Identification of key genes and pathways in human clear cell renal cell carcinoma (ccRCC) by co-expression analysis

Comparative analysis among the degradation potential of enzymes obtained from Escherichia coli against the toxicity of sulfur dyes through molecular docking

The common bacterium Escherichia coli has demonstrated potential in the field of biodegradation. E. coli is naturally capable of biodegradation because it carries a variety of enzymes that are essential for the breakdown of different substances. The degradation process is effectively catalyzed by these enzymes. The collaborative effects of E. coli's aryl sulfotransferase, alkanesulfonate moonoxygenase, and azoreductase enzymes on the breakdown of sulfur dyes from industrial effluents are investigated in this work. ExPASY ProtParam was used to confirm the stability of the enzyme, showing an instability index less than 40. We determined the maximum binding affinities of these enzymes with sulfur dye pollutants - 1-naphthalenesulfonic acid, sulfogene, sulfur green 3, sulfur red 6, sulfur red 1, sulfur yellow 2, thianthrene, thiazone, and thional - using comparative molecular docking. Significantly, the highest binding affinity was shown by monooxygenase (-12.1), whereas aryl sulfotransferase and azoreductase demonstrated significant energies of -11.8 and -11.4, respectively. The interactions between proteins and ligands in the docked complexes were examined. To evaluate their combined effects, co-expression analysis of genes and enzyme bioengineering were carried out. Using aryl sulfotransferase, alkanesulfonate monooxygenase, and azoreductase, this study investigates the enzymatic degradation of sulfur dye pollutants, thereby promoting environmentally friendly and effective sulfur dye pollutant management.

Identification and validation of m6A-associated ferroptosis genes in renal clear cell carcinoma

Urinary cancer is synonymous with clear cell renal cell carcinoma (ccRCC). Unfortunately, existing treatments for this illness are ineffective and unpromising. Finding novel ccRCC biomarkers is crucial to creating successful treatments. The Cancer Genome Atlas provided clear cell renal cell carcinoma transcriptome data. Functional enrichment analysis was performed on ccRCC and control samples' differentially expressed N6-methyladenosine RNA methylation and ferroptosis-related genes (DEMFRGs). Machine learning was used to find and model ccRCC patients' predicted genes. A nomogram was created for clear cell renal cell carcinoma patients. Prognostic genes were enriched. We examined patients' immune profiles by risk score. Our prognostic genes predicted ccRCC treatment drugs. We found 37 DEMFRGs by comparing 1913 differentially expressed ccRCC genes to 202 m6A RNA methylation FRGs. Functional enrichment analysis showed that hypoxia-induced cell death and metabolism pathways were the most differentially expressed methylation functional regulating genes. Five prognostic genes were found by machine learning: TRIB3, CHAC1, NNMT, EGFR, and SLC1A4. An advanced renal cell carcinoma nomogram with age and risk score accurately predicted the outcome. These five prognostic genes were linked to various cancers. Immunological cell number and checkpoint expression differed between high- and low-risk groups. The risk model successfully predicted immunotherapy outcome, showing high-risk individuals had poor results. NIACIN, TAE-684, ROCILETINIB, and others treat ccRCC. We found ccRCC prognostic genes that work. This discovery may lead to new ccRCC treatments.

Identifying molecular subtypes and tumor microenvironment infiltration signatures in kidney renal clear cell carcinoma based on stemness-associated disulfidptosis genes by integrating machine learning, single-cell analyses and experimental validation

Clear cell renal cell carcinoma (ccRCC) is an aggressive malignant tumor. Disulfidptosis is a new programmed cell death mechanism, which is characterized by the abnormal accumulation of intracellular disulfides that are highly toxic to cells. However, the contribution of disulfidptosis to ccRCC progression has not been fully clarified. In this study, two different molecular subtypes related to disulfidptosis were identified in ccRCC patients by the non-negative matrix factorization (NMF) algorithm. The cluster 1 was characterized by a worse prognosis and higher mRNAsi levels. Then, difference analysis and weighted gene co-expression network analysis (WGCNA) were conducted to search modular genes that are highly associated with tumor stemness and tumor microenvironment. Subsequently, a SADG signature containing nine genes was constructed stepwise by WGCNA and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The high-risk score group had a worse outcome, and immune regulation and metabolic signatures might be responsible for cancer progression in the high-risk group. After that, a predictive nomogram was constructed, and the predicting power of the risk model was verified using inter and three independent external validation datasets. Nine SADGs were shown to significantly correlate with immune infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) and immune checkpoint. In addition, based on the single-cell RNA sequencing dataset (GSE139555), the distribution and expression of nine hub genes in various types of immune cells were analyzed. Finally, the expression level of the nine genes was verified in clinical samples by qRT-PCR.

Pan-cancer analysis reveals the prognostic gene CASR suppresses tumor progression and epithelial-mesenchymal transition in renal clear cell carcinoma

Calcium-sensing receptor (CASR), primarily found in the parathyroid gland and other tissues, plays a crucial role in sensing and regulating extracellular calcium, which was also aberrantly expressed in human tumors. Nevertheless, a comprehensive analysis of CASR in pan-cancer has yet to be conducted. To gain a better understanding of CASR in pan-cancer, data profiles on CASR cancers were collected from TCGA database. The expression level, clinical significance, prognostic value, and potential mechanisms of CASR in pan-cancer were analyzed via multiple public databases. The functional assays were conducted using human kidney renal clear cell carcinoma (KIRC) cell lines, clinical samples, and nude mice. Our research revealed that the abnormal expression of CASR was found in a variety of tumors. The expression and mutation of CASR were significantly associated with tumor prognosis and stage. Pathway analyses suggested that CASR was involved in the epithelial-mesenchymal transition (EMT) progress. Besides, CASR expression was correlated with immune inhibitory genes and immunotherapy in cancers. Particularly in KIRC, we established that CASR mRNA and protein levels were downregulated in clinical samples and cell lines. Moreover, a Cox regression analysis revealed that CASR was an independent prognostic factor in both TCGA-KIRC samples and clinical samples from our center. In vitro and in vivo experiments revealed that blocking CASR with lentivirus could suppress tumor growth and invasion, and EMT progress in KIRC cells. In summary, our study provides a comprehensive bioinformatic analysis of CASR in pan-cancer, offering deeper insights into its function and the EMT mechanism in KIRC, warranting further investigation.

Rational Design of Bioorthogonally Activatable PROTAC for Tumor-Targeted Protein Degradation

Protein degradation mediated by the proteolysis-targeting chimera (PROTAC) has emerged as an efficient strategy to accurately control intracellular protein levels. However, the development of PROTACs is limited by their systemic toxicity. Herein, we report a bioorthogonally activatable prodrug (BT-PROTAC) strategy to accurately control the activity of PROTACs. As a proof of concept, we introduced the highly reactive trans-cyclooctene into PROTAC molecule MZ1, the structure-acitivity relationships of which were well characterized previously, to construct the bioorthogonally activatable prodrug BT-PROTAC. Compared with MZ1, BT-PROTAC is incapable of degradation of BRD4 protein. However, BT-PROTAC can be activated by highly active tetrazine compound BODIPY-TZ in vitro. Furthermore, we could selectively degrade BRD4 protein in tumor tissue enabled by tumor-targeted tetrazine compound IR808-TZ. This strategy may represent an alternative to existing strategies and may be widely applied in the design of BT-PROTAC targeting other proteins.

Identification of hub genes in bladder transitional cell carcinoma through ceRNA network construction integrated with gene network analysis

Bladder transitional cell carcinoma (BTCC) forms more than 90% of bladder cancer cases. It brings challenges to the early diagnosis and therapy of BTCC, due to lack of efficient screening biomarkers. We used weighted gene co-expression network analysis (WGCNA) combined competing endogenous RNA (ceRNA) network construction depending on TCGA datasets to investigate potential hub genes and regulatory pathways associated with occurrence and progression of BTCC. We further used real-time polymerase chain reaction (RT-PCR) to validate the relative expression genes correlated with BTCC. By WGCNA, the gene co-expression module with 11 genes was found corelated with BTCC tumour stage and prognosis after survival analyses. Ultimately, we put 100 highly stage-related genes into the above constructed ceRNA network and then constructed another new network. Among them, all elements in AC112721.1/LINC00473/AC128709.1-hsa-mir-195-RECK and LINC00460-hsa-mir-429-ZFPM2 axes were simultaneously corelated with overall survival. RT-PCR showed that AKAP12 was downregulated in tumour tissues. The hub genes screened out in the present study may provide ideals for further treatment on BTCC.

miR-30c-2-3p suppresses the proliferation of human renal cell carcinoma cells by targeting TOP2A

Background

The ambiguity of renal cell carcinoma (RCC) symptoms hinders early diagnosis, thereby contributing to high mortality rates. By attaching to the 3'-untranslated region (UTR) of the target gene, microRNAs (miRNAs) exert significant control over the expression of genes.

Objectives

To investigate the influence of miR-30c-2-3p and DNA topoisomerase II alpha (TOP2A) on RCC growth and the mechanisms underlying the regulation of its expression.

