Identification of potential key genes for HER-2 positive breast cancer based on bioinformatics analysis

Assessing the mechanism of fast-cycling cancer-associated mutations of Rac1 small Rho GTPase

Rho-GTPases proteins function as molecular switches alternating from an active to an inactive state upon Guanosine triphosphate (GTP) binding and hydrolysis to Guanosine diphosphate (GDP). Among them, Rac subfamily regulates cell dynamics, being overexpressed in distinct cancer types. Notably, these proteins are object of frequent cancer-associated mutations at Pro29 (P29S, P29L, and P29Q). To assess the impact of these mutations on Rac1 structure and function, we performed extensive all-atom molecular dynamics simulations on wild-type (wt) and oncogenic isoforms of this protein in GDP- and GTP-bound states. Our results unprecedentedly elucidate that P29Q/S-induced structural and dynamical perturbations of Rac1 core domain weaken the binding of the catalytic site Mg2+ ion, and reduce the GDP residence time within protein, enhancing the GDP/GTP exchange rate and Rac1 activity. This broadens our knowledge of the role of cancer-associated mutations on small GTPases mechanism supplying valuable information for future drug discovery efforts targeting specific Rac1 isoforms.

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Rac1 promotes the reprogramming of glucose metabolism and the growth of colon cancer cells through upregulating SOX9

Metabolic reprogramming is the survival rule of tumor cells, and tumor cells can meet their high metabolic requirements by changing the energy metabolism mode. Metabolic reprogramming of tumor cells is an important biochemical basis of tumor malignant phenotypes. Ras-related C3 botulinum toxin substrate 1 (Rac1) is abnormally expressed in a variety of tumors and plays an important role in the proliferation, invasion, and migration of tumor cells. However, the role of Rac1 in tumor metabolic reprogramming is still unclear. Herein, we revealed that Rac1 was highly expressed in colon cancer tissues and cell lines. Rac1 promotes the proliferation, migration, and invasion of colon cancer cells by upregulating SOX9, which as a transcription factor can directly bind to the promoters of HK2 and G6PD genes and regulate their transcriptional activity. Rac1 upregulates the expression of SOX9 through the PI3K/AKT signaling pathway. Moreover, Rac1 can promote glycolysis and the activation of the pentose phosphate pathway in colon cancer cells by mediating the axis of SOX9/HK2/G6PD. These findings reveal novel regulatory axes involving Rac1/SOX9/HK2/G6PD in the development and progression of colon cancer, providing novel promising therapeutic targets.

Clinical value and potential mechanisms of BUB1B up-regulation in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) has insidious onset, late clinical diagnosis and high recurrence rate, which leads to poor quality of patient life. Therefore, it is necessary to further explore the pathogenesis and therapy targets of NPC. BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) was found to be up-regulated in a variety of cancers, but only two previous study showed that BUB1B was overexpressed in NPC and the sample size was small. The clinical role of BUB1B expression and its underlying mechanism in NPC require more in-depth research. Immunohistochemical samples and public RNA-seq data indicated that BUB1B protein and mRNA expression levels were up-regulated in NPC, and summary receiver operating characteristic curve indicated that BUB1B expression level had a strong ability to distinguish NPC tissues from non-NPC tissues. Gene ontology and Kyoto Encyclopedia of genes and genomes were performed and revealed that BUB1B and its related genes were mainly involved in cell cycle and DNA replication. Protein- Protein Interaction were built to interpret the BUB1B molecular mechanism. Histone deacetylase 2 (HDAC2) could be the upstream regulation factor of BUB1B, which was verified by Chromatin Immunoprecipitation Sequencing samples. In summary, BUB1B was highly expressed in NPC, and HDAC2 may affect cell cycle by regulating BUB1B to promote cancer progression.

A genomic and transcriptomic study toward breast cancer

Background: Breast carcinoma is well recognized to be having the highest global occurrence rate among all cancers, being the leading cause of cancer mortality in females. The aim of this study was to elucidate breast cancer at the genomic and transcriptomic levels in different subtypes so that we can develop more personalized treatments and precision medicine to obtain better outcomes.

