Identification of potential crucial genes associated with carcinogenesis of clear cell renal cell carcinoma

Trichinella spiralis cathepsin B bound and degraded host's intestinal type I collagen

Trichinellosis is a zoonotic parasitic disease that poses threats to human health, the meat industry, food safety, and huge financial losses. The critical stage of Trichinella spiralis (T. spiralis) infection is the invasion of intestinal larvae into the host's intestinal epithelial cells (IECs). T. spiralis Cathepsin B (TsCB) specifically interacts with IECs to facilitate the invasion of larvae. This study aims to look at how TsCB affects mouse IECs. TsCB was successfully cloned, expressed, and characterized, demonstrating its natural cysteine protease hydrolysis activity. A total of 140 proteins that interact with rTsCB were identified by GST pull-down combined with LC-MS/MS, including type I collagen, an essential component of the host's intestinal epithelial barrier system and intimately related to intestinal epithelial damage. TsCB transcription and expression levels rise, whereas type I collagen in the host's intestinal mucosa declines when the T. spiralis larvae invaded. Besides, it was discovered that TsCB bound to and degraded type I collagen of the host's intestine. This research can serve as a foundation for clarifying how T. spiralis invades the host's intestinal barrier and might provide information on potential targets for the creation of novel treatments to treat parasite illnesses.

Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets

INTRODUCTION

Clear cell renal cell carcinoma (ccRCC) is recognized as one of the leading causes of illness and death worldwide. Understanding the molecular mechanisms in ccRCC pathogenesis is crucial for discovering novel therapeutic targets and developing efficient drugs. With the application of a comprehensive in silico analysis of the ccRCC-related array sets, the main objective of this study was to discover the top molecules and pathways in the pathogenesis of this cancer.

METHODS

ccRCC microarray datasets were downloaded from the Gene Expression Omnibus database, and after quality checking, normalization, and analysis using the Limma algorithm, differentially expressed genes (DEGs) were identified, considering the adjusted p value <0.049. The intensity values of the identified DEGs were introduced to the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to construct co-expression modules. Functional enrichment analyses were performed using the DEGs in the disease-correlated module, and hub genes were identified among the top genes in a protein-protein interaction network and the disease most correlated module. The expression analysis of hub genes was done by utilizing GEPIA, and the GSCA server was used to compare the expression patterns of hub genes in ccRCC and other cancers. DGIdb database was utilized to identify the hub gene-related drugs.

RESULTS

Three datasets, including GSE11151, GSE12606, and GSE36897, were retrieved, merged, normalized, and analyzed. Using WGCNA, the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC-correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were upregulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs, including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules, were identified to interact with the hub genes.

CONCLUSION

By utilizing an integrative bioinformatics approach, this experiment shed light on the underlying pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer. Our next step would be to assess the gene expression profiles of the identified hubs in different cell populations in the tumor microenvironment.

An epithelial-mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis

OBJECTIVE

To identify susceptibility modules and genes for colorectal cancer (CRC) using weighted gene co-expression network analysis (WGCNA).

METHODS

Four microarray datasets were downloaded from the Gene Expression Omnibus database. We divided the tumor samples into three subgroups based on consensus clustering of gene expression, and analyzed the correlations between the subgroups and clinical features. The genetic features of the subgroups were investigated by gene set enrichment analysis (GSEA). A gene expression network was constructed using WGCNA, and a protein-protein interaction (PPI) network was used to identify the key genes. Gene modules were annotated by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses.

RESULTS

We divided the cancer cases into three subgroups based on consensus clustering (subgroups I, II, III). The green module identified by WGCNA was correlated with clinical characteristics. Ten key genes were identified according to their degree of connectivity in the protein-protein interaction network: FYN, SEMA3A, AP2M1, L1CAM, NRP1, TLN1, VWF, ITGB3, ILK, and ACTN1.

CONCLUSION

We identified 10 hub genes as candidate biomarkers for CRC. These key genes may provide a theoretical basis for targeted therapy against CRC.

Characterization of the basement membrane in kidney renal clear cell carcinoma to guide clinical therapy

Background

Renal cell carcinoma (RCC) is the most common kidney cancer in adults. According to the histological features, it could be divided into several subtypes, of which the most common one is kidney renal clear cell carcinoma (KIRC), which contributed to more than 90% of cases for RCC and usually ends with a dismal outcome. Previous studies suggested that basement membrane genes (BMGs) play a pivotal role in tumor development. However, the significance and prognostic value of BMGs in KIRC still wrap in the mist.

Methods

KIRC data were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. A prognostic risk score (PRS) model based on BMGs was established using univariate and least absolute shrinkage and selection operator (LASSO) and the Cox regression analysis was performed for prognostic prediction. The Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, receiver operating characteristic (ROC) curves, nomogram, and calibration curves were utilized to evaluate and validate the PRS model. All KIRC cases were divided into the high-risk score (HRS) group and the low-risk score (LRS) group according to the median risk scores. In addition, single-sample gene set enrichment analysis (ssGSEA), immune analysis, tumor microenvironment (TME) analysis, principal component analysis (PCA), and half-maximal inhibitory concentration (IC50) were also applied. Expression levels of BMGs were confirmed by qRT-PCR in both human renal cancer cell lines and tissues.

Results

We established the BMGs-based prognostic model according to the following steps. Within the TCGA cohort, patients’ prognosis of the HRS group was significantly worse than that of the LRS group, which was consistent with the analysis results of the GEO cohort. PCA patterns were significantly distinct for LRS and HRS groups and pathological features of the HRS group were more malignant compared with the LRS group. Correlation analysis of the PRS model and TME features, such as immune cell scores, stromal cell scores, and ESTIMATE values, revealed a higher immune infiltration in the HRS group compared with the LRS group. The chemotherapeutic response was also evaluated in KIRC treatment. It showed that the HRS group exhibited stronger chemoresistance to chemotherapeutics like FR-180204, GSK1904529A, KIN001-102, and YM201636. The therapeutic reactivity of the other 27 chemotherapeutic agents was summarized as well. Furthermore, the FREM2 level was measured in both human kidney tissues and associated cell lines, which suggested that lower FREM2 expression prompts a severer pathology and clinical ending.

Conclusions

Our study showed that KIRC is associated with a unique BMG expression pattern. The risk scores related to the expression levels of 10 BMGs were assessed by survival status, TME, pathological features, and chemotherapeutic resistance. All results suggested that FREM2 could be a potential candidate for KIRC prognosis prediction. In this study, we established a valid model and presented new therapeutic targets for the KIRC prognosis prediction as well as the clinical treatment recommendation, and finally, facilitated precision tumor therapy for every single individual.

