Multiple Bioinformatics Analyses Research Articles

A novel risk prediction model of pyroptosis-related genes for the prognosis and immunotherapy response of endometrial cancer.

This study aimstodevelopariskpredictionmodel of pyroptosis-related genes based on its impact on immunotherapy sensitivityof uterine corpus endometrial carcinoma (UCEC), one of the most common and threatening gynecological malignancies. Through multiple bioinformatics analysis, we obtained raw counts of RNA-sequencing data and corresponding clinical information related to UCEC from The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) to investigate the potential mechanisms of differentially expressed pyroptosis-related genes (DEPRGs), including the correlation between DEPRGs and prognosis, tumor immune microenvironment and the immunotherapy sensitivity of UCEC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis were used to figure out the functional differences. Furthermore, a mRNA-miRNA-lncRNA network was constructed to identify potentialimpact of pyroptosis ontumorprogression. In this study, we achieved six DEPRGs (CASP3, GPX4, GSDMD, NOD2, PYCARD and TIRAP) and constructed a 6-gene signature which classified UCEC patients in the TCGA cohort into a low-risk group or a high-risk group. Patients in the low-risk group showed significantly longer survival time (p=0.000373). Theriskscorewasalso confirmedasanindependentprognosticfactorcombining with the clinical characteristics. GO and KEGG functional analysis revealed the possible molecular mechanisms by which six DEPRGs influence anti-tumor immunity in UCEC patients. In addition, we found that two DEPRGs (GPX4, TIRAP) were not only significantly associated with tumor mutational burden (TMB) or microsatellite Instability (MSI), but also involved in regulating the number and function of CD8+ cells. Upon comprehensive bioinformatics analysis, it was concluded that pyroptosis-related genes (PRGs) could predict the prognosis of EC patients and be affected in modulating theanti-tumorimmune responsesforpatientswithEC.

Integrated Analysis of MATH-Based Subtypes Reveals a Novel Screening Strategy for Early-Stage Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is a frequently diagnosed cancer type, and many patients have already reached an advanced stage when diagnosed. Thus, it is crucial to develop a novel and efficient approach to diagnose and classify lung adenocarcinoma at an early stage. In our study, we combined in silico analysis and machine learning to develop a new five-gene–based diagnosis strategy, which was further verified in independent cohorts and in vitro experiments. Considering the heterogeneity in cancer, we used the MATH (mutant-allele tumor heterogeneity) algorithm to divide patients with early-stage LUAD into two groups (C1 and C2). Specifically, patients in C2 had lower intratumor heterogeneity and higher abundance of immune cells (including B cell, CD4 T cell, CD8 T cell, macrophage, dendritic cell, and neutrophil). In addition, patients in C2 had a higher likelihood of immunotherapy response and overall survival advantage than patients in C1. Combined drug sensitivity analysis (CTRP/PRISM/CMap/GDSC) revealed that BI-2536 might serve as a new therapeutic compound for patients in C1. In order to realize the application value of our study, we constructed the classifier (to classify early-stage LUAD patients into C1 or C2 groups) with multiple machine learning and bioinformatic analyses. The 21-gene–based classification model showed high accuracy and strong generalization ability, and it was verified in four independent validation cohorts. In summary, our research provided a new strategy for clinicians to make a quick preliminary assisting diagnosis of early-stage LUAD and make patient classification at the intratumor heterogeneity level. All data, codes, and study processes have been deposited to Github and are available online.

Identification of the pivotal role of SPP1 in kidney stone disease based on multiple bioinformatics analysis

BackgroundKidney stone disease (KSD) is a multifactorial disease involving both environmental and genetic factors, whose pathogenesis remains unclear. This study aims to explore the hub genes related to stone formation that could serve as potential therapeutic targets.MethodsBased on the GSE73680 dataset with 62 samples, differentially expressed genes (DEGs) between Randall’s plaque (RP) tissues and normal tissues were screened and weighted gene co-expression network analysis (WGCNA) was applied to identify key modules associated with KSD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological functions. The protein–protein interaction (PPI) network was constructed to identify hub genes. Meanwhile, CIBERSORT and ssGSEA analysis were used to estimate the infiltration level of the immune cells. The correlations between hub genes and immune infiltration levels were also investigated. Finally, the top hub gene was selected for further GSEA analysis.ResultsA total of 116 DEGs, including 73 up-regulated and 43 down-regulated genes, were screened in the dataset. The red module was identified as the key module correlated with KSD. 53 genes were obtained for functional enrichment analysis by taking the intersection of DEGs and genes in the red module. GO analysis showed that these genes were mainly involved in extracellular matrix organization (ECM) and extracellular structure organization, and others. KEGG analysis revealed that the pathways of aldosterone-regulated sodium reabsorption, cell adhesion molecules, arachidonic acid (AA) metabolism, and ECM-receptor interaction were enriched. Through PPI network construction, 30 hub genes were identified. CIBERSORT analysis revealed a significantly increased proportion of M0 macrophages, while ssGSEA revealed no significant differences. Among these hub genes, SPP1, LCN2, MMP7, MUC1, SCNN1A, CLU, SLP1, LAMC2, and CYSLTR2 were positively correlated with macrophages infiltration. GSEA analysis found that positive regulation of JNK activity was enriched in RP tissues with high SPP1 expression, while negative regulation of IL-1β production was enriched in the low-SPP1 subgroup.ConclusionsThere are 30 hub genes associated with KSD, among which SPP1 is the top hub gene with the most extensive links with other hub genes. SPP1 might play a pivotal role in the pathogenesis of KSD, which is expected to become a potential therapeutic target, while its interaction with macrophages in KSD needs further investigation.