Method: In this study, an expression profiling dataset downloaded from the Gene Expression Omnibus database, GSE45827, was re-analyzed to compare the expression profiles of breast cancer samples in the different subtypes. Using the GEO2R tool, different expression genes were identified. Using the STRING online tool, the protein–protein interaction networks were conducted. Using the Cytoscape software, we found modules, seed genes, and hub genes and performed pathway enrichment analysis. The Kaplan–Meier plotter was used to analyze the overall survival. MicroRNAs and transcription factors targeted different expression genes and were predicted by the Enrichr web server.

Result: The analysis of these elements implied that the carcinogenesis and development of triple-negative breast cancer were the most important and complicated in breast carcinoma, occupying the most different expression genes, modules, seed genes, hub genes, and the most complex protein–protein interaction network and signal pathway. In addition, the luminal A subtype might occur in a completely different way from the other three subtypes as the pathways enriched in the luminal A subtype did not overlap with the others. We identified 16 hub genes that were related to good prognosis in triple-negative breast cancer. Moreover, SRSF1 was negatively correlated with overall survival in the Her2 subtype, while in the luminal A subtype, it showed the opposite relationship. Also, in the luminal B subtype, CCNB1 and KIF23 were associated with poor prognosis. Furthermore, new transcription factors and microRNAs were introduced to breast cancer which would shed light upon breast cancer in a new way and provide a novel therapeutic strategy.

Conclusion: We preliminarily delved into the potentially comprehensive molecular mechanisms of breast cancer by creating a holistic view at the genomic and transcriptomic levels in different subtypes using computational tools. We also introduced new prognosis-related genes and novel therapeutic strategies and cast new light upon breast cancer.

Survivin in Breast Cancer: A Review

Breast cancer is the most frequently diagnosed cancer in women and ranks second among causes for cancer-related death in women. Gene technology has led to the recognition that breast cancer is a heterogeneous disease composed of different biological subtypes, and genetic profiling enables the response to chemotherapy to be predicted. This fact emphasizes the importance of selecting sensitive diagnostic and prognostic markers in the early disease stage and more efficient targeted treatments for this disease. One such prognostic marker appears to be survivin. Many studies have shown that survivin is strongly expressed in different types of cancers. Its overexpression has been demonstrated in breast cancer, and high activity of the survivin gene has been associated with a poor prognosis and worse survival rates.

Screening a novel six critical gene-based system of diagnostic and prognostic biomarkers in prostate adenocarcinoma patients with different clinical variables

The mechanisms behind prostate adenocarcinoma (PRAD) pathogenicity remain to be understood due to tumor heterogeneity. In the current study, we identified by microarray technology six eligible real hub genes from already identified hub genes through a systematic in silico approach that could be useful to lower the heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment. For this purpose, microarray technology-based, already-identified PRAD-associated hub genes were initially explored through extensive literature mining; then, a protein-protein interaction (PPI) network construction of those hub genes and its analysis helped us to identify six most critical genes (real hub genes). Various online available expression databases were then used to explore the tumor driving, diagnostic, and prognostic roles of real hub genes in PRAD patients with different clinicopathologic variables. In total, 124 hub genes were extracted from the literature, and among those genes, six, including CDC20, HMMR, AURKA, CDK1, ASF1B, and CCNB1 were identified as real hub genes by the degree method. Further expression analysis revealed the significant up-regulation of real hub genes in PRAD patients of different races, age groups, and nodal metastasis status relative to controls. Moreover, through correlational analyses, different valuable correlations between treal hub genes expression and different other data (promoter methylation status, genetic alterations, overall survival (OS), tumor purity, CD4+ T, CD8+ T immune cells infiltration, and different other mutant genes and a few more) across PRAD samples were also documented. Ultimately, from this study, a few important transcription factors (TFS), miRNAs, and chemotherapeutic drugs showing a great therapeutic potential were also identified. In conclusion, we have discovered a set of six real hub genes that might be utilized as new biomarkers for lowering heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment.