Identification of a Five-miRNA Signature for Diagnosis of Kidney Renal Clear Cell Carcinoma

Motivation: Kidney renal clear cell carcinoma, which is a common type and accounts for 70-80% of renal cell carcinoma, can easily lead to metastasis and even death. A reliable signature for diagnosis of this cancer is in need. Hence, we seek to select miRNAs for identifying kidney renal clear cell carcinoma. Method: A feature selection strategy is used and improved to identify microRNAs for diagnosis of kidney renal clear cell carcinoma. Samples representing kidney renal clear cell carcinoma and normal tissues are split into training and testing groups. Accumulated scores representing the variable importance of each miRNA are derived from an iteration of resampling, training, and scoring. Those miRNAs with higher scores are selected based on the Gaussian mixture model. The sample split is repeated ten times to get more central miRNAs. Results: A total of 611 samples are downloaded from TCGA, each of which contains 1,343 miRNAs. The improved feature selection method is implemented, and five miRNAs are identified as a biomarker for diagnosis of kidney renal clear cell carcinoma. GSE151419 and GSE151423 are selected as the independent testing sets. Experimental results indicate the effectiveness of the selected signature. Both data-driven measurements and knowledge-driven evidence are given to show the effectiveness of our selection results.

Identification of key candidate genes and biological pathways in the synovial tissue of patients with rheumatoid arthritis

The aim of the present study was to identify potential key candidate genes and mechanisms associated with rheumatoid arthritis (RA). Gene expression data from GSE55235, GSE55457 and GSE1919 datasets were downloaded from the Gene Expression Omnibus database. These datasets comprised 78 tissue samples collectively, including 25 healthy synovial membrane samples and 28 RA synovial membrane samples, whilst the 25 osteoarthritis (OA) samples were not included in the analysis. The differentially expressed genes (DEGs) between the two types of samples were identified with the Linear Models for Microarray Analysis package in R. Gene Ontology (GO) functional term and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analyses were also performed. In addition, Protein-Protein Interaction (PPI) network and module analyses were visualized using Cytoscape, and subsequent hub gene identification as well as GO and KEGG enrichment analyses of the modules was performed. Finally, reverse transcription-quantitative PCR (RT-qPCR) was used to validate the expression of the DEGs identified by GO and KEGG analysis in vitro. The analysis identified 491 DEGs, including 289 upregulated and 202 downregulated genes, which were mainly enriched in the following pathways: ‘Cytokine-cytokine receptor interaction’, ‘Rheumatoid arthritis’, ‘Chemokine signaling pathway’, ‘Intestinal immune network for IgA production’ and ‘Primary immunodeficiency’. The top 10 hub genes identified from the PPI network were IL-6, protein tyrosine phosphatase receptor type C, VEGFA, CD86, EGFR, C-X-C chemokine receptor type 4, matrix metalloproteinase 9, CC-chemokine receptor type (CCR)7, CCR5 and selectin L. KEGG signaling pathway enrichment analysis of the top two modules identified from the PPI network revealed that the genes in Module 1 were mainly enriched in the ‘Cytokine-cytokine receptor interaction’ and ‘Chemokine signaling pathway’, whereas analysis of Module 2 revealed that the genes were mainly enriched in ‘Primary immunodeficiency’ and ‘Cytokine-cytokine receptor interaction’. Finally, the results of the RT-qPCR and western blot analysis demonstrated that the expression levels of inflammation and NF-κB signaling pathway-related mRNAs were significantly upregulated following lipopolysaccharide stimulation. In conclusion, the findings of the present study identified key genes and signaling pathways associated with RA, which may improve the current understanding of the molecular mechanisms underlying its development and progression. The identified hub genes may also be used as potential targets for RA diagnosis and treatment.

Bioinformatics Analysis and Identification of Potential Genes Associated with Pathogenesis and Prognosis of Gastric Cancer

OBJECTIVE

Gastric cancer (GC) is a deadly cancer and a challenging public health problem globally. This study aimed to analyze potential genes associated with pathogenesis and prognosis of gastric cancer.

METHODS

This work selected the overlapping differentially expressed genes (DEGs) in GC from four datasets, the GSE29272, GSE29998, GSE54129 and GSE118916 Gene Expression Omnibus databases. These DEGs were used to carry out comprehensive bioinformatic analysis to analyze the related functions and pathways enriched, the relative expression levels and immune infiltrates, the prognostic characteristics and the interaction network.

RESULTS

In total, 55 DEGs increased while 98 decreased in their expression levels. For those DEGs with increased expression, they were mostly concentrated on "focal adhesion" and "ECM-receptor interaction", whereas DEGs with decreased expression were mostly associated with "gastric acid secretion" and "drug metabolism cytochrome P450". MCODE and ClueGO results were then integrated to screen 10 hub genes, which were FN1, COL1A1, COL3A1, BGN, TIMP1, COL1A2, LUM, VCAN, COL5A2 and SPP1. Survival analysis revealed that higher expression of the ten hub genes significantly predicted lower overall survival of GC patients. TIMP1 was most significantly related to neutrophils, CD8⁺ T cells, as well as dendritic cells, while LUM was most significantly related to macrophages.

CONCLUSION

Immunohistochemistry results and functional testing showed that the expression of COL5A2 was elevated in GC and that it might be a key gene in GC tumorigenesis.

Knockdown of CD44 inhibits proliferation, migration and invasion of osteosarcoma cells accompanied by downregulation of cathepsin S

BACKGROUND

Osteosarcoma (OS) is a malignant bone tumour of mesenchymal origin. These tumours are characterised by rich vascularisation, therefore promoting rapid proliferation and facilitating metastasis. CD44 has been reported to be involved in OS, but its role and molecular mechanisms in the pathogenesis of the disease are not fully determined.

METHODS

In this study, we investigated the antitumor effect of CD44 on the development of OS and further explored the molecular mechanisms. The expression of CD44, cathepsin S and MMP-9 was detected by Western blot (WB) and reverse transcription-polymerase chain reaction (RT-qPCR) in different cell lines (MG63, U2OS OS and hFOB 1.19). To elucidate the role of CD44 in OS, MG63 and U2OS cells were treated with small interference RNA (siRNA) to knock down CD44, and the knockdown efficiency was validated with GFP and RT-qPCR. Furthermore, cell proliferation was assayed using Cell Counting Kit‑8 (CCK-8) and colony formation assays, and cell migration and invasion were assayed by transwell and wound-healing assays.

RESULTS

We found that CD44 expression in the MG63 and U2OS OS cell lines was markedly increased compared to that of the human osteoblast hFOB 1.19 cell line. Knockdown of CD44 inhibited proliferation, migration and invasion of MG63 and U2OS cells. Cathepsin S expression in the MG63 and U2OS OS cell lines was increased compared to that of the human osteoblast hFOB 1.19 cell line. When CD44 was knocked down, its expression level went down.

CONCLUSION

Taken together, our data reinforced the evidence that CD44 knockdown inhibited cell proliferation, migration and invasion of OS cells accompanied by altered expression of cathepsin S. These findings offer new clues for OS development and progression, suggesting CD44 as a potential therapeutic target for OS.

