strategy for GBM.

Identification of potential prognostic biomarkers for breast cancer using WGCNA and PPI integrated techniques

In this study, we aimed to detect promising prognostic factors of breast cancer and interpreted the relevant mechanisms using an integrated bioinformatics analysis. RNA sequencing profile of breast cancer was downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, which were combined as a group (TCGA_GTEx). GSE70947 dataset was from Gene Expression Omnibus. Blue and turquoise modules, respectively identified in TCGA_GTEx database and GSE70947 dataset using weighted co-expression network analysis (WGCNA), were both notably associated with breast cancer. By comparing genes in the two significant modules with differentially expressed genes (DEGs), we obtained a set of 40 shared genes, which were mainly enriched in chromosome segregation and mismatch repair pathway. After protein-protein interaction (PPI) network and overall survival analysis, two hub genes EXO1 and KIF4A were extracted from the set of 40 shared genes, which were up-regulated and associated with the dismal outcome of breast cancer patients. There was a notable negative correlation between EXO1 and KIF4A expression and age of breast cancer patients, whereas a positive relationship with two another clinical traits stage and tumor category was detected. Univariate and multivariate Cox regression analysis revealed that the two hub genes could be independent prognostic factors of breast cancer. Mechanistically, gene correlation analysis suggested that EXO1 and KIF4A exerted their oncogenic role via promoting breast cancer cell proliferation. Overall, our findings identify two promising individual prognostic predictors of breast cancer and pave the new way for diagnosis and therapy strategy of breast cancer.

Screening and identification of potential prognostic biomarkers in metastatic skin cutaneous melanoma by bioinformatics analysis.

Skin cutaneous melanoma (SKCM) is a multifactorial disease that presents a poor prognosis due to its rapid progression towards metastasis. This study focused on the identification of prognostic differentially expressed genes (DEGs) between primary and metastatic SKCM. DEGs were obtained using three chip data sets from the Gene Expression Omnibus database. The protein‐protein interaction network was described by STRING and Cytoscape. Kaplan‐Meier curves were implemented to evaluate survival benefits within distinct groups. A total of 258 DEGs were distinguished as possible candidate biomarkers. Besides, survival curves indicated that DSG3, DSC3, PKP1, EVPL, IVL, FLG, SPRR1A and SPRR1B were of significant value to predict the metastatic transformation of melanoma. To further validate our hypotheses, functional enrichment and significant pathways of the hub genes were performed to indicate that the most involved considerable path. In summary, this study identified substantial DEGs participating in melanoma metastasis. DGS3, DSC3, PKP1, EVPL, IVL, FLG, SPRR1A and SPRR1B may be considered as new biomarkers in the therapeutics of metastatic melanoma, which might help us predict the potential metastatic capability of SKCM patients, thus provide earlier precautionary treatments. However, further experiments are still required to support the specific mechanisms of these hub genes.

Identification of Potential Prognostic Biomarkers for Breast Cancer Based on lncRNA-TF-Associated ceRNA Network and Functional Module.

Breast cancer leads to most of cancer deaths among women worldwide. Systematically analyzing the competing endogenous RNA (ceRNA) network and their functional modules may provide valuable insight into the pathogenesis of breast cancer. In this study, we constructed a lncRNA-TF-associated ceRNA network via combining all the significant lncRNA-TF ceRNA pairs and TF-TF PPI pairs. We computed important topological features of the network, such as degree and average path length. Hub nodes in the lncRNA-TF-associated ceRNA network were extracted to detect differential expression in different subtypes and tumor stages of breast cancer. MCODE was used for identifying the closely connected modules from the ceRNA network. Survival analysis was further used for evaluating whether the modules had prognosis effects on breast cancer. TF motif searching analysis was performed for investigating the binding potentials between lncRNAs and TFs. As a result, a lncRNA-TF-associated ceRNA network in breast cancer was constructed, which had a scale-free property. Hub nodes such as MDM4, ZNF410, AC0842-19, and CTB-89H12 were differentially expressed between cancer and normal sample in different subtypes and tumor stages. Two closely connected modules were identified to significantly classify patients into a low-risk group and high-risk group with different clinical outcomes. TF motif searching analysis suggested that TFs, such as NFAT5, might bind to the promoter and enhancer regions of hub lncRNAs and function in breast cancer biology. The results demonstrated that the synergistic, competitive lncRNA-TF ceRNA network and their functional modules played important roles in the biological processes and molecular functions of breast cancer.