Methods

The expression of miRNA-30c-2-3p and TOP2A in RCC cells was examined using quantitative real-time polymerase chain reaction (qRT-PCR). MiR-30c-2-3p mimics, its inhibitors, and controls, as well as TOP2A short hairpin RNA (shRNA) and controls, were used to transfect the human RCC cell lines 786-O, Caki-1, and ACHN. Additionally, the roles of miRNA-30c-2-3p and TOP2A in the growth of RCC were evaluated using the cell counting kit (CCK)-8 test, colony formation assay, apoptosis analysis, and Western blotting. Meanwhile, binding of miRNA-30c-2-3p and TOP2A was verified using dual-luciferase reporter assays and Western blotting.

Results

miR-30c-2-p is underexpressed in RCC cells. Overexpression of miR-30c-2-p promotes apoptosis and inhibits proliferation of ACHN, Caki-1, and 786-O cells. miR-30c-2-3p targets TOP2A, which is elevated in RCC tissues and cells, whereas TOP2A silencing inhibits the proliferation ability of RCC cells. The miRNA-30c-2-3p inhibitor compromises TOP2A shRNA-induced apoptosis of RCC. RCC cells cotransfected with miRNA-30c-2-3p inhibitors and TOP2A shRNAs have a higher proliferation rate than those transfected with only TOP2A shRNAs.

Conclusions

Collectively, our results verify that miRNA-30c-2-3p has a tumor suppressor property. miRNA-30c-2-3p inhibits the proliferation of RCC through regulation of TOP2A. The data provide a viable therapeutic target for RCC.

Identification of Feature Genes and Key Biological Pathways in Immune-Mediated Necrotizing Myopathy: High-Throughput Sequencing and Bioinformatics Analysis

Background

Immune-mediated necrotizing myopathy (IMNM), a subgroup of idiopathic inflammatory myopathies (IIMs), is characterized by severe proximal muscle weakness and prominent necrotic fibers but no infiltration of inflammatory cells. IMNM pathogenesis is unclear. This study investigated key biomarkers and potential pathways for IMNM using high-throughput sequencing and bioinformatics technology.

Methods

RNA sequencing was conducted in 18 IMNM patients and 10 controls. A combination of weighted gene coexpression network analysis (WGCNA) and differentially expressed gene (DEG) analysis was conducted to identify IMNM-related DEGs. Feature genes were screened out by employing the protein-protein interaction (PPI) network, support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage selection operator (LASSO). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to verify their differential expression, and the receiver operating characteristic curve (ROC) was used to evaluate their diagnostic efficiency. Functional enrichment analysis was applied to reveal the hidden functions of feature genes. Furthermore, 28 immune cell abundance patterns in IMNM samples were measured.

Results

We identified 193 IMNM-related DEGs that were aberrantly upregulated in the IMNM population and were closely associated with immune-inflammatory responses, regulation of skeletal and cardiac muscle contraction, and lipoprotein metabolism. With the help of the PPI network and the LASSO and SVM-RFE algorithms, three feature genes, LTK, MYBPH, and MYL4, were identified and further confirmed by qRT-PCR. ROC curves among IMNM, dermatomyositis (DM), inclusion body myositis (IBM), and polymyositis (PM) samples validated the LTK and MYL4 genes as IMNM-specific feature markers. In addition, all three genes had a notable association with the autophagy-lysosome pathway and immune-inflammatory responses. Ultimately, IMNM displayed a marked immune-cell infiltrative microenvironment. The most significant correlation was found between CD4 T cells, CD8 T cells, macrophages, natural killer (NK) cells, and dendritic cells (DCs).

Conclusions

LTK, MYBPH, and MYL4 were identified as potential key molecules for IMNM and are believed to play a role in the autophagy-lysosome pathway and muscle inflammation.

Identification of fatty acid metabolism-based molecular subtypes and prognostic signature to predict immune landscape and guide clinical drug treatment in renal clear cell carcinoma

Three subtypes of samples were generated based on genes involved in fatty acid metabolism in The Cancer Genome Atlas (TCGA)-RCC patients using a non-negative matrix factorization (NMF) algorithm. 32 co-expressed modules were identified using WCGNA. We constructed a four-gene signature in our training set using least absolute shrinkage selection operator regression analysis and verified it in our testing and overall sets. A relevant study analysis in clinical trials was conducted, which showed the model had good stability and potential application value for predicting outcomes. We analyzed the immune microenvironment using MCPcounter, CIBERSORT, quanTIseq, TIMER and ESTIMATE algorithms, and the result indicated risk was positively related to T cells, B-lineage, and fibroblasts and negatively correlated with monocytic lineage, myeloid dendritic cells, neutrophils, and endothelial cells, and CPT1B was positively related to T cells, CD8 + T cells, Cytotoxic lymphocytes and NK cells, and negatively correlated with myeloid dendritic cells, fibroblasts, endothelial cells. Tumor mutation burden was positively related to risk score and the expression of CPT1B using the R packages corrplot, circlize. Through the R package pRRophetic, drug sensitivity tests showed that the low-risk score group would benefit more from sunitinib and less from pazopanib, sorafenib, temsirolimus, gemcitabine and doxorubicin than the high-risk score group. We performed the relevant basic assay validation for CPT1B, and the proliferation ability of RCC cells was inhibited after the knockdown of protein expression of CPT1B. In conclusion, we established a four-gene model that can predict outcomes of RCC with potential applications in diagnosis and treatment.

HIF2α-induced upregulation of RNASET2 promotes triglyceride synthesis and enhances cell migration in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC), the most common malignant subtype of renal cell carcinoma, is characterized by the accumulation of lipid droplets in the cytoplasm. RNASET2 is a protein coding gene with a low expression level in ovarian cancers, but it is overexpressed in poorly differentiated neuroendocrine carcinomas. There is a correlation between RNASET2 upregulation and triglyceride expression levels in human serum but is unknown whether such an association is a factor contributing to lipid accumulation in ccRCC. Herein, we show that RNASET2 expression levels in ccRCC tissues and cell lines are significantly higher than those in both normal adjacent tissues and renal tubular epithelial cells. Furthermore, its upregulation is associated with increases in ccRCC malignancy and declines in patient survival. We also show that an association exists between increases in both cytoplasmic lipid accumulation and HIF-2α transcription factor upregulation, and increases in both RNASET2 and triglyceride expression levels in ccRCC tissues. In addition, DGAT1 and DGAT2, two key enzymes involved in triglyceride synthesis, are highly expressed in ccRCC tissues. By contrast, RNASET2 knockdown inhibited their expression levels and lowered lipid droplet accumulation, as well as suppressing in vitro cell proliferation, cell invasion, and migration. In conclusion, our data suggest HIF2α upregulates RNASET2 transcription in ccRCC cells, which promotes both the synthesis of triglycerides and ccRCC migration. As such, RNASET2 may have the potential as a biomarker or target for the diagnosis and treatment of ccRCC.

circFOXO3 Induced by KLF16 Modulates Clear Cell Renal Cell Carcinoma Growth and Natural Killer Cell Cytotoxic Activity through Sponging miR-29a-3p and miR-122-5p

Renal cell carcinoma (RCC) is one of the most common urological malignancies with high incidence and metastatic relapse. Clear cell RCC (ccRCC) comprises nearly 70% of all RCC cases and is responsible for the majority of morbidity and mortality of RCC. Due to the poor diagnosis strategy and unsatisfactory clinical intervention, ccRCC causes a huge economic burden and poor patient quality of life; therefore, novel diagnostic or therapeutic targets for ccRCC are urgently needed. This study investigated the biological role of circFOXO3 in ccRCC development, showing that circFOXO3 is highly expressed in RCC cells and tissues and inhibits the viability of ccRCC cells. circFOXO3 dysregulation regulates NK cell cytotoxicity towards RCC cells by directly sponging miR-29a-3p and miR-122-5p. Overexpression of miR-29a-3p or miR-122-5p attenuated NK cell toxicity towards RCC cells and the transcriptional factor Kruppel-Like Factor 16 (KLF16) regulates circFOXO3 expression in RCC cells. In conclusion, this study has partially elucidated the function of circFOXO3 in ccRCC development, providing potential novel therapeutic targets for ccRCC.

Integrated bioinformatics data analysis reveals a risk signature and PKD1 induced progression in endometrial cancer patients with postmenopausal status

Background: Endometrial cancer (EC) is one of the most common type of female genital malignancies. The purpose of the present study was to reveal the underlying oncogene and mechanism that played a pivotal role in postmenopausal EC patients.

Methods: Weighted gene co-expression network analysis (WGCNA) was conducted using the microarray dataset and clinical data of EC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify significant gene modules and hub genes associated with postmenopausal status in EC patients. LASSO regression was conducted to build and validate the risk model. Finally, expression of hub gene was validated in pre- and post-menopausal EC patients in our center.

Results: 1240 common genes were used to construct the WGCNA model. According to the WGCNA results, we identified a brown module with 471 genes which was significantly associated with postmenopausal status in EC patients. Furthermore, we constructed an 11-gene risk signature to predict the overall survival of EC patients. The Kaplan–Meier curve and area under the ROC curve (AUC) of this model showed high accuracy in prediction. We also validate the risk model in patients in our center and it also has a high accuracy. Among the 11 genes, PKD1 was recognized as a potential biomarker in the progression of EC patients with postmenopausal status.