Lift the Veil of Breast Cancers Using 4 or Fewer Critical Genes

Known genes in the breast cancer study literature could not be confirmed whether they are vital to breast cancer formations due to lack of convincing accuracy, although they may be biologically directly related to breast cancer based on present biological knowledge. It is hoped vital genes can be identified with the highest possible accuracy, for example, 100% accuracy and convincing causal patterns beyond what has been known in breast cancer. One hope is that finding gene-gene interaction signatures and functional effects may solve the puzzle. This research uses a recently developed competing linear factor analysis method in differentially expressed gene detection to advance the study of breast cancer formation. Surprisingly, 3 genes are detected to be differentially expressed in TNBC and non-TNBC (Her2, Luminal A, Luminal B) samples with 100% sensitivity and 100% specificity in 1 study of triple-negative breast cancers (TNBC, with 54 675 genes and 265 samples). These 3 genes show a clear signature pattern of how TNBC patients can be grouped. For another TNBC study (with 54 673 genes and 66 samples), 4 genes bring the same accuracy of 100% sensitivity and 100% specificity. Four genes are found to have the same accuracy of 100% sensitivity and 100% specificity in 1 breast cancer study (with 54 675 genes and 121 samples), and the same 4 genes bring an accuracy of 100% sensitivity and 96.5% specificity in the fourth breast cancer study (with 60 483 genes and 1217 samples). These results show the 4-gene-based classifiers are robust and accurate. The detected genes naturally classify patients into subtypes, for example, 7 subtypes. These findings demonstrate the clearest gene-gene interaction patterns and functional effects with the smallest numbers of genes and the highest accuracy compared with findings reported in the literature. The 4 genes are considered to be essential for breast cancer studies and practice. They can provide focused, targeted researches and precision medicine for each subtype of breast cancer. New breast cancer disease types may be detected using the classified subtypes, and hence new effective therapies can be developed.

Analysis of the Effect of Trastuzumab Combined with Docetaxel on Serum Tumor Markers in the Treatment of HER-2 Positive Breast Cancer and Factors Influencing Therapeutic Efficacy

Objective

To explore the influence of trastuzumab (TZ) combined with docetaxel (DTX) on serum tumor markers (TMs) in the treatment of human epidermal growth factor receptor 2-positive (HER-2+) breast cancer (BC) and to analyze the factors influencing therapeutic efficacy.

Methods

Ninety-six patients with HER-2+ BC treated in the First Affiliated Hospital of Anhui University Of Science and Technology from January 2019 to December 2020 were selected. According to different treatment plans, the patients were divided into two arms with 48 cases each. The control group (CG) was treated with DTX, and the research group (RG) was given TZ combined with DTX (TZ+DTX). The two arms were compared regarding the following aspects: curative effects, adverse reaction, alterations of TMs and inflammatory factors (IFs), and quality of life. Logistic regression analysis was performed to analyze the factors affecting the efficacy of patients.

Results

After treatment, the TMs carcinoembryonic antigen (CEA), carbohydrate antigen (CA)125 and CA15-3 were significantly lower in RG compared with CG. The levels of IFs C-reactive protein (CRP) and tumor necrosis factor-α (TNF-α) were also lower in CG. The overall response rate and the Karnofsky performance status (KPS) score were significantly higher in RG. No evident difference was observed in the total incidence of adverse reactions between the two arms. The high expression of CEA, CA125 and CA15-3 as well as DTX monotherapy increased the risk of adverse prognosis.

Conclusion

TZ+DTX can effectively improve the clinical efficacy of HER-2+ BC patients and reduce their levels of serum TMs and IFs.

Identification of candidate biomarkers correlated with poor prognosis of breast cancer based on bioinformatics analysis

Breast cancer (BC) is a malignancy with high incidence among women in the world. This study aims to screen key genes and potential prognostic biomarkers for BC using bioinformatics analysis. Total 58 normal tissues and 203 cancer tissues were collected from three Gene Expression Omnibus (GEO) gene expression profiles, and then the differential expressed genes (DEGs) were identified. Subsequently, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway were analyzed to investigate the biological function of DEGs. Additionally, hub genes were screened by constructing a protein–protein interaction (PPI) network. Then, we explored the prognostic value and molecular mechanism of these hub genes using Kaplan–Meier (KM) curve and Gene Set Enrichment Analysis (GSEA). As a result, 42 up-regulated and 82 down-regulated DEGs were screened out from GEO datasets. The DEGs were mainly related to cell cycles and cell proliferation by GO and KEGG pathway analysis. Furthermore, 12 hub genes (FN1, AURKA, CCNB1, BUB1B, PRC1, TPX2, NUSAP1, TOP2A, KIF20A, KIF2C, RRM2, ASPM) with a high degree were identified initially, among which, 11 hub genes were significantly correlated with the prognosis of BC patients based on the Kaplan–Meier-plotter. GSEA reviewed that these hub genes correlated with KEGG_CELL_CYCLE and HALLMARK_P53_PATHWAY. In conclusion, this study identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis. This finding will improve our knowledge of the BC progress and mechanisms.