HIF-1α Induces HECTD2 Up-Regulation and Aggravates the Malignant Progression of Renal Cell Cancer via Repressing miR-320a

Renal cell carcinoma (RCC) is a frequent malignancy of the urinary system. It has been found that hypoxia mediates the malignant evolvement of RCC. Here, we probe the impact and potential mechanism of HECT domain E3 ubiquitin-protein ligase 2 (HECTD2) and HIF-1α on regulating RCC evolvement. RCC tissues and adjacent normal tissues were collected, and the association between the expression profiles of HECTD2 and HIF-1α and the clinicopathological features was analyzed. Additionally, we constructed HECTD2/HIF-1α overexpression and knockdown models in RCC cell lines to ascertain the impacts of HECTD2 and HIF-1α on RCC cell proliferation, apoptosis, migration, and growth in vivo. We applied bioinformatics to predict the upstream miRNA targets of HECTD2. Meanwhile, RNA immunoprecipitation (RIP), and the dual-luciferase reporter assays were employed to clarify the targeting association between HECTD2 and miR-320a. The effect of miR-320a on HECTD2-mediated RCC progression was investigated. The results suggested that both HIF-1α and HECTD2 were up-regulated in RCC (compared with adjacent non-tumor tissues), and they had positive relationship. Moreover, higher level of HECTD2 and HIF-1α is associated with poorer overall survival of RCC patients. HECTD2 overexpression heightened RCC cell proliferation and migration, and weakened cell apoptosis. On the other hand, the malignant phenotypes of RCC cells were signally impeded by HECTD2 or HIF-1α knockdown. Moreover, miR-320a targeted the 3'-untranslated region of HECTD2 and suppressed HECTD2 expression. The rescue experiments showed that miR-320a restrained HECTD2-mediated malignant progression in RCC, while up-regulation of HIF-1α hampered miR-320a expression. Collectively, HIF-1α mediated HECTD2 up-regulation and aggravated RCC progression by attenuating miR-320a.

Bioinformatics Analysis of Prognostic Value of SPC24 in ccRCC and Pan-Cancer

Purpose

Clear cell renal cell carcinoma (ccRCC) is one of the most common diseases in the world, with high morbidity and mortality. Recent studies have revealed the important role of SPC24, a subunit of the Ndc80 complex, in the occurrence and development of carcinoma. However, the latent effect of SPC24 in the progress of ccRCC remains to be further explored. The intent of this research is to investigate whether SPC24 can be used as an index to predict the progression of ccRCC and to explore its relationship with the immune microenvironment and pan-cancer.

Materials and Methods

Based on data from public databases, we determined the expression level and clinical value of SPC24 in ccRCC and human pan-cancer. RT-qPCR analysis was carried out to detect the expression level of SPC24 in the OSRC/786O (human ccRCC cells) cell lines and HK2 (human normal kidney cells) cell line. The signal transduction pathways activated by different levels of SPC24 expression were explored by Gene Set Enrichment Analysis (GSEA), and the CIBERSORT algorithm was applied to analyze the relationship between infiltrating immune cells and SPC24 expression in ccRCC and pan-cancer tissues.

Results

SPC24 is overexpressed in ccRCC and several types of tumors, which is associated with poor prognosis. GSEA and CIBERSORT algorithms suggested that the high expression group of SPC24 enriched various pathways including immune-related pathways, and the several infiltrated immunocytes were related to the expression of SPC24.

Conclusion

Our study revealed that SPC24 is a prognostic factor in ccRCC related to immunomodulation and has generalized value in pan-cancer.

[Therapeutic mechanism of natural astaxanthin against renal clear cell carcinoma based on network pharmacology and bioinformatics]

OBJECTIVE

To explore the molecular mechanism by which natural astaxanthin (AST) inhibits renal clear cell carcinoma (KIRC) based on network pharmacology and bioinformatics.

METHODS

PharmMapper database was used to retrieve the targets of natural astaxanthin, and TCGA database was used to identify the differentially expressed genes (DEGs) in KIRC and adjacent tissues. The target genes of AST was analyzed using Cytoscape software to construct the "drug-target" network diagram. The visual protein-protein interaction (PPI) network was constructed using String database, and GO enrichment analysis of the core targets was performed. Single gene bioinformatics was performed to verify the screened core target of AST, namely placental growth factor (PGF). The effect of natural AST on the viability of KIRC cells was tested using CCK-8 method, and the binding between natural AST and PGF was assessed with molecular docking technology. The effect of natural AST on the mRNA and protein expression of the target genes was analyzed using RT-qPCR and Western blotting.

RESULTS

We identified 278 candidate targets of AST, 1081 KIRC-related targets, and 7 core targets involved in the therapeutic mechanism of AST against KIRC. Among these 7 core targets, PGF showed significantly upregulated expression in KIRC (P < 0.001) in correlation with a poor prognosis (HR=1.37, P=0.043). Molecular docking showed that the binding energy of AST and PGF was -5.43 kcal/mol. CCK-8 assay showed that AST at the concentration of 50 μmol/L was capable of inhibiting the proliferation of KIRC cells, and a higher concentration resulted in a stronger inhibitory effect. The results of RT-qPCR and Western blotting showed that AST treatment significantly reduced the expression of PGF at both the mRNA and protein levels in KIRC cells.

CONCLUSION

Natural AST can suppress the proliferation of KIRC and inhibit the expression of PGF in the cells.

A five-gene methylation signature predicts overall survival of patients with clear cell renal cell carcinoma

BACKGROUND

In this study, we aimed to screen methylation signatures associated with the prognosis of patients with clear cell renal cell carcinoma (ccRCC).

METHODS

Gene expression and methylation profiles of ccRCC patients were downloaded from publicly available databases, and differentially expressed genes (DEGs)-differentially methylated genes (DMGs) were obtained. Subsequently, gene set enrichment and transcription factor (TF) regulatory network analyses were performed. In addition, a prognostic model was constructed and the relationship between disease progression and immunity was analyzed.

RESULTS

A total of 23 common DEGs-DMGs were analyzed, among which 14 DEGs-DMGs were obtained with a cutoff value of PCC < 0 and p < 0.05. The enrichment analysis showed that the 14 DEGs-DMGs were enriched in three GO terms and three KEGG pathways. In addition, a total of six TFs were shown to be associated with the 14 DEGs-DMGs, including RP58, SOX9, NF-κB65, ATF6, OCT, and IK2. A prognostic model using five optimized DEGs-DMGs which efficiently predicted survival was constructed and validated using the GSE105288 dataset. Additionally, four types of immune cells (NK cells, macrophages, neutrophils, and cancer-associated fibroblasts), as well as ESTIMATE, immune, and stromal scores were found to be significantly correlated with ccRCC progression (normal, primary, and metastasis) in addition to the five optimized DEGs-DMGs.

CONCLUSION

A five-gene methylation signature with the predictive ability for ccRCC prognosis was investigated in this study, consisting of CCNB2, CDKN1C, CTSH, E2F2, and ERMP1. In addition, potential targets for methylation-mediated immunotherapy were highlighted.