Conclusion: Taken together, we uncovered a common PKD1-mediated mechanism underlying postmenopausal EC patients’ progression by integrated analyses. This finding may improve targeted therapy for EC patients.

Identification of UBE2I as a Novel Biomarker in ccRCC Based on a Large-Scale CRISPR-Cas9 Screening Database and Immunohistochemistry

Background: The genome-wide CRISPR-cas9 dropout screening has emerged as an outstanding approach for characterization of driver genes of tumor growth. The present study aims to investigate core genes related to clear cell renal cell carcinoma (ccRCC) cell viability by analyzing the CRISPR-cas9 screening database DepMap, which may provide a novel target in ccRCC therapy.

Methods: Candidate genes related to ccRCC cell viability by CRISPR-cas9 screening from DepMap and genes differentially expressed between ccRCC tissues and normal tissues from TCGA were overlapped. Weighted gene coexpression network analysis, pathway enrichment analysis, and protein–protein interaction network analysis were applied for the overlapped genes. The least absolute shrinkage and selection operator (LASSO) regression was used to construct a signature to predict the overall survival (OS) of ccRCC patients and validated in the International Cancer Genome Consortium (ICGC) and E-MTAB-1980 database. Core protein expression was determined using immunohistochemistry in 40 cases of ccRCC patients.

Results: A total of 485 essential genes in the DepMap database were identified and overlapped with differentially expressed genes in the TCGA database, which were enriched in the cell cycle pathway. A total of four genes, including UBE2I, NCAPG, NUP93, and TOP2A, were included in the gene signature based on LASSO regression. The high-risk score of ccRCC patients showed worse OS compared with these low-risk patients in the ICGC and E-MTAB-1980 validation cohort. UBE2I was screened out as a key gene. The immunohistochemistry indicated UBE2I protein was highly expressed in ccRCC tissues, and a high-level nuclear translocation of UBE2I occurs in ccRCC. Based on the area under the curve (AUC) values, nuclear UBE2I had the best diagnostic power (AUC = 1). Meanwhile, the knockdown of UBE2I can inhibit the proliferation of ccRCC cells.

Conclusion: UBE2I, identified by CRISPR-cas9 screening, was a core gene-regulating ccRCC cell viability, which accumulated in the nucleus and acted as a potential novel promising diagnostic biomarker for ccRCC patients. Blocking the nuclear translocation of UBE2I may have potential therapeutic value with ccRCC patients.

Integrative Bioinformatics Analysis Demonstrates the Prognostic Value of Chromatin Accessibility Biomarkers in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) accounts for 75%–85% of renal cell carcinoma (RCC) and has a poor 5-year survival rate. In recent years, medical advancement has promoted the understanding of the histopathological and molecular characterization of ccRCC; however, the carcinogenesis and molecular mechanisms of ccRCC remain unclear. Chromatin accessibility is an essential determinant of cellular phenotype. This study aimed to explore the potential role of chromatin accessibility in the development and progression of ccRCC. By the combination of open-access genome-wide chromatin accessibility profiles and gene expression profiles in ccRCC, we obtained a total of 13,474 crucial peaks, corresponding to 5,120 crucial genes and 9,185 differentially expressed genes. Moreover, two potential function modules (P2 and G4) that contained 129 upregulated genes were identified via the weighted gene co-expression network analysis (WGCNA). Furthermore, we obtained five independent predictors (FSCN1, SLC17A9, ANKRD13B, ADCY2, and MAPT), and a prognostic model was established based on these genes through the least absolute shrinkage and selection operator-proportional hazards model (LASSO-Cox) analysis. This model can stratify the ccRCC samples into a high-risk and a low-risk group, from which the patients have distinct prognosis. Further analysis demonstrated a completely different immune cell infiltration pattern between these two risk groups. This study also suggested that mast cell resting is associated with the prognosis of ccRCC and could be a target of immunotherapy. Overall, this study indicated that chromatin accessibility plays an essential role in ccRCC. The five prognostic chromatin accessibility biomarkers and the prognostic immune cells can provide a new direction for the treatment of ccRCC.

Screening Prognosis-Related lncRNAs Based on WGCNA to Establish a New Risk Score for Predicting Prognosis in Patients with Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) remains an important cause of cancer death. The molecular mechanism of hepatocarcinogenesis and prognostic factors of HCC have not been completely uncovered.

METHODS

In this study, we screened out differentially expressed lncRNAs (DE lncRNAs), miRNAs (DE miRNAs), and mRNAs (DE mRNAs) by comparing the gene expression of HCC and normal tissue in The Cancer Genome Atlas (TCGA) database. DE mRNAs were used to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the miRNA and lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a competitive endogenous RNA (ceRNA) network by weighted gene coexpression network analysis (WGCNA). Moreover, univariable Cox regression and Kaplan-Meier curve analyses of DE lncRNAs and DE mRNAs were conducted. Finally, the lasso-penalized Cox regression analysis and nomogram model were used to establish a new risk scoring system and predict the prognosis of patients with liver cancer. The expression of survival-related DE lncRNAs was verified by qRT-PCR.

RESULTS

A total of 1896 DEmRNAs, 330 DElncRNAs, and 76 DEmiRNAs were identified in HCC and normal tissue samples. Then, the turquoise miRNA and turquoise lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a ceRNA network by WGCNA. In this ceRNA network, there were 566 lncRNA-miRNA-mRNA regulatory pairs, including 30 upregulated lncRNAs, 16 downregulated miRNAs, and 75 upregulated mRNAs. Moreover, we screened out 19 lncRNAs and 14 hub mRNAs related to prognosis from this ceRNA network by univariable Cox regression and Kaplan-Meier curve analyses. Finally, a new risk scoring system was established by selecting the optimal risk lncRNAs from the 19 prognosis-related lncRNAs through lasso-penalized Cox regression analysis. In addition, we established a nomogram model consisting of independent prognostic factors to predict the survival rate of HCC patients. Finally, the correlation between the risk score and immune cell infiltration and gene set enrichment analysis were determined.

CONCLUSIONS

In conclusion, the results may provide potential biomarkers or therapeutic targets for HCC and the establishment of the new risk scoring system and nomogram model provides the new perspective for predicting the prognosis of HCC.

Prognostic Impact of MITD1 and Associates With Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most malignant diseases with poor survival rate over the world. The tumor microenvironment (TME) is highly related to the oncogenesis, development, and prognosis of KIRC. Thus, making the identification of KIRC biomarkers and immune infiltrates critically important. Microtubule Interacting and Trafficking Domain containing 1(MITD1) was reported to participate in cytokinesis of cell division. In the present study, multiple bioinformatics tools and databases were applied to investigate the expression level and clinical value of MITD1 in KIRC. We found that the expression of MITD1 was significantly increased in KIRC tissues. Further, the KIRC patients with high MITD1 levels showed a worse overall survival (OS) rate and disease free survival (DFS) rate. Otherwise, we found a significant correlation MITD1 expression and the abundance of CD8+ T cells. Functional enrichment analyses revealed that immune response and cytokine-cytokine receptor are very critical signaling pathways which associated with MITD1 in KIRC. In conclusion, our findings indicated that MITD1 may be a potential biomarker and associated with immune infiltration in KIRC.

Development of a novel gene signature to predict prognosis and response to PD-1 blockade in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common kidney malignancy characterized by a poor prognosis. The treatment efficacy of immune checkpoint inhibitors (ICIs) also varies widely in advanced ccRCC. We aim to construct a robust gene signature to improve the prognostic discrimination and prediction of ICIs for ccRCC patients. In this study, adopting differentially expressed genes from seven ccRCC datasets in GEO (Gene Expression Omnibus), a novel signature (FOXM1&TOP2A) was constructed in TCGA (The Cancer Genome Atlas) database by LASSO and Cox regression. Survival and time-dependent ROC analysis revealed the strong predictive ability of our signature in discovery set, two online validation sets and one tissue microarray (TMA) from our institution. High-risk group based on the signature comprises more high-grade (G3&G4) and advanced pathologic stage (stageIII/IV) tumors and presents hyperactivation of cell cycle process according to the functional analysis. Meanwhile, high-risk tumors demonstrate an immunosuppressive phenotype with more infiltrations of regulatory T cells (Tregs), macrophages and high expressions of genes negatively regulating anti-tumor immunity. Low-risk tumors have an improved response to anti-PD-1 therapy and the predictive ability of our signature is better than other recognized biomarkers in ccRCC. A nomogram containing this signature showed a high predictive accuracy with AUCs of 0.90 and 0.84 at 3 and 5 years. Overall, this robust signature could predict prognosis, evaluate immune microenvironment and response to anti-PD-1 therapy in ccRCC, which is very promising in clinical promotion.

miR-145-5p as a predictive biomarker for breast cancer stemness by computational clinical investigation

BACKGROUND

Breast tumors enriched with breast cancer stem cells (BCSCs), play a crucial role in metastasis and tumor relapse. Hence, targeting BCSCs may lead to efficacious breast cancer therapy. BCSCs have a unique expression of stemness markers, including Nanog, POU5F1, SOX2, and CD44, which play a vital role in cancer stem cell properties. However, the regulation of microRNAs (miRNAs)-mediated cancer stem cell marker expressions is largely unclear.