Identification of Key Genes Associated with Endothelial Cell Dysfunction in Atherosclerosis Using Multiple Bioinformatics Tools.

Atherosclerosis is the most notable cardiovascular disease, the latter being the main cause of death globally. Endothelial cell dysfunction plays a major role in the pathogenesis of atherosclerosis. However, it is currently unclear which genes are involved between endothelial cell dysfunction and atherosclerosis. This study was aimed at identifying these genes. Based on the GSE83500 dataset, the quantification of endothelial cell function was conducted using single-sample gene set enrichment analysis; the coexpression modules were conducted using weighted correlation network analysis. After building module-trait relationships, tan and yellow modules were regarded as hub modules. 10 hub genes from each hub module were identified by the protein-protein interaction network analysis. The key genes (RAB5A, CTTN, ITGB1, and MMP9) were obtained by comparing the expression differences of the hub gene between atherosclerotic and normal groups from the GSE28829 and GSE43292 datasets, respectively. ROC analysis showed the diagnostic value of key genes. Moreover, the differential expression of key genes in normal and atherosclerotic aortic walls was verified. In vitro, we establish a model of ox-LDL-injured endothelial cells and transfect RAB5A overexpression and shRNA plasmids. The results showed that overexpression of RAB5A ameliorates the proliferation and migration function of ox-LDL-injured endothelial cells, including the ability of tubule formation. It was speculated that the interferon response, Notch signaling pathways, etc. were involved in this function of RAB5A by using gene set variation analysis. With the multiple bioinformatics analysis methods, we detected that yellow and tan modules are related to the abnormal proliferation and migration of endothelial cells associated with atherosclerosis. RAB5A, CTTN, ITGB1, and MMP9 can be used as potential targets for therapy and diagnostic markers. In vitro, overexpression of RAB5A can ameliorate the proliferation and migration function of ox-LDL-injured endothelial cells, and the possible molecules involved in this process were speculated.

Identification of potential blood biomarkers for early diagnosis of schizophrenia through RNA sequencing analysis

Schizophrenia (SCZ) is a highly heritable, polygenic complex mental disorder with imprecise diagnostic boundaries. Finding sensitive and specific novel biomarkers to improve the biological homogeneity of SCZ diagnosis is still one of the research hotspots. To identify the blood specific diagnostic biomarkers of SCZ, we performed RNA sequencing (RNA-seq) on 30 peripheral blood samples from 15 first-episode drug-naïve SCZ patients and 15 healthy controls (CTL). By performing multiple bioinformatics analysis algorithms based on RNA-seq data and microarray datasets, including differential expression genes (DEGs) analysis, WGCNA and CIBERSORT, we first identified 6 specific key genes (TOMM7, SNRPG, KRT1, AQP10, TMEM14B and CLEC12A) in SCZ. Moreover, we found that the proportions of lymphocyte, monocyte and neutrophils were significantly distinct in SCZ patients with CTL samples. Therefore, combining various features including age, sex and the novel blood biomarkers, we constructed the risk prediction model with three classifiers (RF: Random Forest; SVM: support vector machine; DT: decision tree) through repeated k-fold cross validation ensuring better generalizability. Finest result of Area under Receiver Operating Characteristic (AUROC) score of 0.91 was achieved by RF classifier and with a comparable good performance of AUROC 0.77 in external validation dataset. A lower AUROC of 0.63 was demonstrated when it was further applied to a Bipolar disorder (BPD) cohort. In conclusion, the study identified three peripheral core immunocytes and six key genes associated with the occurrence of SCZ, and further studies are required to test and validate these novel biomarkers for early diagnosis and treatment of SCZ.