An efficient hybrid feature selection method to identify potential biomarkers in common chronic lung inflammatory diseases

Asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF) are three serious lung inflammatory diseases. The understanding of the pathogenesis mechanism and the identification of potential prognostic biomarkers of these diseases can provide the patients with more efficient treatments. In this study, an efficient hybrid feature selection method was introduced in order to extract informative genes. We implemented an ontology-based ranking approach on differentially expressed genes following a wrapper method. The examination of the different gene ontologies and their combinations motivated us to propose a biological functional-based method to improve the performance of further wrapper methods. The results identified: TOM1L1, SRSF1, and GIT2 in asthma; CHCHD4, PAIP2, CRLF3, UBQLN4, TRAK1, PRELID1, VAMP4, CCM2, and APBB1IP in COPD; and TUFT1, GAB2, B4GALNT1, TNFRSF17, PRDM8, and SETDB2 in IPF as the potential biomarkers. The proposed method can be used to identify hub genes in other high-throughput datasets.

Screening and identification of potential prognostic biomarkers in bladder urothelial carcinoma: Evidence from bioinformatics analysis

Bladder urothelial carcinoma (BLCA) is a very much invasive urological malignant tumor that results in reduced patient survival. At current, the mechanism of BLCA metastasis is not clear. This study was aimed to explore the prognostic and diagnostic related biomarkers in BLCA and guide the therapy. First, we acquired data of gene expression in non-muscle invasive bladder cancer samples (N = 15) and muscle invasive bladder cancer samples (N = 15) from the Gene Expression Omnibus (GEO) database. Integrated analysis was used to explore differentially expressed genes (DEGs) (up and down regulated genes) between non invasive BLCA samples and invasive BLCA samples. Pathway enrichment analyses and Gene ontology (GO) of DEGs were performed. DEGs were used to construct a protein–protein interaction (PPI) network using Protein Interaction Network Analysis platform (PINA) database, and the modules from PPI network were verified by Cytoscape software and hub genes were extracted. Furthermore, gene regulatory networks were depicted based on miRNAs and TFs with interest target genes. The survival analysis, expression analysis, stage analysis, mutation analysis, immunohistochemical (IHC) analysis, ROC analysis and RT-PCR for the integrated expression score was applied to determine the diagnostic ability of the candidate biomarkers. In total, 900 DEGs that consisted of 455 up regulated and 445 down regulated genes were screened out. The differentially expressed genes were enriched in stearate biosynthesis, metabolic pathways, acetate conversion to acetyl-CoA, focal adhesion, oxoacid metabolic process, mitochondrial matrix, circulatory system development and extracellular matrix, of which pathways and GO terms were associated in the BLCA development. PPI network analysis, demonstrated the interactions between those DEGs, and 10 hub genes (P4HB, APEX1, SIRT5, SRP68, HIP1R, MYC, A2M, MAP1B, HIPK2 and TCF) were extracted from the network and verified by survival analysis, expression analysis, stage analysis, mutation analysis, immunohistochemical (IHC) analysis, ROC analysis, RT-PCR and immune infiltration analysis.. In this study, we highlighted mechanisms underlying the control of BLCA molecular signatures by TFs and miRNAs which their alteration reduce the chance of survival rate in BLCA. These results revealed that the DEGs may serve as candidate essential genes during BLCA metastasis. The 10 hub genes, including P4HB, APEX1, SIRT5, SRP68, HIP1R, MYC, A2M, MAP1B, HIPK2 and TCF, may serve as promising prognostic biomarkers in BLCA metastasis. In conclusion, the present study identifies DEGs and hub genes that may be involved in poor prognosis and early recurrence of BLCA. The expression levels of P4HB, APEX1, SIRT5, SRP68, HIP1R, MYC, A2M, MAP1B, HIPK2 and TCF are of high prognostic value, and may help us understand better the underlying carcinogenesis or progression of BLCA. Further studies are required to elucidate molecular pathogenesis and alteration in signaling pathways for these genes in BLCA.