METHODS

MIENTURNET was used to predict miRNA-target interactions. miR-TV, UALCAN and GEPIA databases were used to analyze the expression of miR-145-5p and SOX2. Survival analysis was obtained by cBioportal, KM plotter and Breast Cancer Gene-Expression Miner. RNAComposer was used to perform miRNA-mRNA duplex prediction. In vitro mRNA and miRNA analysis was performed by qRT-PCR.

RESULTS

It was observed that miR-145-5p was the common miRNA targeting stemness markers. miR-145-5p expression was found to be lower in breast cancer patients compared to healthy subjects. Based on survival analysis, low expression of miR-145-5p and high expression of SOX2 led to a poor overall survival rate in breast cancer patients. Pathway enrichment analysis indicated that SOX2 was highly enriched with transcription factors. Moreover, SOX2 expression level was also upregulated in axillary metastatic lymph nodules. Further, in vitro ectopic expression of miR-145-5p by its mimic downregulated the SOX2 expression compared to the control mimic. Overall, SOX2 was a direct target for miR-145-5p as per the binding and minimal-free energy.

CONCLUSIONS

In this study, miR-145-5p targeting SOX2 was identified as a potential predictive biomarker for breast cancer stemness.

Involvement of plasminogen activator inhibitor-1 and its related molecules in atrial fibrosis in patients with atrial fibrillation

Atrial fibrillation is the most common form of cardiac arrhythmia. Atrial fibrosis is a significant feature of atrial fibrillation though its mechanism is not well understood. We searched the Gene Expression Omnibus database to compare mRNA expression patterns between atrial fibrillation and sinus rhythm samples; one hundred and forty eight differentially expressed genes were identified. Most of these genes were significantly enriched in the extracellular matrix organization process and collagen-activated tyrosine kinase receptor signaling pathway. To screen hub genes involved in atrial fibrosis, we constructed a protein-protein interaction network and found that three hub genes (SERPINE1/plasminogen activator inhibitor-1/PAI-1, TIMP Metallopeptidase Inhibitor 3/TIMP3 and decorin/DCN) play vital roles in atrial fibrosis, especially plasminogen activator inhibitor-1. Elevated plasminogen activator inhibitor-1 expression was positively correlated with the p53 signaling pathway. Plasminogen activator inhibitor-1 and p53 protein expression levels were verified in patients with sinus rhythm and atrial fibrillation by Western blot analysis. Compared with the sinus rhythm controls, p53 and plasminogen activator inhibitor-1 protein expressions were upregulated in the atrial tissues of patients with atrial fibrillation. p53 was also found to regulate plasminogen activator inhibitor-1 based on the results of cellular and molecular experiments. Thus, the p53/plasminogen activator inhibitor-1 signaling axis may participate in the pathophysiological processes of atrial fibrillation, and plasminogen activator inhibitor-1 may serve as a new therapeutic biomarker in atrial fibrillation.

Prediction and analysis of novel key genes ITGAX, LAPTM5, SERPINE1 in clear cell renal cell carcinoma through bioinformatics analysis

Background

Clear Cell Renal Cell Carcinoma (CCRCC) is the most aggressive subtype of Renal Cell Carcinoma (RCC) with high metastasis and recurrence rates. This study aims to find new potential key genes of CCRCC.

Methods

Four gene expression profiles (GSE12606, GSE53000, GSE68417, and GSE66272) were downloaded from the Gene Expression Omnibus (GEO) database. The TCGA KIRC data was downloaded from The Cancer Genome Atlas (TCGA). Using GEO2R, the differentially expressed genes (DEG) in CCRCC tissues and normal samples were analyzed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed in DAVID database. A protein-protein interaction (PPI) network was constructed and the hub gene was predicted by STRING and Cytoscape. GEPIA and Kaplan-Meier plotter databases were used for further screening of Key genes. Expression verification and survival analysis of key genes were performed using TCGA database, GEPIA database, and Kaplan-Meier plotter. Receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of key genes in CCRCC, which is plotted by R software based on TCGA database. UALCAN database was used to analyze the relationship between key genes and clinical pathology in CCRCC and the methylation level of the promoter of key genes in CCRCC.

Results

A total of 289 up-regulated and 449 down-regulated genes were identified based on GSE12606, GSE53000, GSE68417, and GSE66272 profiles in CCRCC. The upregulated DEGs were mainly enriched with protein binding and PI3K-Akt signaling pathway, whereas down-regulated genes were enriched with the integral component of the membrane and metabolic pathways. Next, the top 35 genes were screened out from the PPI network according to Degree, and three new key genes ITGAX, LAPTM5 and SERPINE1 were further screened out through survival and prognosis analysis. Further results showed that the ITGAX, LAPTM5, and SERPINE1 levels in CCRCC tumor tissues were significantly higher than those in normal tissues and were associated with poor prognosis. ROC curve shows that ITGAX, LAPTM5, and SERPINE1 have good diagnostic value with good specificity and sensitivity. The promoter methylation levels of ITGAX, LAPTM5 and SERPINE1 in CCRCC tumor tissues were significantly lower than those in normal tissues. We also found that key genes were associated with clinical pathology in CCRCC.

Conclusion

ITGAX, LAPTM5, and SERPINE1 were identified as novel key candidate genes that could be used as prognostic biomarkers and potential therapeutic targets for CCRCC.

Determination of the key ccRCC-related molecules from monolayer network to three-layer network

Clear cell renal cell carcinoma (ccRCC), with an increasing incidence rate, is one of the ubiquitous cancers. Its pathogenic factors are complicated and the molecular mechanism is not clear. It is essential to analyze the potential key genes related to ccRCC carcinogenesis. In this study, the differentially expressed mRNAs, miRNAs and lncRNAs (DEmRNAs, DEmiRNAs and DElncRNAs) of ccRCC were screened from TCGA database. Then the miRNA-mRNA network, lncRNA-miRNA network and lncRNA-mRNA network were constructed by online database or WGCNA algorithm. Topology attributes of these monolayer networks showed that hsa-mir-155, hsa-mir-200c, hsa-mir-122, hsa-mir-506, hsa-mir-216b, hsa-mir-141, lncRNA AC137723.1 and AC021074.3 are the crucial genes related with the regulatory effects on the proliferation, metastasis and invasion of ccRCC cells. Subsequently, these three monolayer networks were integrated into a lncRNA-miRNA-mRNA multilayer network. Considering node degree, closeness centrality and betweenness centrality, we found hsa-mir-122 is screened out as the only crucial gene in three-layer network. In order to better illustrate the effect of hsa-mir-122 on ccRCC, the lncRNA-hsa-mir-122-mRNA network was constructed with hsa-mir-122 as the center. Pathway analysis of the unique target gene GALNT3 linked to hsa-mir-122 showed that GALNT3 influenced the metabolic process of mucin type O-Glycan biosynthesis. LncRNA AC090377.1 is the unique gene that has target genes among lncRNAs with clinical significance that linked to hsa-mir-122 in the lncRNA-hsa-mir-122-mRNA network. Pathway analysis of AC090377.1 suggested that GUCY2F enriched in phototransduction pathway associated with retina. From monolayer network to three-layer network, hsa-mir-122 is identified as an important molecule in the oncogenesis and progression of ccRCC, offering new strategies to further study of the carcinogenic mechanism of ccRCC.

Association between SNAP25 and human glioblastoma multiform: a comprehensive bioinformatic analysis

Background: Glioblastoma multiforme (GBM) is a most common aggressive malignant brain tumor. In recent years, targeted therapy has been increasingly applied in GBM treatment.

Methods: In the present study, GSE22866 was downloaded from gene expression omnibus (GEO). The genomic and clinical data were obtained from TCGA. The differentially expressed genes (DEGs) were identified and functional analysis was performed using clusterprofiler. Then, the co-expression network for the DEGs was established using the “WGCNA” package. Next, the protein–protein interaction (PPI) was assessed using Search Tool for the Retrieval of Interacting Genes Database (STRING) and hub modules in Cytoscape were screened. The Venn diagram was plotted to showcase the overlapped hub DEGs in PPI network and TCGA. Univariate and multivariate Cox proportional hazards regression analyses were performed to predict the risk score of each patient. Validations of the hub gene were completed in other databases.

Results: Functional analysis of the DEGs verified the involvement of DEGs in growth factor binding and gated channel activity. Among the 10 GBM-related modules, the red one displayed the strongest tie with GBM. VAMP2 was filtered out as the most intimate protein. The PPI network and TCGA were comprehensively analyzed. Finally, SNAP25 was identified as a real hub gene positively correlated with GBM prognosis. The result was validated by GEPIA, ONCOMINE database and qRT-PCR.

Conclusions: SNAP25 might act as a GBM suppressor and a biomarker in GBM treatment.