Identification of differential proteomics in Epstein-Barr virus-associated gastric cancer and related functional analysis

BACKGROUND

Epstein-Barr virus-associated gastric cancer (EBVaGC) is the most common EBV-related malignancy. A comprehensive research for the protein expression patterns in EBVaGC established by high-throughput assay remains lacking. In the present study, the protein profile in EBVaGC tissue was explored and related functional analysis was performed.

METHODS

Epstein-Barr virus-encoded RNA (EBER) in situ hybridization (ISH) was applied to EBV detection in GC cases. Data-independent acquisition (DIA) mass spectrometry (MS) was performed for proteomics assay of EBVaGC. Functional analysis of identified proteins was conducted with bioinformatics methods. Immunohistochemistry (IHC) staining was employed to detect protein expression in tissue.

RESULTS

The proteomics study for EBVaGC was conducted with 7 pairs of GC cases. A total of 137 differentially expressed proteins in EBV-positive GC group were identified compared with EBV-negative GC group. A PPI network was constructed for all of them, and several proteins with relatively high interaction degrees could be the hub genes in EBVaGC. Gene enrichment analysis showed they might be involved in the biological pathways related to energy and biochemical metabolism. Combined with GEO datasets, a highly associated protein (GBP5) with EBVaGC was screened out and validated with IHC staining. Further analyses demonstrated that GBP5 protein might be associated with clinicopathological parameters and EBV infection in GC.

CONCLUSIONS

The newly identified proteins with significant differences and potential central roles could be applied as diagnostic markers of EBVaGC. Our study would provide research clues for EBVaGC pathogenesis as well as novel targets for the molecular-targeted therapy of EBVaGC.

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.

Four Novel Prognostic Genes Related to Prostate Cancer Identified Using Co-expression Structure Network Analysis

Prostate cancer (PCa) is one of the most common malignancies for males, but very little is known about its pathogenesis. This study aimed to identify novel biomarkers associated with PCa prognosis and elucidate the underlying molecular mechanism. First, The Cancer Genome Atlas (TCGA) RNA-sequencing data were utilized to identify differentially expressed genes (DEGs) between tumor and normal samples. The DEGs were then applied to construct a co-expression and mined using structure network analysis. The magenta module that was highly related to the Gleason score (r = 0.46, p = 3e-26) and tumor stage (r = 0.38, p = 2e-17) was screened. Subsequently, all genes of the magenta module underwent function annotation. From the key module, CCNA2, CKAP2L, NCAPG, and NUSAP1 were chosen as the four candidate genes. Finally, internal (TCGA) and external data sets (GSE32571, GSE70770, and GSE141551) were combined to validate and predict the value of real hub genes. The results show that the above genes are up-regulated in PCa samples, and higher expression levels show significant association with higher Gleason scores and tumor T stage. Moreover, receiver operating characteristic curve and survival analysis validate the excellent value of hub genes in PCa progression and prognosis. In addition, the protein levels of these four genes also remain higher in tumor tissues when compared with normal tissues. Gene set enrichment analysis and gene set variation analysis for a single gene reveal the close relation with cell proliferation. Meanwhile, 11 small molecular drugs that have the potential to treat PCa were also screened. In conclusion, our research identified four potential prognostic genes and several candidate molecular drugs for treating PCa.

Targeting a positive regulatory loop in the tumor-macrophage interaction impairs the progression of clear cell renal cell carcinoma

Although the interaction between tumors and tumor-associated macrophages (TAMs) has been reported to facilitate the targeted drug resistance and progression of clear cell renal cell carcinoma (ccRCC), the related mechanisms remain unknown. Here, we report that SOX17 serves as a novel tumor suppressor in ccRCC and a positive regulatory loop, SOX17low/YAP/TEAD1/CCL5/CCR5/STAT3, facilitates the ccRCC-TAM interaction. SOX17 expression was commonly downregulated and negatively correlated with TAM infiltration in ccRCC specimens, and the integration of SOX17 and TAMs with the existing clinical indicators TNM stage or SSIGN score achieved better accuracy for predicting the prognosis of ccRCC patients. Mechanistically, SOX17 knockdown activated YAP signaling by promoting the transcription and nuclear distribution of YAP, which recruited TEAD1 to trigger CCL5 transcription. Then, CCL5 educated macrophages toward TAMs, which reciprocally enhanced ccRCC progression through CCL5/CCR5 and activated STAT3/SOX17low/YAP. However, SOX17 overexpression in ccRCC achieved the opposite effect. Thus, a positive regulatory loop, SOX17low/YAP/TEAD1/CCL5/CCR5/STAT3, was identified in the ccRCC-TAM interaction. Furthermore, targeting tumor-TAM interactions by blocking this positive regulatory network impaired the metastasis and targeted drug resistance of ccRCC in in vivo mouse models of lung metastasis and orthotopic ccRCC. These findings provide a new mechanism underlying the tumor-TAM interplay in ccRCC progression and present a potential target for inhibiting targeted drug resistance and metastasis in advanced ccRCC.

Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis. Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively. Results: We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines. Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

Integrative bioinformatics approaches for identifying potential biomarkers and pathways involved in non-obstructive azoospermia

Background

Non-obstructive azoospermia (NOA) is a disease related to spermatogenic disorders. Currently, the specific etiological mechanism of NOA is unclear. This study aimed to use integrated bioinformatics to screen biomarkers and pathways involved in NOA and reveal their potential molecular mechanisms.

Methods

GSE145467 and GSE108886 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NOA tissues and matched obstructive azoospermia (OA) tissues were identified using the GEO2R tool. Common DEGs in the two datasets were screened out by the VennDiagram package. For the functional annotation of common DEGs, DAVID v.6.8 was used to perform Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. In accordance with data collected from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, a protein–protein interaction (PPI) network was constructed by Cytoscape. Cytohubba in Cytoscape was used to screen the hub genes. Furthermore, the hub genes were validated based on a separate dataset, GSE9210. Finally, potential micro RNAs (miRNAs) of hub genes were predicted by miRWalk 3.0.

Results

A total of 816 common DEGs, including 52 common upregulated and 764 common downregulated genes in two datasets, were screened out. Some of the more important of these pathways, including focal adhesion, PI3K-Akt signaling pathway, cell cycle, oocyte meiosis, AMP-activated protein kinase (AMPK) signaling pathway, FoxO signaling pathway, and Huntington disease, were involved in spermatogenesis. We further identified the top 20 hub genes from the PPI network, including CCNB2, DYNLL2, HMMR, NEK2, KIF15, DLGAP5, NUF2, TTK, PLK4, PTTG1, PBK, CEP55, CDKN3, CDC25C, MCM4, DNAI1, TYMS, PPP2R1B, DNAI2, and DYNLRB2, which were all downregulated genes. In addition, potential miRNAs of hub genes, including hsa-miR-3666, hsa-miR-130b-3p, hsa-miR-15b-5p, hsa-miR-6838-5p, and hsa-miR-195-5p, were screened out.