GFPT2 pan-cancer analysis and its prognostic and tumor microenvironment associations*

Abstract Objective Glutamine fructose-6-phosphate transaminase 2 (GFPT2) is involved in a wide range of biological functions in human cancer. However, few studies have comprehensively analyzed the correlation between GFPT2 and different cancer prognoses and tumor microenvironments (TMEs). Methods We evaluated the expression level and prognostic value of GFPT2 using updated public databases and multiple comprehensive bioinformatics analysis methods and explored the relationship between GFPT2 expression and immune infiltration, immune neoantigens, tumor mutational burden (TMB), and microsatellite instability in pan-cancer. Results GFPT2 was highly expressed in five cancers. GFPT2 expression correlates with the prognosis of several cancers from The Cancer Genome Atlas (TCGA) and is significantly associated with stromal and immune scores in pan-cancer. High GFPT2 expression in BLCA, BRCA, and CHOL was positively correlated with the infiltration of immune cells, such as B-cells, CD4+ T, CD8+ T cells, dendritic cells, neutrophils, and macrophages. Conclusion High GFPT2 expression may modify the outcomes of patients with BLCA, BRCA, or CHOL cancers by increasing immune cell infiltration. These findings may provide insights for further investigation into GFPT2 as a potential target in pan-cancer.

Construction and validation of a metabolic gene-associated prognostic model for cervical carcinoma and the role on tumor microenvironment and immunity.

Metabolic reprogramming is a common feature of tumor cells and is associated with tumorigenesis and progression. In this study, a metabolic gene-associated prognostic model (MGPM) was constructed using multiple bioinformatics analysis methods in cervical carcinoma (CC) tissues from The Cancer Genome Atlas (TCGA) database, which comprised fifteen differentially expressed metabolic genes (DEMGs). Patients were divided into a high-risk group with shorter overall survival (OS) and a low-risk group with better survival. Receiver operating characteristic (ROC) curve analysis showed that the MGPM precisely predicted the 1-, 3- and 5-year survival of CC patients. As expected, MGPM exhibited a favorable prognostic significance in the training and testing datasets of TCGA. And the clinicopathological parameters including stage, tumor (T) and metastasis (M) classifications had significant differences in low- and high-risk groups, which further demonstrated the MGPM had a favorite prognostic prediction ability. Additionally, patients with low-ESTMATEScore had a shorter OS and when those combined with high-risk scores presented a worse prognosis. Through “CIBERSORT” package and Wilcoxon rank-sum test, patients in the high-risk group with a poor prognosis showed lower levels of infiltration of T cell CD8 (P < 0.001), T cells memory activated (P = 0.010) and mast cells resting (P < 0.001), and higher levels of mast cells activated (P < 0.001), and we also found these patients had a worse response for immunosuppressive therapy. These findings demonstrate that MGPM accurately predicts survival outcomes in CC patients, which will be helpful for further optimizing immunotherapies for cancer by reprogramming its cell metabolism.

Pan-Cancer Analyses Reveal Oncogenic and Immunological Role of Dickkopf-1 (DKK1).

WNT signaling pathway inhibitor Dickkopf-1 (DKK1) is related to cancer progression; however, its diagnostic and prognostic potential have not been investigated in a pan-cancer perspective. In this study, multiple bioinformatic analyses were conducted to evaluate therapeutic value of DKK1 in human cancers. The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project served as data resources. The Wilcoxon rank test was performed to evaluate the expression difference of DKK1 between cancer tissues and normal tissues. A Kaplan-Meier curve and Cox regression were used for prognosis evaluation. Single-sample gene set enrichment analysis (ssGSEA) was used to evaluate the association of DKK1 expression with the immune cell infiltration. The potential function of DKK1 was explored by STRING and clusterProfiler. We found that the expression level of DKK1 is significantly different in different cancer types. Importantly, we demonstrated that DKK1 is an independent risk factor in ESCA, LUAD, MESO, and STAD. Further analysis revealed that DKK1 had a large effect on the immune cell infiltration and markers of certain immune cells, such as Th1 and Th2 cells. PPI network analysis and further pathway enrichment analysis indicated that DKK1 was mainly involved in the WNT signaling pathway. Our findings suggested that DKK1 might serve as a marker of prognosis for certain cancers by affecting the WNT signaling pathway and tumor immune microenvironment.