Identification of metastasis and prognosis-associated genes for serous ovarian cancer

Serous ovarian cancer is one of the most fatal gynecological tumors with an extremely low 5-year survival rate. Most patients are diagnosed at an advanced stage with wide metastasis. The dysregulation of genes serves an important role in the metastasis progression of ovarian cancer. Differentially expressed genes (DEGs) between primary tumors and metastases of serous ovarian cancer were screened out in the gene expression profile of GSE73168 from Gene Expression Omnibus (GEO). Cytoscape plugin cytoHubba and weighted gene co-expression network analysis (WGCNA) were utilized to select hub genes. Univariate and multivariate Cox regression analyses were used to screen out prognosis-associated genes. Furthermore, the Oncomine validation, prognostic analysis, methylation mechanism, gene set enrichment analysis (GSEA), TIMER database analysis and administration of candidate molecular drugs were conducted for hub genes. Nine hundred and fifty-seven DEGs were identified in the gene expression profile of GSE73168. After using Cytoscape plugin cytoHubba, 83 genes were verified. In co-expression network, the blue module was most closely related to tumor metastasis. Furthermore, the genes in Cytoscape were analyzed, showing that the blue module and screened 17 genes were closely associated with tumor metastasis. Univariate and multivariate Cox regression revealed that the age, stage and STMN2 were independent prognostic factors. The Cancer Genome Atlas (TCGA) suggested that the up-regulated expression of STMN2 was related to poor prognosis of ovarian cancer. Thus, STMN2 was considered as a new key gene after expression validation, survival analysis and TIMER database validation. GSEA confirmed that STMN2 was probably involved in ECM receptor interaction, focal adhesion, TGF beta signaling pathway and MAPK signaling pathway. Furthermore, three candidate small molecule drugs for tumor metastasis (diprophylline, valinomycin and anisomycin) were screened out. The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and western blot showed that STMN2 was highly expressed in ovarian cancer tissue and ovarian cancer cell lines. Further studies are needed to investigate these prognosis-associated genes for new therapy target.

Integrated analysis identifies oxidative stress genes associated with progression and prognosis in gastric cancer

Oxidative stress (OS) reactions are reported to be associated with oncogenesis and tumor progression. However, little is known about the potential diagnostic value of OS in gastric cancer (GC). This study identified hub OS genes associated with the prognosis and progression of GC and illustrated the underlying mechanisms. The transcriptome data and corresponding GC clinical information were collected from The Cancer Genome Atlas (TCGA) database. Aberrantly expressed OS genes between tumors and adjacent normal tissues were screened, and 11 prognosis-associated genes were identified with a series of bioinformatic analyses and used to construct a prognostic model. These genes were validated in the Gene Expression Omnibus (GEO) database. Furthermore, weighted gene co-expression network analysis (WGCNA) was subsequently conducted to identify the most significant hub genes for the prediction of GC progression. Analysis revealed that a good prognostic model was constructed with a better diagnostic accuracy than other clinicopathological characteristics in both TCGA and GEO cohorts. The model was also significantly associated with the overall survival of patients with GC. Meanwhile, a nomogram based on the risk score was established, which displayed a favorable discriminating ability for GC. In the WGCNA analysis, 13 progression-associated hub OS genes were identified that were also significantly associated with the progression of GC. Furthermore, functional and gene ontology (GO) analyses were performed to reveal potential pathways enriched with these genes. These results provide novel insights into the potential applications of OS-associated genes in patients with GC.

Identification of key genes controlling cancer stem cell characteristics in gastric cancer

BACKGROUND

Self-renewal of gastric cancer stem cells (GCSCs) is considered to be the underlying cause of the metastasis, drug resistance, and recurrence of gastric cancer (GC).

AIM

To characterize the expression of stem cell-related genes in GC.

METHODS

RNA sequencing results and clinical data for gastric adenoma and adenocarcinoma samples were obtained from The Cancer Genome Atlas database, and the results of the GC mRNA expression-based stemness index (mRNAsi) were analyzed. Weighted gene coexpression network analysis was then used to find modules of interest and their key genes. Survival analysis of key genes was performed using the online tool Kaplan-Meier Plotter, and the online database Oncomine was used to assess the expression of key genes in GC.

RESULTS

mRNAsi was significantly upregulated in GC tissues compared to normal gastric tissues (P < 0.0001). A total of 16 modules were obtained from the gene coexpression network; the brown module was most positively correlated with mRNAsi. Sixteen key genes (BUB1, BUB1B, NCAPH, KIF14, RACGAP1, RAD54L, TPX2, KIF15, KIF18B, CENPF, TTK, KIF4A, SGOL2, PLK4, XRCC2, and C1orf112) were identified in the brown module. The functional and pathway enrichment analyses showed that the key genes were significantly enriched in the spindle cellular component, the sister chromatid segregation biological process, the motor activity molecular function, and the cell cycle and homologous recombination pathways. Survival analysis and Oncomine analysis revealed that the prognosis of patients with GC and the expression of three genes (RAD54L, TPX2, and XRCC2) were consistently related.

CONCLUSION

Sixteen key genes are primarily associated with stem cell self-renewal and cell proliferation characteristics. RAD54L, TPX2, and XRCC2 are the most likely therapeutic targets for inhibiting the stemness characteristics of GC cells.

TYROBP is a potential prognostic biomarker of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) exhibits high recurrence and metastasis rates. Although target therapy has significantly improved the prognosis of some patients with ccRCC, the median survival rate remains poor. Thus, there remains a need for the identification of novel potential targets for diagnosis and therapy. Here, we screened differentially expressed genes between ccRCC and normal tissues through analyzing The Cancer Genome Atlas database. We identified 55 up‐regulated and 67 down‐regulated genes associated with poor prognosis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that these genes were associated with glycometabolic process, complement and coagulation cascades. In addition, the eight down‐regulated genes (HRG, FABP1, ALDOB, PCK1, HAO2, CASR, PLG, and HMGCS2) and two up‐regulated genes (SERPINE1 and TYROBP) were filtered out. Finally, TYROBP was selected through repeated verification of various databases. High expression of TYROBP is associated with low survival rate in ccRCC, is closely related to immune cell infiltration and is coexpressed with Programmed cell death protein‐1(PD‐1) and Cytotoxic T lymphocyte‐associated antigen‐4(CTLA‐4). In conclusion, TYROBP may have potential for diagnosis and treatment of ccRCC.

Identification of unique long non-coding RNAs as putative biomarkers for chromophobe renal cell carcinoma

Aim: To investigate whether long non-coding RNAs (lncRNAs) can be utilized as molecular biomarkers in predicting the occurrence and progression of chromophobe renal cell carcinoma. Methods & results: Genetic and related clinical traits of chromophobe renal cell carcinoma were downloaded from the Cancer Genome Atlas and used to construct modules using weighted gene coexpression network analysis. In total, 44,889 genes were allocated into 21 coexpression modules depending on intergenic correlation. Among them, the green module was the most significant key module identified by module-trait correlation calculations (R² = 0.43 and p = 4e-04). Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses demonstrated that genes in the green module were enriched in many pathways. Coexpression, protein-protein interaction networks, screening for differentially expressed genes, and survival analysis were used to select hub lncRNAs. Five hub lncRNAs (TTK, CENPE, KIF2C, BUB1, and RAD51AP1) were selected out. Conclusion: Our findings suggest that the five lncRNAs may act as potential biomarkers for chromophobe renal cell carcinoma progression and prognosis.

Influence of gene expression on survival of clear cell renal cell carcinoma

Approximately 10%‐20% of patients with clinically localized clear cell renal cell carcinoma (ccRCC) at time of surgery will subsequently experience metastatic progression. Although considerable progression was seen in the systemic treatment of metastatic ccRCC in last 20 years, once ccRCC spreads beyond the confines of the kidney, 5‐year survival is less than 10%. Therefore, significant clinical advances are urgently needed to improve overall survival and patient care to manage the growing number of patients with localized ccRCC. We comprehensively evaluated expression of 388 candidate genes related with survival of ccRCC by using TCGA RNAseq (n = 515), Total Cancer Care (TCC) expression array data (n = 298), and a well characterized Moffitt RCC cohort (n = 248). We initially evaluated all 388 genes for association with overall survival using TCGA and TCC data. Eighty‐one genes were selected for further analysis and tested on Moffitt RCC cohort using NanoString expression analysis. Expression of nine genes (AURKA, AURKB, BIRC5, CCNE1, MK167, MMP9, PLOD2, SAA1, and TOP2A) was validated as being associated with poor survival. Survival prognostic models showed that expression of the nine genes and clinical factors predicted the survival in ccRCC patients with AUC value: 0.776, 0.821 and 0.873 for TCGA, TCC and Moffitt data set, respectively. Some of these genes have not been previously implicated in ccRCC survival and thus potentially offer insight into novel therapeutic targets. Future studies are warranted to validate these identified genes, determine their biological mechanisms and evaluate their therapeutic potential in preclinical studies.