Conclusions

Taken together, the identification of the above hub genes, miRNAs and pathways will help us better understand the mechanisms associated with NOA, and provide potential biomarkers and therapeutic targets for NOA.

C3, C3AR1, HLA-DRA, and HLA-E as potential prognostic biomarkers for renal clear cell carcinoma

Background

Prognostic biomarkers play a vital role in the early detection of the cancer and assessment of prognosis. With advances in technology, a large number of biomarkers of kidney renal clear cell carcinoma (KIRC) have been discovered, but their prognostic value has not been fully investigated, and thus have not been widely used in clinical practice. We aimed to identify the reliable markers associated with the prognosis of KIRC patients.

Methods

We obtained 72 normal samples and 539 tumor samples from The Cancer Genome Atlas (TCGA), and 23 normal samples and 32 tumor samples from the Gene Expression Omnibus (GEO). Overlapping differentially expressed genes (ODEGs) were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, followed by construction of a protein-protein interaction (PPI) network to screen hub genes. Kaplan-Meier analysis, univariate Cox analysis, multivariate Cox analysis, Wilcoxon signed-rank test, Kruskal-Wallis test, and gene set enrichment analysis (GSEA) were performed to verify the prognostic value and function of the markers we selected. The relationships among gene expression level, tumor immune cell infiltration, and immune-checkpoints were also analyzed.

Results

A total of 910 genes were screened out, and C3, C3AR1, HLA-DRA, and HLA-E were identified as potential tumor markers. The expression of each gene was closely associated with tumor immune cell infiltration, survival rate, and the patients’ clinical characteristics (P<0.05). C3AR1, HLA-DRA, and HLA-E were also verified as independent prognostic factors of KIRC (P<0.05), and all these potential biomarkers had a close correlation with immune checkpoints.

Conclusions

C3, C3AR1, HLA-DRA, and HLA-E could be reliable biomarkers of KIRC and may have a significant contribution to make in immunotherapy, thus playing an important role in the improvement of prognosis.

Bioinformatic gene analysis for possible biomarkers and therapeutic targets of hypertension-related renal cell carcinoma

BACKGROUND

Renal cell carcinoma (RCC) is one of the most prevalent malignant tumors of the urinary system. Hypertension can cause hypertensive nephropathy (HN). Meanwhile, Hypertension is considered to be related to kidney cancer. We analyzed co-expressed genes to find out the relationship between hypertension and RCC and show possible biomarkers and novel therapeutic targets of hypertension-related RCC.

METHODS

We identified the differentially expressed genes (DEGs) of HN and RCC through analyzing Gene Expression Omnibus (GEO) datasets GSE99339, GSE99325, GSE53757 and GSE15641 by means of bioinformatics analysis, respectively. Then we evaluated these genes with protein-protein interaction (PPI) networks, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and CTD database. Subsequently, we verified co-expressed DEGs with Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, corresponding predicted miRNAs of co-expressed DEGs were identified and verified via mirDIP database and Starbase, respectively.

RESULTS

We identified 9 co-expressed DEGs, including BCAT1, CORO1A, CRIP1, ESRRG, FN1, LYZ, PYCARD, SAP30, and PTRF. CRIP1 and ESRRG and their corresponding predicted miRNAs, especially hsa-miR-221-5p, hsa-miR-205-5p, hsa-miR-152-3p and hsa-miR-137 may be notably related to hypertension-related RCC.

CONCLUSIONS

CRIP1 and ESRRG genes have great potential to become novel biomarkers and therapeutic targets concerning hypertension-related RCC.

The effect of CCL5 on the immune cells infiltration and the prognosis of patients with kidney renal clear cell carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) is the most representative subtype of renal cancer. Immune infiltration was associated with the survival time of patients with tumors. C-C chemokine ligand 5 (CCL5) can promote the malignant process of tumor and be related to infiltration immune cells in some cancers, but not reported in KIRC. Methods: The expression profile and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. The correlation between the expression level of CCL5 and clinical features in KIRC was analyzed. Gene Set Enrichment Analysis (GSEA) was utilized to explore the functions and pathways of CCL5 in KIRC. Then, the analysis between the survival and immune infiltration cells was carried out, as well as the non-parametric tests between the CCL5 expression and the ratios of immune infiltration cells. Results: The correlations between the expression levels of CCL5 in KIRC and clinical features including survival time, pathological stage, grade, and status of the patient, have been identified. Meanwhile, GSEA analysis has shown relationships between the expression of CCL5 and immune pathways. The immune infiltrated cells were correlated with the prognosis of KIRC, especially regulatory T cells (Tregs), mast cells, and dendritic cells. And Tregs was associated with the CCL5 expression. Conclusion: The increased expression of CCL5 is related to poor prognosis and clinical features. Meanwhile, CCL5 is related to Tregs ratios and CCL5 may act as a typical chemokine to recruit Tregs in KIRC. CCL5 could be used as a biomarker for the prognosis prediction and a potential therapeutic target for patients with KIRC.

Long non-coding RNA LINC01234 regulates proliferation, migration and invasion via HIF-2α pathways in clear cell renal cell carcinoma cells

Long non-coding RNAs (lncRNAs) have been proved to have an important role in different malignancies including clear cell renal cell carcinoma (ccRCC). However, their role in disease progression is still not clear. The objective of the study was to identify lncRNA-based prognostic biomarkers and further to investigate the role of one lncRNA LINC01234 in progression of ccRCC cells. We found that six adverse prognostic lncRNA biomarkers including LINC01234 were identified in ccRCC patients by bioinformatic analysis using The Cancer Genome Atlas database. LINC01234 knockdown impaired cell proliferation, migration and invasion in vitro as compared to negative control. Furthermore, the epithelial-mesenchymal transition was inhibited after LINC01234 knockdown. Additionally, LINC01234 knockdown impaired hypoxia-inducible factor-2a (HIF-2α) pathways, including a suppression of the expression of HIF-2α, vascular endothelial growth factor A, epidermal growth factor receptor, c-Myc, Cyclin D1 and MET. Together, these datas showed that LINC01234 was likely to regulate the progression of ccRCC by HIF-2α pathways, and LINC01234 was both a promising prognostic biomarker and a potential therapeutic target for ccRCC.