Prediction of Disease Genes Based on Stage-Specific Gene Regulatory Networks in Breast Cancer

Breast cancer is one of the most common malignant tumors in women, which seriously endangers women’s health. Great advances have been made over the last decades, however, most studies predict driver genes of breast cancer using biological experiments and/or computational methods, regardless of stage information. In this study, we propose a computational framework to predict the disease genes of breast cancer based on stage-specific gene regulatory networks. Firstly, we screen out differentially expressed genes and hypomethylated/hypermethylated genes by comparing tumor samples with corresponding normal samples. Secondly, we construct three stage-specific gene regulatory networks by integrating RNA-seq profiles and TF-target pairs, and apply WGCNA to detect modules from these networks. Subsequently, we perform network topological analysis and gene set enrichment analysis. Finally, the key genes of specific modules for each stage are screened as candidate disease genes. We obtain seven stage-specific modules, and identify 20, 12, and 22 key genes for three stages, respectively. Furthermore, 55%, 83%, and 64% of the genes are associated with breast cancer, for example E2F2, E2F8, TPX2, BUB1, and CKAP2L. So it may be of great importance for further verification by cancer experts.

Prognostic value of baculoviral IAP repeat containing 5 expression as a new biomarker in lung adenocarcinoma: a meta-analysis

BACKGROUND

BIRC5 is associated with the prognosis of a variety of tumors. This meta-analysis aimed to identify whether BIRC5 is associated with the prognosis of lung adenocarcinoma (LUAD).

RESEARCH DESIGN AND METHODS

We conducted an in-depth review of seven Chinese and English databases and two high-throughput sequencing databases according to inclusion and exclusion criteria to find relevant studies. The pooled standardized mean differences (SMDs) and 95% confidence intervals (CIs) for the associations between the BIRC5 expression level and clinicopathological characteristics were calculated, and the pooled hazard ratios (HRs) and 95% CIs were calculated to estimate associations between the BIRC5 expression level and survival outcomes.

RESULTS

In total, 17 studies involving 2887 LUAD patients whose BIRC5 expression level was known were included in this meta-analysis. The BIRC5 expression level was higher in younger patients, males, and smokers and correlated with advanced AJCC, T and N stages but not M stage. A high BIRC5 expression level also correlated with poor overall survival (OS) and progression-free survival (PFS). There was no publication bias in this study.

CONCLUSIONS

This meta-analysis indicates that BIRC5 is a significant biomarker for a poor prognosis and poor clinicopathological outcomes in patients with LUAD.

Rac1, A Potential Target for Tumor Therapy

RAS-related C3 botulinum toxin substrate 1 (Rac.1) is one of the important members of Rho GTPases. It is well known that Rac1 is a cytoskeleton regulation protein that regulates cell adhesion, morphology, and movement. Rac1 is highly expressed in different types of tumors, which is related to poor prognosis. Studies have shown that Rac1 not only participates in the tumor cell cycle, apoptosis, proliferation, invasion, migration and angiogenesis, but also participates in the regulation of tumor stem cell, thus promoting the occurrence of tumors. Rac1 also plays a key role in anti-tumor therapy and participates in immune escape mediated by the tumor microenvironment. In addition, the good prospects of Rac1 inhibitors in cancer prevention and treatment are exciting. Therefore, Rac1 is considered as a potential target for the prevention and treatment of cancer. The necessity and importance of Rac1 are obvious, but it still needs further study.