Depletion of lncRNA MALAT1 inhibited sunitinib resistance through regulating miR-362-3p-mediated G3BP1 in renal cell carcinoma

Long non-coding RNA metastasis associated with lung adenocarcinoma transcript 1 (MALAT1) contributes to chemotherapy resistance in some cancers, but the role of MALAT1 in sunitinib (SU) chemoresistance of carcinoma (RCC) is still unknown. In this study, MALAT1 expression in SU-resistance tumor tissues and cells was tested by qRT-PCR. Then, CCK-8, Annexin V-FITC/PI, transwell, and Western blotting assays were used to evaluate cell viability and IC50, apoptosis, cell invasion, and resistance of SU-resistance RCC cells after transfected with small interfering RNA against MALAT1. Further, RNA pull-down and luciferase reporter assay were applied to investigate the underlying mechanism of MALAT1 in SU resistance. The results showed that MALAT1 expression was dramatically upregulated in SU-resistance RCC tissues and cell lines. Knockdown of MALAT1 inhibited proliferation, invasion, and SU chemoresistance, but induced apoptosis in RCC cells. The results of RNA pull-down and luciferase reporter assay indicated that MALAT1 could interact with miR-362-3p and miR-362-3p interact with RasGAP SH3-domain-Binding Protein 1 (G3BP1). Moreover, G3BP1 also played a role in SU chemoresistance of RCC cells, and MALAT1 could perform as a miR-362-3p sponge to modulate G3BP1 expression. Rescue experiments suggested that downregulation of miR-362-3p and overexpression of G3BP1 can reverse the SU chemosensitivity of MALAT1 knockdown in RCC cells. In conclusion, depletion of LncRNA MALAT1 inhibited SU chemoresistance through modulating G3BP1 via sponging miR-362-3p in RCC cells, suggesting that targeting MALAT1 may be a potential therapeutic strategy for SU-resistance RCC.

Integrated bioinformatics analysis for the identification of potential key genes affecting the pathogenesis of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (CCRCC) is a typical type of RCC with the worst prognosis among the common epithelial neoplasms of the kidney. However, its molecular pathogenesis remains unknown. Therefore, the aim of the present study was to screen for effective and potential pathogenic biomarkers of CCRCC. The gene expression profile of the GSE16441, GSE36895, GSE40435, GSE46699, GSE66270 and GSE71963 datasets were downloaded from the Gene Expression Omnibus database. First, the limma package in R language was used to identify differentially expressed genes (DEGs) in each dataset. The robust and strong DEGs were explored using the robust rank aggregation method. A total of 980 markedly robust DEGs were identified (429 upregulated and 551 downregulated). According to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, these DEGs exhibited an obvious enrichment in various cancer-related biological pathways and functions. The Search Tool for the Retrieval of Interacting Genes/Proteins database was used for the construction of a protein-protein interaction (PPI) network, the Cytoscape MCODE plug-in for module analysis and the cytoHubba plug-in to identify hub genes from the aforementioned DEGs. A total of four key modules were identified in the PPI network. A total of six hub genes, including C-X-C motif chemokine ligand 12, bradykinin receptor B2, adenylate cyclase 7, calcium sensing receptor (CASR), kininogen 1 and lysophosphatidic acid receptor 5, were identified. The DEG results of the hub genes were verified using The Cancer Genome Atlas database, and CASR was found to be significantly associated with the prognosis of patients with CCRCC. In conclusion, the present study provided new insight and potential biomarkers for the diagnosis and prognosis of CCRCC.

Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis

Background

Clear cell renal cell carcinoma (ccRCC) is the most common malignant tumor of kidney with high mortality. The pathogenesis of ccRCC is complicated and effective prognostic predictors for clinical practice are still limited. This study aimed to identify significant genes with prognostic influence in ccRCC via bioinformatics analysis.

Methods

Four gene expression profiles were acquired from the Gene Expression Omnibus (GEO) database, including 168 ccRCC tissues and 143 normal tissues. Common differentially expressed genes (DEGs) between ccRCC tissues and normal kidney tissues were screened out. Then gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were investigated. Protein-protein interaction (PPI) network of the common DEGs was diagrammed and analyzed. Kaplan–Meier analysis was conducted to identify genes with prognostic influence in ccRCC. Gene Expression Profiling Interactive Analysis (GEPIA) was finally applied to validating differential expression of genes.

Results

Ninety-nine common DEGs between ccRCC tissues and normal kidney tissues were eventually screened out (P<0.05, |log FC| >2). GO functional analysis showed that the down-regulated genes were enriched in excretion, negative regulation of cell proliferation, heparin binding and cellular response to BMP stimulus, etc. KEGG pathway analysis indicated that the common DEGs were particularly enriched in HIF-1 signaling pathway and aldosterone-regulated sodium reabsorption. Seven core DEGs were distinguished through PPI network analysis, of which 6 core genes ANGPTL4, CA9, CXCR4, LOX, EGF and HRG showed significantly prognostic difference in patients with ccRCC by Kaplan–Meier analysis (P<0.05). And GEPIA confirmed these genes were expressed differentially between tumor and normal tissues (P<0.05). High expression of HRG was correlated with good OS in ccRCC patients. Specifically, HRG was commonly down-regulated in ccRCC tissues compared with normal tissues according to GEPIA.

Conclusions

Our study shows that high expression of HRG denotes a better prognosis in ccRCC patients. HRG is down-regulated in ccRCC tissues compared with normal kidney tissues. The selective expression pattern suggests that HRG could be a novel prognostic predictor and potential therapeutic target for ccRCC patients.

Identification of Four Pathological Stage-Relevant Genes in Association with Progression and Prognosis in Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis

Clear cell renal cell carcinoma (ccRCC) is a major histological subtype of renal cell carcinoma and can be clinically divided into four stages according to the TNM criteria. Identifying clinical stage-related genes is beneficial for improving the early diagnosis and prognosis of ccRCC. By using bioinformatics analysis, we aim to identify clinical stage-relevant genes that are significantly associated with the development of ccRCC. First, we analyzed the gene expression microarray data sets: GSE53757 and GSE73731. We divided these data into five groups by staging information-normal tissue and ccRCC stages I, II, III, and IV-and eventually identified 500 differentially expressed genes (DEGs). To obtain precise stage-relevant genes, we subsequently applied weighted gene coexpression network analysis (WGCNA) to the GSE73731 dataset and KIRC data from The Cancer Genome Atlas (TCGA). Two modules from each dataset were identified to be related to the tumor TNM stage. Several genes with high inner connection inside the modules were considered hub genes. The intersection results between hub genes of key modules and 500 DEGs revealed UBE2C, BUB1B, RRM2, and TPX2 as highly associated with the stage of ccRCC. In addition, the candidate genes were validated at both the RNA expression level and the protein level. Survival analysis also showed that 4 genes were significantly correlated with overall survival. In conclusion, our study affords a deeper understanding of the molecular mechanisms associated with the development of ccRCC and provides potential biomarkers for early diagnosis and individualized treatment for patients at different stages of ccRCC.

Methylation signature genes identification of cancers occurrence and pattern recognition

In order to identify the signature genes of tumorigenesis, the pattern-recognition method was used to analyze the gene methylation (ME) data which included only normal and cancer samples and was collected from the TCGA (The Cancer Genome Atlas) database. Here, we analyzed the DNA methylation profiles of the six types of cancer and the ME signature genes for each cancer were selected by means of a combination of correlation, student's t-test and Elastic Net. Modeling by support vector machine, the accuracy of ME signature genes can be as high as 98 % for training set and as high as 97 % for the independent test set, the recognition accuracy of stage I is more than 97 % for training set and more than 98 % for test set. Then, the common signature genes and common pathways emerging in multiple cancers were obtained. A functional analysis of these signature genes indicates that the identified signatures have direct relationship with tumorigenesis and is very important for understanding the pathogenesis of cancer and the early therapy.

Identification of molecular markers associated with the progression and prognosis of endometrial cancer: a bioinformatic study

Background

Endometrial cancer (EC) is one kind of women cancers. Bioinformatic technology could screen out relative genes which made targeted therapy becoming conventionalized.

Methods

GSE17025 were downloaded from GEO. The genomic data and clinical data were obtained from TCGA. R software and bioconductor packages were used to identify the DEGs. Clusterprofiler was used for functional analysis. STRING was used to assess PPI information and plug-in MCODE to screen hub modules in Cytoscape. The selected genes were coped with functional analysis. CMap could find EC-related drugs that might have potential effect. Univariate and multivariate Cox proportional hazards regression analyses were performed to predict the risk of each patient. Kaplan–Meier curve analysis could compare the survival time. ROC curve analysis was performed to predict value of the genes. Mutation and survival analysis in TCGA database and UALCAN validation were completed. Immunohistochemistry staining from Human Protein Atlas database. GSEA, ROC curve analysis, Oncomine and qRT-PCR were also performed.

Results

Functional analysis showed that the upregulated DEGs were strikingly enriched in chemokine activity, and the down-regulated DEGs in glycosaminoglycan binding. PPI network suggested that NCAPG was the most relevant protein. CMap identified 10 small molecules as possible drugs to treat EC. Cox analysis showed that BCHE, MAL and ASPM were correlated with EC prognosis. TCGA dataset analysis showed significantly mutated BHCE positively related to EC prognosis. MAL and ASPM were further validated in UALCAN. All the results demonstrated that the two genes might promote EC progression. The profile of ASPM was confirmed by the results from immunohistochemistry. ROC curve demonstrated that the mRNA levels of two genes exhibited difference between normal and tumor tissues, indicating their diagnostic efficiency. qRT-PCR results supported the above results. Oncomine results showed that DNA copy number variation of MAL was significantly higher in different EC subtypes than in healthy tissues. GSEA suggested that the two genes played crucial roles in cell cycle.