BRDT is a novel regulator of eIF4EBP1 in renal cell carcinoma

Among all types of kidney diseases, renal cell carcinoma (RCC) has the highest mortality, recurrence and metastasis rates, which results in high numbers of tumor-associated mortalities in China. Identifying a novel therapeutic target has attracted increasing attention. Bromodomain and extraterminal domain (BET) proteins have the ability to read the epigenome, leading to regulation of gene transcription. As an important member of the BET family, bromodomain testis-specific protein (BRDT) has been well studied; however, the mechanism underlying BRDT in the regulation of RCC has not been fully investigated. Eukaryotic translation initiation factor 4E-binding protein 1 (eIF4EBP1) is a binding partner of eIF4E that is involved in affecting the progression of various cancer types via regulating gene transcription. To identify novel regulators of eIF4EBP1, an immunoprecipitation assay and mass spectrometry analysis was performed in RCC cells. It was revealed that eIF4EBP1 interacted with BRDT, a novel interacting protein. In addition, the present study further demonstrated that BRDT inhibitors PLX51107 and INCB054329 blocked the progression of RCC cells, along with suppressing eIF4EBP1 and c-myc expression. Small interfering (si) RNAs were used to knock down BRDT expression, which suppressed RCC cell proliferation and eIF4EBP1 protein expression. In addition, overexpression of eIF4EBP1 partially abolished the inhibited growth function of PLX51107 but knocking down eIF4EBP1 improved the inhibitory effects of PLX51107. Furthermore, treatment with PLX51107 or knockdown of BRDT expression decreased c-myc expression at both the mRNA and protein levels, and attenuated its promoter activity, as determined by luciferase reporter assays. PLX51107 also significantly altered the interaction between the c-myc promoter with eIF4EBP1 and significantly attenuated the increase of RCC tumors, accompanied by decreased c-myc mRNA and protein levels in vivo. Taken together, these data suggested that blocking of BRDT by PLX51107, INCB054329 or BRDT knockdown suppressed the growth of RCC via decreasing eIF4EBP1, thereby leading to decreased c-myc transcription levels. Considering the regulatory function of BET proteins in gene transcription, the present study suggested that there is a novel mechanism underlying eIF4EBP1 regulation by BRDT, and subsequently decreased c-myc in RCC, and further identified a new approach by regulating eIF4EBP1 or c-myc for enhancing BRDT-targeting RCC therapy.

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 Hub Genes Related to Carcinogenesis and Prognosis in Colorectal Cancer Based on Integrated Bioinformatics

The high mortality of colorectal cancer (CRC) patients and the limitations of conventional tumor-node-metastasis (TNM) stage emphasized the necessity of exploring hub genes closely related to carcinogenesis and prognosis in CRC. The study is aimed at identifying hub genes associated with carcinogenesis and prognosis for CRC. We identified and validated 212 differentially expressed genes (DEGs) from six Gene Expression Omnibus (GEO) datasets and the Cancer Genome Atlas (TCGA) database. We investigated functional enrichment analysis for DEGs. The protein-protein interaction (PPI) network was constructed, and hub modules and genes in CRC carcinogenesis were extracted. A prognostic signature was developed and validated based on Cox proportional hazards regression analysis. The DEGs mainly regulated biological processes covering response to stimulus, metabolic process, and affected molecular functions containing protein binding and catalytic activity. The DEGs played important roles in CRC-related pathways involving in preneoplastic lesions, carcinogenesis, metastasis, and poor prognosis. Hub genes closely related to CRC carcinogenesis were extracted including six genes in model 1 (CXCL1, CXCL3, CXCL8, CXCL11, NMU, and PPBP) and two genes and Metallothioneins (MTs) in model 2 (SLC26A3 and SLC30A10). Among them, CXCL8 was also related to prognosis. An eight-gene signature was proposed comprising AMH, WBSCR28, SFTA2, MYH2, POU4F1, SIX4, PGPEP1L, and PAX5. The study identified hub genes in CRC carcinogenesis and proposed an eight-gene signature with good reproducibility and robustness at the molecular level for CRC, which might provide directive significance for treatment selection and survival prediction.

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.

Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial.

METHODS

Raw count of RNA-sequencing data and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA). Tumor samples were divided into two sets. Differentially expressed genes (DEGs) were screened in the whole set and prognosis-related genes were identified from the training set. Their common genes were used in LASSO and best subset regression which were performed to identify the best prognostic 5 genes. The gene-based risk score was developed based on the Cox coefficient of the individual gene. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess its prognostic power. GSE29609 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to screen independent prognostic parameters to construct a nomogram. The predictive power of the nomogram was revealed by time-dependent ROC curves and the calibration plot and verified in the validation set. Finally, Functional enrichment analysis of DEGs and 5 novel genes were performed to suggest the potential biological pathways.

RESULTS

PADI1, ATP6V0D2, DPP6, C9orf135 and PLG were screened to be significantly related to the prognosis of ccRCC patients. The risk score effectively stratified the patients into high-risk group with poor overall survival (OS) based on survival analysis. AJCC-stage, age, recurrence and risk score were regarded as independent prognostic parameters by Cox regression analysis and were used to construct a nomogram. Time-dependent ROC curves showed the nomogram performed best in 1-, 3- and 5-year survival predictions compared with AJCC-stage and risk score in validation sets. The calibration plot showed good agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis suggested several enriched biological pathways related to cancer.

CONCLUSIONS

In our study, we constructed a gene-based model integrating clinical prognostic parameters to predict prognosis of ccRCC well, which might provide a reliable prognosis assessment tool for clinician and aid treatment decision-making in the clinic.

Identification of microenvironment-related genes with prognostic value in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney tumor. Previous studies have shown that the interaction between tumor cells and microenvironment has an important impact on prognosis. Immune and stromal cells are two vital components of the tumor microenvironment. Our study aimed to better understand and explore the genes involved in immune/stromal cells on prognosis. We used the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm to calculate immune/stromal scores. According to the scores, we divided ccRCC patients from The Cancer Genome Atlas database into low and high groups and identified the genes which were differentially expressed and significantly associated with prognosis. The result of functional enrichment analysis and protein-protein interaction networks indicated that these genes mainly were involved in extracellular matrix and regulation of cellular activities. Then another independent cohort from the International Cancer Genome Consortium database was used to validate these genes. Finally, we acquired a list of microenvironment-related genes that can predict prognosis for ccRCC patients.

Identification of Prognostic and Metastatic Alternative Splicing Signatures in Kidney Renal Clear Cell Carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) is the malignancy originated from the renal epithelium, with a high rate of distant metastasis. Aberrant alternative splicing (AS) of pre-mRNA are widely reported to be involved in the tumorigenesis and metastasis of multiple cancers. The aim of this study is to explore the mechanism of alternative splicing events (ASEs) underlying tumorigenesis and metastasis of KIRC. Methods: RNA-seq of 537 KIRC samples downloaded from the TCGA database and ASEs data from the TCGASpliceSeq database were used to identify ASEs in patients with KIRC. The univariate and Lasso regression analysis were used to screen the most significant overall survival-related ASEs (OS-SEs). Based on those, the OS-SEs model was proposed. The interaction network of OS-SEs and splicing factors (SFs) with absolute value of correlation coefficient value >0.750 was constructed by Pearson correlation analysis. The OS-SEs significantly related to distant metastasis and clinical stage were identified by non-parametric test, and those were also integrated into co-expression analysis with prognosis-related Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways identified by Gene Set Variation Analysis (GSVA). ASEs with significance were selected for multiple online database validation. Results: A total of prognostic 6,081 overall survival-related ASEs (OS-SEs) were identified by univariate Cox regression analysis and a prediction model was constructed based on 5 OS-SEs screened by Lasso regression with the Area Under Curve of 0.788. Its risk score was also illustrated to be an independent predictor, which the good reliability of the model. Among 390 identified candidate SFs, DExD-Box Helicase 39B (DDX39B) was significantly correlated with OS and metastasis. After external database validation, Retained Intron of Ras Homolog Family Member T2 (RHOT2) and T-Cell Immune Regulator 1 (TCIRG1) were identified. In the co-expression analysis, overlapped co-expression signal pathways for RHOT2 and TCIRG1 were sphingolipid metabolism and N-glycan biosynthesis. Conclusions: Based on the results of comprehensive bioinformatic analysis, we proposed that aberrant DDX39B regulated RHOT2-32938-RI and TCIRG1-17288-RI might be associated with the tumorigenesis, metastasis, and poor prognosis of KIRC via sphingolipid metabolism or N-glycan biosynthesis pathway.