The BIRC Family Genes Expression in Patients with Triple Negative Breast Cancer

The BIRC (baculoviral IAP repeat-containing; BIRC) family genes encode for Inhibitor of Apoptosis (IAP) proteins. The dysregulation of the expression levels of the genes in question in cancer tissue as compared to normal tissue suggests that the apoptosis process in cancer cells was disturbed, which may be associated with the development and chemoresistance of triple negative breast cancer (TNBC). In our study, we determined the expression level of eight genes from the BIRC family using the Real-Time PCR method in patients with TNBC and compared the obtained results with clinical data. Additionally, using bioinformatics tools (Ualcan and The Breast Cancer Gene-Expression Miner v4.5 (bc-GenExMiner v4.5)), we compared our data with the data in the Cancer Genome Atlas (TCGA) database. We observed diverse expression pattern among the studied genes in breast cancer tissue. Comparing the expression level of the studied genes with the clinical data, we found that in patients diagnosed with breast cancer under the age of 50, the expression levels of all studied genes were higher compared to patients diagnosed after the age of 50. We observed that in patients with invasion of neoplastic cells into lymphatic vessels and fat tissue, the expression levels of BIRC family genes were lower compared to patients in whom these features were not noted. Statistically significant differences in gene expression were also noted in patients classified into three groups depending on the basis of the Scarff-Bloom and Richardson (SBR) Grading System.

Key Genes and Prognostic Analysis in HER2+ Breast Cancer

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes (CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

Independent prognostic implications of RRM2 in lung adenocarcinoma

Background: Ribonucleoside-diphosphate reductase subunit M2 (RRM2) is the catalytic subunit of ribonucleotide reductase and modulates the enzymatic activity, which is essential for DNA replication and repair. However, the role of RRM2 in lung adenocarcinoma (LUAD) remains unclear.

Methods: In this study, we explored the expression pattern and prognostic value of RRM2 in LUAD across TCGA, GEO, Oncomine, UALCAN, PrognoScan, and Kaplan-Meier Plotter, and confirmed its independent prognostic value via Cox analyses. LinkedOmics and GEPIA2 were applied to investigate co-expression and functional networks associated with RRM2. Besides, we used TIMER to assess the correlation between RRM2 and the main six types of tumor-infiltrating immune cells. Lastly, the correlations between immune signatures of immunomodulators, chemokines, and 28 tumor-infiltrating lymphocytes (TILs) and RRM2 were examined by tumor purity-corrected partial Spearman's rank correlation coefficient through TIMER portal.

Results:RRM2 was found upregulated in tumor tissues in TCGA-LUAD, and validated in multiple independent cohorts. Moreover, whether in TCGA or other cohorts, high RRM2 expression was found to be associated with poor survival. Cox analyses showed that high RRM2 expression was an independent risk factor for overall survival, disease-specific survival, and progression-free survival of LUAD. Functional network analysis suggested that RRM2 regulates RNA transport, oocyte meiosis, spliceosome, ribosome biogenesis in eukaryotes, and cellular senescence signaling through pathways involving multiple cancer-related kinases and E2F family. Also, RRM2 expression correlated with infiltrating levels of B cells, CD4+ T cells, and neutrophils. Subsequent analysis found that B cells and dendritic cells could predict the outcome of LUAD. B cells were identified as an independent risk factor among six types of immune cells through Cox analyses. At last, the correlation analysis showed RRM2 correlated with 67.68% (624/922) of the immune signatures we performed.

Conclusion: Our research showed that RRM2 could independently predict the prognosis of LUAD and was associated with immune infiltration. In particular, the tight relationship between RRM2 and B cell marker genes are the potential epicenter of the immune response and one of the critical factors affecting the prognosis. Our findings laid the foundation for further research on the immunomodulatory role of RRM2 in LUAD.

Identification of Potential Hub Genes and Therapeutic Drugs in Malignant Pleural Mesothelioma by Integrated Bioinformatics Analysis

INTRODUCTION

Malignant pleural mesothelioma (MPM) is closely linked to asbestos exposure and is an extremely aggressive tumor with poor prognosis.

OBJECTIVE

Our study aimed to elucidate hub genes and potential drugs in MPM by integrated bioinformatics analysis.