Conclusion

BCHE, MAL and ASPM are tumor-related genes and can be used as potential biomarkers in EC treatment.

IRF1 association with tumor immune microenvironment and use as a diagnostic biomarker for colorectal cancer recurrence

Colorectal cancer (CRC) is considered to be one of the most lethal cancer types globally, and its recurrence is a major treatment challenge. Identifying the factors involved when determining the risk of CRC recurrence is required to improve personalized therapy for patients with CRC. Based on the GSE39582 dataset, the present study demonstrated that a higher ratio of M1 macrophages and activated memory CD4⁺ T cells indicated a better recurrence-free survival (RFS) time for CRC, using CIBERSORT and Pearson's correlation analysis. Through weighted correlation network analysis (WGCNA), an immune-associated module was identified that was significantly positively correlated with the ratio of M1 macrophages and activated memory CD4⁺ T cells. In this module, using WGCNA and a protein-protein interaction network, interferon regulatory factor 1 (IRF1), chemokine ligand 5, ubiquitin/ISG15-conjugating enzyme E2 L6, guanylate binding protein 1 and interleukin 2 receptor subunit beta were identified as hub genes. Among these genes, univariate Cox and multivariate Cox analysis revealed that IRF1 may be a potential diagnostic biomarker for RFS in patients with CRC. This was further validated using The Cancer Genome Atlas data. Gene set enrichment analysis demonstrated that IRF1 influenced the genes and pathways that are associated with immune cell recruitment and activation. Additionally, the DNA methylation of cg27587780 and cg15375424 CpG sites in the IRF1 gene region was indicated to be negatively correlated with IRF1 mRNA expression and positively correlated with the recurrence of CRC. Collectively, the results of the present study demonstrated that IRF1 may be a potential diagnostic biomarker for RFS in patients with CRC.

Cell Adhesion-Related Molecules Play a Key Role in Renal Cancer Progression by Multinetwork Analysis

Renal cell carcinoma (RCC) is one of the most common malignancies in the urinary system. The study aimed to identify genetic characteristics and reveal the underlying mechanisms in RCC. GSE53757, GSE46699, and TCGA KIRC database (n = 897) were analyzed to screen differentially expressed genes (DEGs) in RCC. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by the analysis of the protein-protein interaction (PPI) network of the DEGs by Cytoscape software. In all, 834 DEGs were identified in RCC, including 416 upregulated genes and 418 downregulated genes. The top 10 hub genes, VEGFA, EGFR, EGF, CD44, CD86, FN1, ITGAM, ITGB2, TLR2, and PTPRC, were identified from the PPI network according to the core degree. The following subnetwork revealed that these significant modules were enriched in positive regulation of response to external stimulus, regulation of leukocyte-mediated immunity, and regulation of exocytosis. The expressions of these hub genes were also validated using qRT-PCR and IHC in Changzheng RCC database (n = 160). We especially found that half of the top ten hub genes were cell adhesion-related molecules, which were associated with RCC progression and poor prognosis. In conclusion, these hub genes, particularly cell adhesion-related molecules, could be used as prognostic biomarkers and potential therapeutic targets for RCC.

Bioinformatic analysis identifies potentially key differentially expressed genes in oncogenesis and progression of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset GSE53757 was downloaded from the Gene Expression Omnibus database. The GSE53757 dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein–protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.

Identification of RAD54 homolog B as a promising therapeutic target for breast cancer

Breast cancer is a recognized threat to the health of women globally. Due to the lack of the knowledge about the molecular pathogenesis of breast cancer, therapeutic strategies remain inadequate, especially for aggressive breast cancer. In the present study, sequential bioinformatics analysis was performed using data from the GSE20711 dataset, and the results demonstrated that three genes may impact the survival of patients with breast cancer. One of these genes, RAD54 homolog B (RAD54B), may be a potential prognostic factor for breast cancer. A signature was established that could evaluate the overall survival for patients with breast cancer based on the risk score calculated from RAD54B expression and the Tumor-Node-Metastasis (TNM) stage [risk score=expRAD54B × 0.236 + TNM stage (I/II=0 or III/IV=1) ×1.025]. In addition, based on the GSE85871 dataset and inhibitory assay, the study identified a natural compound, Japonicone A, which may reduce the proliferation of breast cancer cells by inhibiting the expression of RAD54B. Overall, the present study identified a novel candidate gene and a candidate compound as promising therapeutic targets for the treatment of breast cancer.

Integrated analysis of co-expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer

Long non‐coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co‐expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan‐Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co‐expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2‐AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.

Novel Biomarkers Associated With Progression and Prognosis of Bladder Cancer Identified by Co-expression Analysis

Our study's goal was to screen novel biomarkers that could accurately predict the progression and prognosis of bladder cancer (BC). Firstly, we used the Gene Expression Omnibus (GEO) dataset GSE37815 to screen differentially expressed genes (DEGs). Secondly, we used the DEGs to construct a co-expression network by weighted gene co-expression network analysis (WGCNA) in GSE71576. We then screened the brown module, which was significantly correlated with the histologic grade (r = 0.85, p = 1e-12) of BC. We conducted functional annotation on all genes of the brown module and found that the genes of the brown module were mainly significantly enriched in "cell cycle" correlation pathways. Next, we screened out two real hub genes (ANLN, HMMR) by combining WGCNA, protein-protein interaction (PPI) network and survival analysis. Finally, we combined the GEO datasets (GSE13507, GSE37815, GSE31684, GSE71576). Oncomine, Human Protein Atlas (HPA), and The Cancer Genome Atlas (TCGA) dataset to confirm the predict value of the real hub genes for BC progression and prognosis. A gene-set enrichment analysis (GSEA) revealed that the real hub genes were mainly enriched in "bladder cancer" and "cell cycle" pathways. A survival analysis showed that they were of great significance in predicting the prognosis of BC. In summary, our study screened and confirmed that two biomarkers could accurately predict the progression and prognosis of BC, which is of great significance for both stratification therapy and the mechanism study of BC.

PPWD1 is associated with the occurrence of postmenopausal osteoporosis as determined by weighted gene co‑expression network analysis

Postmenopausal osteoporosis (PMO) is the most common type of primary osteoporosis (OP), a systemic skeletal disease. Although many factors have been revealed to contribute to the occurrence of PMO, specific biomarkers for the early diagnosis and therapy of PMO are not available. In the present study, a weighted gene co-expression network analysis (WGCNA) was performed to screen gene modules associated with menopausal status. The turquoise module was verified as the clinically significant module, and 12 genes (NUP133, PSMD12, PPWD1, RBM8A, CRNKL1, PPP2R5C, RBM22, PIK3CB, SKIV2L2, PAPOLA, SRSF1 and COPS2) were identified as ‘real’ hub genes in both the protein-protein interaction (PPI) network and co-expression network. Furthermore, gene expression analysis by microarray in blood monocytes from pre- and post-menopausal women revealed an increase in the expression of these hub genes in postmenopausal women. However, only the expression of peptidylprolyl isomerase domain and WD repeat containing 1 (PPWD1) was correlated with bone mineral density (BMD) in postmenopausal women. In the validation set, a similar expression pattern of PPWD1 was revealed. Functional enrichment analysis revealed that the fatty acid metabolism pathway was significantly abundant in the samples that exhibited a higher expression of PPWD1. Collectively, PPWD1 is indicated as a potential diagnostic biomarker for the occurrence of PMO.

Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy

Background

Esophageal squamous cell carcinoma (ESCC) is one of leading malignant cancers of gastrointestinal tract worldwide. Until now, the involved mechanisms during the development of ESCC are largely unknown. This study aims to explore the driven-genes and biological pathways in ESCC.

Methods

mRNA expression datasets of GSE29001, GSE20347, GSE100942, and GSE38129, containing 63 pairs of ESCC and non-tumor tissues data, were integrated and deeply analyzed. The bioinformatics approaches include identification of differentially expressed genes (DEGs) and hub genes, gene ontology (GO) terms analysis and biological pathway enrichment analysis, construction and analysis of protein-protein interaction (PPI) network, and miRNA-gene network construction. Subsequently, GEPIA2 database and qPCR assay were utilized to validate the expression of hub genes. DGIdb database was performed to search the candidate drugs for ESCC.

Results

Finally, 120 upregulated and 26 downregulated DEGs were identified. The functional enrichment of DEGs in ESCC were mainly correlated with cell cycle, DNA replication, deleted in colorectal cancer (DCC) mediated attractive signaling pathway, and Netrin-1 signaling pathway. The PPI network was constructed using STRING software with 146 nodes and 2392 edges. The most significant three modules in PPI were filtered and analyzed. Totally ten genes were selected and considered as the hub genes and nuclear division cycle 80 (NDC80) was closely related to the survival of ESCC patients. DGIdb database predicted 33 small molecules as the possible drugs for treating ESCC.

Conclusions

In summary, the data may provide new insights into ESCC pathogenesis and treatments. The candidate drugs may improve the efficiency of personalized therapy in future.