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.

Identification of key candidate targets and pathways for the targeted treatment of leukemia stem cells of chronic myelogenous leukemia using bioinformatics analysis

Background

Chronic myelogenous leukemia (CML) is a myeloproliferative neoplasm that arises from the acquisition of constitutively active BCR‐ABL tyrosine kinase in hematopoietic stem cells. The persistence of bone marrow leukemia stem cells (LSCs) is the main cause of TKI resistance and CML relapse. Therefore, finding a key target or pathway to selectively target LSCs is of great significance for the thorough treatment of CML.

Methods

In this study, we aimed to identify key microRNAs, microRNA targets and pathways for the treatment of CML LSCs by integrating analyses of three microarray data profiles. We identified 51 differentially expressed microRNAs through integrated analysis of GSE90773 and performed functional gene predictions for microRNAs. Then, GSE11889 and GSE11675 were integrated to obtain differentially expressed genes (DEGs), and the overlapping DEGs were used as models to identify predictive functional genes. Finally, we identified 116 predictive functional genes. Clustering and significant enrichment analysis of 116 genes was based on function and signaling pathways. Subsequently, a protein interaction network was constructed, and module analysis and topology analysis were performed on the network.

Results

A total of 11 key candidate targets and 33 corresponding microRNAs were identified. The key pathways were mainly concentrated on the PI3K/AKT, Ras, JAK/STAT, FoxO and Notch signaling pathways. We also found that LSCs negatively regulated endogenous and exogenous apoptotic pathways to escape from apoptosis.

Conclusion

We identified key candidate targets and pathways for CML LSCs through bioinformatics methods, which improves our understanding of the molecular mechanisms of CML LSCs. These candidate genes and pathways may be therapeutic targets for CML LSCs.

The promising novel biomarkers and candidate small molecule drugs in lower-grade glioma: Evidence from bioinformatics analysis of high-throughput data

Overall survival of patients with low-grade glioma (LGG) has shown no significant improvement over the past 30 years, with survival averaging approximately 7 years. This study aimed to identify novel promising biomarkers of LGG and reveal its potential molecular mechanisms by integrated bioinformatics analysis. The microarray datasets of GSE68848 and GSE4290 were selected from GEO database for integrated analysis. In total, 293 overlapping differentially expressed genes (DEGs) were detected using the limma package. One hundred and eighty-eight nodes with 603 interactions were obtained from the establishment of protein-protein interaction (PPI) network. Functional and signaling pathway enriched were significantly correlated with the synapse and calcium signaling pathway, respectively. Module analysis revealed eight hub genes with high connectivity, which included CHRM1, DLG2, GABRD, GRIN1, HTR2A, KCNJ3, KCNJ9, and NUSAP1, and they were markedly correlated with patients' prognosis. The mining of the Gene Expression Profiling Interactive Analysis database and qPCR further confirmed the abnormal expression of these key genes with their prognostic value in LGG. We eventually predicted the 20 most vital small molecule drugs, which potentially reverse the carcinogenic state of LGG, as per the CMap (connectivity map) database and these DEGs, and MS-275 (enrichment score = -0.939) was considered as the most promising small molecule to treat LGG. In conclusion, our study provided eight reliable novel molecular biomarkers for diagnosis, prognosis prediction, and treatment targets for LGG. These conclusions will contribute to a better comprehension of molecular mechanisms fundamental to LGG occurrence and progression, and providing new insights for future development of genomic individualized treatment in LGG.

Comprehensive Analysis of Driver Genes in Personal Genomes of Clear Cell Renal Cell Carcinoma

AIM

To characterize personal driver genes in clear cell renal cell carcinoma independent of somatic mutation frequencies.

METHODS

Personal cancer driver genes were predicted by Integrated CAncer GEnome Score in 417 patients with clear cell renal cell carcinoma using 26 786 somatic mutations from The Cancer Genome Atlas, followed by an integrated investigation on personal driver genes.

RESULTS

A total of 233 personal driver genes were determined by Integrated CAncer GEnome Score. The coexpression network analysis found 5 coexpressed modules. The blue module was significantly negatively correlated with all 5 clinical features, including cancer stage, lymph node metastasis, distant metastasis, age, and survival status (death). CTNNB1, TGFBR2, KDR, FLT1, and INSR were the hub genes in the blue module. The expression of 79 personal driver genes was significantly associated with clinical outcomes of patients with clear cell renal cell carcinoma.

CONCLUSIONS

The set of personal driver genes sheds insights into the tumorigenesis of clear cell renal cell carcinoma and paves the way for developing personalized medicine for clear cell renal cell carcinoma.

Identification of key candidate genes and biological pathways in bladder cancer

Background

Bladder cancer is a malignant tumor in the urinary system with high mortality and recurrence rates. However, the causes and recurrence mechanism of bladder cancer are not fully understood. In this study, we used integrated bioinformatics to screen for key genes associated with the development of bladder cancer and reveal their potential molecular mechanisms.

Methods

The GSE7476, GSE13507, GSE37815 and GSE65635 expression profiles were downloaded from the Gene Expression Omnibus database, and these datasets contain 304 tissue samples, including 81 normal bladder tissue samples and 223 bladder cancer samples. The RobustRankAggreg (RRA) method was utilized to integrate and analyze the four datasets to obtain integrated differentially expressed genes (DEGs), and the gene ontology (GO) functional annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. The OncoLnc online tool was utilized to analyze the relationship between the expression of hub genes and the prognosis of bladder cancer.

Results

In total, 343 DEGs, including 111 upregulated and 232 downregulated genes, were identified from the four datasets. GO analysis showed that the upregulated genes were mainly involved in mitotic nuclear division, the spindle and protein binding. The downregulated genes were mainly involved in cell adhesion, extracellular exosomes and calcium ion binding. The top five enriched pathways obtained in the KEGG pathway analysis were focal adhesion (FA), PI3K-Akt signaling pathway, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. The top 10 hub genes identified from the PPI network were vascular endothelial growth factor A (VEGFA), TOP2A, CCNB1, Cell division cycle 20 (CDC20), aurora kinase B, ACTA2, Aurora kinase A, UBE2C, CEP55 and CCNB2. Survival analysis revealed that the expression levels of ACTA2, CCNB1, CDC20 and VEGFA were related to the prognosis of patients with bladder cancer. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 mainly involved the cell cycle and oocyte meiosis, while the analysis in Module 2 mainly involved the complement and coagulation cascades, vascular smooth muscle contraction and FA.