METHODS

GSE42977 was download from the Gene Expression Omnibus (GEO) database; the differentially expressed genes (DEGs) with adj.p value <0.05 and |logFC| ≥2 were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed by DAVID database. The STRING database was used to construct a protein-protein interaction network, and modules analysis and hub genes acquisition were performed by Cytoscape. The Gene Expression Profiling Interactive Analysis (GEPIA) database was used to assess the impact of hub genes on the prognosis of MPM patients. The Drug-Gene Interaction database (DGIdb) was used to select the related drugs.

RESULTS

A total of 169 upregulated and 70 downregulated DEGs were identified. These DEGs are enriched in the pathway of extracellular matrix-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and PPAR signaling pathway. Finally, 10 hub genes (CDC20, CDK1, UBE2C, TOP2A, CCNB2, NUSAP1, KIF20A, AURKA, CEP55, and ASPM) were identified, which are considered to be closely related to the poor prognosis of MPM. In addition, 119 related drugs that may have a therapeutic effect on MPM were filtered out.

CONCLUSION

These discovered genes and small-molecule drugs provide some new ideas for further research on MPM.

Inhibition of Rac1 reverses enzalutamide resistance in castration-resistant prostate cancer

Enzalutamide, an androgen receptor inhibitor, has been clinically approved for the treatment of metastatic castration-resistant prostate cancer (CRPC) in the United States. However, patients only benefit from enzalutamide for a short period of time as resistance may develop. Therefore, it is vital to develop a novel strategy to overcome enzalutamide resistance. Ras-related C3 botulinum toxin substrate 1 (Rac1), which is commonly upregulated in human cancer types, has been recognized as a key molecular component in tumorigenesis, invasion and metastasis. However, the role of Rac1 in enzalutamide-resistance in prostate cancer (PCa) remains unknown. In the present study, Rac1 was demonstrated to be upregulated in enzalutamide-resistant PCa cells, and Rac1 knockdown inhibited enzalutamide-resistant cell proliferation and colony formation. Western blotting results indicated that enzalutamide treatment downregulated the expression levels of JNK and activated transcription factor 2, as well as enhanced the Bax/Bcl-2 ratio and induced cleavage of poly-ADP ribose polymerase. Moreover, knockdown of Rac1 in MR49F cells significantly inhibited cell migration and invasion via the downregulation of Snail and the upregulation of E-cadherin. The results of a nude mouse xenograft tumor model using 22RV1 cells demonstrated that enzalutamide inhibited tumor growth after Rac1 knockdown dramatically, compared to vehicle and single treatment groups. Therefore, the present study provided novel evidence that Rac1 may serve a crucial role in enzalutamide resistance, and that targeting Rac1 may be a potential approach for the treatment of CRPC.

Identification of Candidate Genes Associated with Breast Cancer Prognosis

Breast cancer (BC) is the most malignant tumor in women. The molecular mechanisms underlying tumorigenesis still need to be further elucidated. It is necessary to investigate novel candidate genes involved in breast cancer progression and prognosis. In this study, we commit to explore candidate genes that associate with prognosis and therapy in BC by a comprehensive bioinformatic analysis. Four GEO datasets (GSE5764, GSE7904, GSE20711, and GSE29431) and the BC-related transcriptome data in TCGA database were downloaded and used to identify the differently expressed genes (DEGs). The function of DEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. The protein-protein interaction (PPI) network of DEGs was constructed to identify hub genes. Prognostic candidate genes were identified through survival analysis. In addition, potential therapeutic targets were identified by constructed gene-drug interaction network through Comparative Toxicogenomics Database. A total of 547 DEGs (302 up and 245 down) were identified. Three core-subnetwork and 25 hub genes were identified in PPI network. Seven genes (namely COL12A1, QPRT, MRPL13, KRT14, KRT15, LAMB3, and MYBPC1) were identified as crucial prognostic candidate genes, which significantly associated with breast cancer overall survival. Furthermore, two representative candidate genes (COL12A1 and LAMB3) were optionally chosen for verification by reverse transcription and quantitative real-time polymerase chain reaction (RT-PCR). What's more, the gene-drugs interaction analysis indicates several antitumor drugs that could affect the expression of these prognostic markers, such as doxorubicin, cisplatin, and tamoxifen. These results identified seven crucial candidate genes that may serve as prognosis biomarkers and novel therapeutic targets of breast cancer, which may facilitate further understanding the molecular pathogenesis and providing potential therapeutic strategies for BC.