Eleven genes associated with progression and prognosis of endometrial cancer (EC) identified by comprehensive bioinformatics analysis

Background

Endometrial cancer (EC) is one of the female malignant tumors. Endometrial cancer predominately affects post-menopausal women. Bioinformatics analysis has been widely applied to screen and analyze genes in linkage to various types of cancer progression.

Methods

Download the gene expression profile from Gene Expression Omnibus (GEO). Calculate raw expression data according to pre-processing procedures. We performed the “limma” R language package to screen DEGs between Endometrial cancer tissue samples and normal uterus tissue samples. Enrichment of the functions and pathways was analyzed by using clusterprofiler. We utilized Search Tool for the Retrieval of Interacting Genes Database (STRING) to assess protein–protein interaction (PPI) information, and then we used plug-in Molecular Complex Detection (MCODE) to screen hub modules of PPI network in Cytoscape. We also performed functional analysis on the genes in the hub module by using clusterprofiler. Next, we utilized the “WGCNA” package in R to establish co-expression network for the DEGs. The Venn diagram was performed to overlap the gene in key module and hub PPI cluster. We validated the key genes in TCGA, GEPIA, UALCAN and Immunohistochemistry staining obtained from The Human Protein Atlas database. And then we did ROC curve analysis by SPSS. Gene set enrichment analysis (GSEA) and mutation analysis were also performed for hub genes.

Results

Functional and pathway enrichment analysis demonstrated that the upregulated differentially expressed genes (DEGs) were significantly enriched in CXCR chemokine receptor binding, chemokine activity, chemokine receptor binding, G-protein coupled receptor binding, RAGE receptor binding, cytokine activity, microtubule binding, receptor regulator activity and microtubule motor activity, and the down-regulated genes were highly enriched in collagen binding. After using STRING software to construct PPI network, 30 prominent proteins were identified and the first two significant modules were selected. In co-expression network, 5 EC-related modules were identified. Among them, the turquoise module has the highest correlation with the EC. We further analyzed the genes in the PPI and turquoise module, and selected eleven key genes related to EC after validation of TCGA database, GEPIA, UALCAN and immunohistochemistry. Six of them had mutation significance.

Conclusions

In summary, these 11 genes may become new therapy targets for EC treatment.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0859-1) contains supplementary material, which is available to authorized users.

Radiogenomics of Clear Cell Renal Cell Carcinoma: Associations Between mRNA-Based Subtyping and CT Imaging Features

PURPOSE

To investigate associations between clear-cell renal cell carcinoma mRNA-based subtyping and CT features.

MATERIALS AND METHODS

The CT data from 177 patients generated with The Cancer Imaging Archive were reviewed. The correlation was analyzed using chi-square test and univariate regression analysis.

RESULTS

Identified were 124 (53.2%) m1, 67 (28.8%) m2, 17 (7.3%) m3, and 14 (8.7%) m4 subtypes. m1-subtype rates were significantly higher in well-defined margin lesions (p = 0.041). m3-subtype rates were significantly higher in ill-defined margin lesions (p = 0.012), in collecting system invasion lesions (p = 0.028) and collecting system invasion lesions (p = 0.026).On univariate logistic regression analysis, tumor margin (well-defined margin vs ill-defined margin, OR: 2.104; p = 0.041; 95% CI: 1.024-4.322) was associated with m1-subtype. Tumor margin (well-defined margin vs ill-defined margin, OR: 2.104; p = 0.012; 95% CI: 0.212-0.834) and collecting system invasion (yes vs no, OR: 0.421; p = 0.028; 95% CI: 0.212-0.834) and renal vein invasion (yes vs no, OR: 2.164; p = 0.026; 95% CI: 1.090-4.294) were associated with m3-subtype. There was no significant difference between mRNA-based subtyping (m2 vs other; m4 vs other) and the CT features.

CONCLUSIONS

This preliminary radiogenomics analysis of clear-cell renal cell carcinoma revealed associations between CT features and mRNA-based subtyping which warrant further investigation and validation.

ITLNI identified by comprehensive bioinformatic analysis as a hub candidate biological target in human epithelial ovarian cancer

Background

Epithelial ovarian cancer (EOC) is a female malignant tumor. Bioinformatics has been widely utilized to analyze genes related to cancer progression. Targeted therapy for specific biological factors has become more valuable.

Materials and methods

Gene expression profiles of GSE18520 and GSE27651 were downloaded from Gene Expression Omnibus. We used the “limma” package to screen differentially expressed genes (DEGs) between EOC and normal ovarian tissue samples and then used Clusterprofiler to do functional and pathway enrichment analyses. We utilized Search Tool for the Retrieval of Interacting Genes Database to assess protein–protein interaction (PPI) information and the plug-in Molecular Complex Detection to screen hub modules of PPI network in Cytoscape, and then performed functional analysis on the genes in the hub module. Next, we utilized the Weighted Gene Expression Network Analysis package to establish a co-expression network. Validation of the key genes in databases and Gene Expression Profiling Interactive Analysis (GEPIA) were completed. Finally, we used quantitative real-time PCR to validate hub gene expression in clinical tissue samples.

Results

We analyzed the DEGs (96 samples of EOC tissue and 16 samples of normal ovarian tissue) for functional analysis, which showed that upregulated DEGs were strikingly enriched in phosphate ion binding and the downregulated DEGs were significantly enriched in glycosaminoglycan binding. In the PPI network, CDK1 was screened as the most relevant protein. In the co-expression network, one EOC-related module was identified. For survival analysis, database and clinical sample validation of genes in the turquoise module, we found that ITLN1 was positively correlated with EOC prognosis and had lower level in EOC than in normal tissues, which was consistent with the results predicted in GEPIA.

Conclusion

In this study, we exhibited the key genes and pathways involved in EOC and speculated that ITLN1 was a tumor suppressor which could be used as a potential biomarker for treating EOC, Gene Expression Omnibus, prognosis.

Coexpression network analysis identified Krüppel-like factor 6 (KLF6) association with chemosensitivity in ovarian cancer

Although most patients with ovarian cancer (OC) are initially sensitive to paclitaxel/carboplatin combination chemotherapy, eventually they develop resistance to chemotherapy drugs and experience disease relapse. OC is the most lethal gynecological malignancy, and the five-year survival rate is extremely low. Thus, research on specific biomarkers and potential targets for chemotherapy-resistant patients with OC is needed. In our study, genes in the top 10% of variance in data set GSE30161 from chemoresistant and chemosensitive OC tissues were determined to conduct a weighted gene coexpression network analysis (WGCNA). The magenta module was most strongly related to OC chemoresponse. Gene ontology enrichment analysis indicated that the function of the magenta module primarily focused on transcription regulation, cell cycle control, and apoptosis modulation. Integration of the WGCN with the protein-protein interaction network identified five candidate genes. These five genes were verified using the GSE51373 test set, and Krüppel-like factor 6 ( KLF6) was identified as tightly linked to OC chemosensitivity. The receiver operating characteristic (ROC) curve showed that KLF6 differentiated chemoresistant from chemosensitive OC tissues. The Kaplan-Meier online database indicated that high KLF6 expression was associated with poor OC prognosis. Gene set enrichment analysis determined that the KLF6 mechanism was potentially associated with cell cycle, mTOR, and DNA-damage repair signaling pathways. In conclusion, KLF6 was identified in association with OC chemoresistance, and the mechanism of KLF6-mediated chemoresistance may involve the cell cycle, mTOR, and DNA-damage repair signaling pathways.

Identification of biomarkers of chromophobe renal cell carcinoma by weighted gene co-expression network analysis

Background

Chromophobe renal cell carcinoma (ChRCC) is the second common subtype of non-clear cell renal cell carcinoma (nccRCC), which accounting for 4–5% of renal cell carcinoma (RCC). However, there is no effective bio-marker to predict clinical outcomes of this malignant disease. Bioinformatic methods may provide a feasible potential to solve this problem.

Methods

In this study, differentially expressed genes (DEGs) of ChRCC samples on The Cancer Genome Atlas database were filtered out to construct co-expression modules by weighted gene co-expression network analysis and the key module were identified by calculating module-trait correlations. Functional analysis was performed on the key module and candidate hub genes were screened out by co-expression and MCODE analysis. Afterwards, real hub genes were filter out in an independent dataset GSE15641 and validated by survival analysis.

Results

Overall 2215 DEGs were screened out to construct eight co-expression modules. Brown module was identified as the key module for the highest correlations with pathologic stage, neoplasm status and survival status. 29 candidate hub genes were identified. GO and KEGG analysis demonstrated most candidate genes were enriched in mitotic cell cycle. Three real hub genes (SKA1, ERCC6L, GTSE-1) were selected out after mapping candidate genes to GSE15641 and two of them (SKA1, ERCC6L) were significantly related to overall survivals of ChRCC patients.

Conclusions

In summary, our findings identified molecular markers correlated with progression and prognosis of ChRCC, which might provide new implications for improving risk evaluation, therapeutic intervention, and prognosis prediction in ChRCC patients.

Electronic supplementary material

The online version of this article (10.1186/s12935-018-0703-z) contains supplementary material, which is available to authorized users.