Conclusions

This study identified key genes and pathways in bladder cancer, which will improve our understanding of the molecular mechanisms underlying the development and progression of bladder cancer. These key genes might be potential therapeutic targets and biomarkers for the treatment of bladder cancer.

Identification of differentially expressed genes in synovial tissue of rheumatoid arthritis and osteoarthritis in patients

Rheumatoid arthritis (RA) and osteoarthritis (OA) are the common joints disorder in the world. Although they have showed the analogous clinical manifestation and overlapping cellular and molecular foundation, the pathogenesis of RA and OA were different. The pathophysiologic mechanisms of arthritis in RA and OA have not been investigated thoroughly. Thus, the aim of study is to identify the potential crucial genes and pathways associated with RA and OA and further analyze the molecular mechanisms implicated in genesis. First, we compared gene expression profiles in synovial tissue between RA and OA from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Gene Expression Series (GSE) 1919, GSE55235, and GSE36700 were downloaded from the GEO database, including 20 patients of OA and 21 patients of RA. Differentially expressed genes (DEGs) including "CXCL13," "CD247," "CCL5," "GZMB," "IGKC," "IL7R," "UBD///GABBR1," "ADAMDEC1," "BTC," "AIM2," "SHANK2," "CCL18," "LAMP3," "CR1," and "IL32." Second, Gene Ontology analyses revealed that DEGs were significantly enriched in integral component of extracellular space, extracellular region, and plasma membrane in the molecular function group. Signaling pathway analyses indicated that DEGs had common pathways in chemokine signaling pathway, cytokine-cytokine receptor interaction, and cytosolic DNA-sensing pathway. Third, DEGs showed the complex DEGs protein-protein interaction network with the Coexpression of 83.22%, Shared protein domains of 8.40%, Colocalization of 4.76%, Predicted of 2.87%, and Genetic interactions of 0.75%. In conclusion, the novel DEGs and pathways between RA and OA identified in this study may provide new insight into the underlying molecular mechanisms of RA.

COL1A1: A potential therapeutic target for colorectal cancer expressing wild-type or mutant KRAS

Colorectal cancer (CRC) treatment primarily relies on chemotherapy along with surgery, radiotherapy and, more recently, targeted therapy at the late stages. However, chemotherapeutic drugs have high cytotoxicity, and the similarity between the effects of these drugs on cancerous and healthy cells limits their wider use in clinical settings. Targeted monoclonal antibody treatment may compensate for this deficiency. Epidermal growth factor receptor (EGFR)-targeted drugs have a positive effect on CRC with intact KRAS proto-oncogene GTPase (KRAS or KRAS^WT), but may be ineffective or harmful in patients with KRAS mutations (KRAS^MUT). Therefore, it is important to identify drug target genes that are uniformly effective with regards to KRAS^WT and KRAS^MUT CRC. The present study performed gene expression analysis, and identified 294 genes upregulated in KRAS^WT and KRAS^MUT CRC samples. Collagen type I α 1 (COL1A1) was identified as the hub gene through STRING and Cytoscape analyses. Consistent with results obtained from Oncomine, a cancer microarray database and web-based data-mining platform, it was demonstrated that the expression of COL1A1 was significantly upregulated in CRC tissues and cell lines regardless of KRAS status. Inhibition of COL1A1 in KRAS^WT and KRAS^MUT CRC cell lines significantly decreased cell proliferation and invasion. In addition, increased COL1A1 expression in CRC was significantly associated with serosal invasion, lymph metastases and hematogenous metastases. Taken together, the findings of the present study indicated that COL1A1 may serve as a candidate diagnostic biomarker and a promising therapeutic target for CRC.

Identification of Potential Key Genes Associated With the Pathogenesis and Prognosis of Gastric Cancer Based on Integrated Bioinformatics Analysis

Background and Objective: Despite striking advances in multimodality management, gastric cancer (GC) remains the third cause of cancer mortality globally and identifying novel diagnostic and prognostic biomarkers is urgently demanded. The study aimed to identify potential key genes associated with the pathogenesis and prognosis of GC.

Methods: Differentially expressed genes between GC and normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Key genes related to the pathogenesis and prognosis of GC were identified by employing protein–protein interaction network and Cox proportional hazards model analyses.

Results: We identified nine hub genes (TOP2A, COL1A1, COL1A2, NDC80, COL3A1, CDKN3, CEP55, TPX2, and TIMP1) which might be tightly correlated with the pathogenesis of GC. A prognostic gene signature consisted of CST2, AADAC, SERPINE1, COL8A1, SMPD3, ASPN, ITGBL1, MAP7D2, and PLEKHS1 was constructed with a good performance in predicting overall survivals.

Conclusion: The findings of this study would provide some directive significance for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of GC.

Bioinformatics-based identification of potential microRNA biomarkers in frequent and non-frequent exacerbators of COPD

Objectives

MicroRNAs (miRNAs) play essential roles in the development of COPD. In this study, we aimed to identify and validate potential miRNA biomarkers in frequent and non-frequent exacerbators of COPD patients using bioinformatic analysis.

Materials and methods

The candidate miRNA biomarkers in COPD were screened from Gene Expression Omnibus (GEO) dataset and identified using GEO2R online tool. Then, we performed bioinformatic analyses including target prediction, gene ontology (GO), pathway enrichment analysis and construction of protein–protein interaction (PPI) network. Furthermore, the expression of the identified miRNAs in peripheral blood monocular cells (PBMCs) of COPD patients was validated using quantitative real-time polymerase chain reaction (qRT-PCR).

Results

MiR-23a, miR-25, miR-145 and miR-224 were identified to be significantly downregulated in COPD patients compared with healthy controls. GO analysis showed the four miRNAs involved in apoptotic, cell differentiation, cell proliferation and innate immune response. Pathway analysis showed that the targets of these miRNAs were associated with p53, TGF-β, Wnt, VEGF and MAPK signal pathway. In healthy controls, the miR-25 and miR-224 levels were significantly decreased in smokers compared with nonsmokers (P<0.001 and P<0.05, respectively). In COPD patients, the levels of miR-23a, miR-25, miR-145 and miR-224 were associated with Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages. Notably, miR-23a and miR-145 were significantly elevated in non-frequent exacerbators compared with frequent exacerbators (P<0.05), and miR-23a showed higher area under the receiver–operator characteristic curve (AUROC) than miR-145 (0.707 vs 0.665, P<0.05).

Conclusion

MiR-23a, miR-25, miR-145 and miR-224 were associated with the development of COPD, and miR-23a might be a potential biomarker for discriminating the frequent exacerbators from non-frequent exacerbators.