Characterization of fungal communities on shared bicycles in Southwest China

BackgroundThe widespread use of shared bicycles has increased the demand and sanitary requirements for shared bicycles. Previous studies have identified potentially pathogenic bacteria on the surfaces of shared bicycles, but fungal communities have not been investigated.MethodsWe sampled shared-bicycle handles and saddles from five selected locations in a metropolis (Chengdu, China, n = 98) and used surrounding air deposition samples as controls (n = 12). Full-length ITS sequencing and multiple bioinformatic analyses were utilized to reveal fungal community structures and differences.ResultsAspergillus was dominant on both the handles and saddles of shared bicycles, and Alternaria and Cladosporium were the most abundant families in the air samples. Significant differences in fungal community structures were found among the three groups. The handle samples contained higher abundances of Aureobasidium melanogenum and Filobasidium magnum than the saddle and air samples. The saddle samples had a higher abundance of Cladosporium tenuissimum than the other two sample types (P < 0·05). A higher abundance of fungal animal pathogens on shared-bicycle surfaces than in air by FUNGuild (P < 0·05). Moreover, the co-occurrence network of fungi on handles was more stable than that on saddles.ConclusionThere were more potential pathogens, including Aspergillus pseudoglaucus, Aureobasidium melanogenum, Kazachstania pintolopesii, Filobasidium magnum, Candida tropicalis, and Malassezia globose were found on shared bicycles than in air, suggesting that hands should not contact mucous membrane after cycling, especially in susceptible individuals, and hygiene management of shared bicycles should be given more attention by relevant organizations worldwide.

TUBA1C is a Prognostic Marker in Low-grade Glioma and Correlates with Immune Cell Infiltration in the Tumor Microenvironment.

TUBA1C, a microtubule component, contributes to the development of several cancers. Our purpose was to study the expression of TUBA1C, its potential prognostic value, and its effects on the infiltration of immune cells of low-grade glioma (LGG). Through applying multiple bioinformatics analyses, we extracted and analyzed datasets from TCGA, TIMER, GTEx, GEPIA, and HPA to investigate the potential oncogenic mechanisms of TUBA1C, including the correlation between TUBA1C and prognosis, immune-checkpoints, tumor microenvironment (TME), and infiltration of immune cells in LGG. GO functional annotations and KEGG pathway analyses were further applied to investigate the potential action of TUBA1C in LGG. We revealed that the mRNA levels of TUBA1C were increased in LGG tumor tissues than in normal tissues. Additionally, TUBA1C was up-regulated in the grade III of LGG than in grade II. Moreover, we found that TUBA1C may be an independent prognostic factor of LGG, and high TUBA1C expression correlated to a poor prognosis of LGG. TUBA1C expression was positively associated with the infiltration of B cells, CD8 T+ cells, CD4+ T cells, macrophages, dendritic cells, and neutrophils. TUBA1C was also verified to be co-expressed with immune-related genes and immune-checkpoints. GO and KEGG pathway analyses indicated that TUBA1C may potentially regulate the pathogenesis of LGG through immune-related pathways, including chemokine pathway; JAK-STAT pathway; natural killer cell mediated cytotoxicity; T cell receptor pathway; leukocyte migration; negative regulation of immune system process; regulation of lymphocyte activation; T cell activation and other pathways. In conclusion, TUBA1C expression is increased in LGG and high TUAB1C expression is related to a poor prognosis. TUBA1C may influence tumor development by regulating the tumor-infiltrating cells in the TME. TUBA1C may be a potential target for immunotherapy.

Proteomics analysis reveals the correlation of programmed ROS-autophagy loop and dysregulated G1/S checkpoint with imatinib resistance in chronic myeloid leukemia cells.

Although tyrosine kinase inhibitors (TKIs), including imatinib, have greatly improved clinical treatment of patients with chronic myeloid leukemia (CML), drug resistance remains a major obstacle. Studies on the mechanisms underlying imatinib resistance and other alternative drugs are urgently needed. Liquid chromatography tandem mass spectrometry was applied to investigate the differences in proteomics and phosphoproteomics between K562 and K562/G (imatinib resistant K562). Multiple bioinformatics analyses were performed to unveil the differential signal pathways. CCK-8 was used to detect cell proliferation. Flow cytometry was performed to analyze reactive oxygen species (ROS), cell cycle, and cell apoptosis. Western blotting and quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) were used to observe the changes of ROS and autophagy associated with imatinib resistance in CML. Our results indicated that ROS-autophagy formed one negative feedback loop and was associated with imatinib resistance. Additionally, the limited-rate enzymes of serine synthesis pathway were escalated in K562/G, which could contribute to the increased cyclin-dependent kinases and cell proliferation index. According to phosphoproteomics data, K562/G cells exhibited abnormal phosphorylation of splicing signals. These results revealed that it could be one useful strategy to correct metabolism shift and oxidative stress, or moderately regulate autophagy. Future research should focus on the discovery of potential targets in ROS-autophagy loop.

Development of a KRAS-Associated Metabolic Risk Model for Prognostic Prediction in Pancreatic Cancer.

Background KRAS was reported to affect some metabolic genes and promote metabolic reprogramming in solid tumors. However, there was no comprehensive analysis to explore KRAS-associated metabolic signature or risk model for pancreatic cancer (PC). Methods In the current study, multiple bioinformatics analyses were used to identify differentially expressed metabolic genes based on KRAS mutation status in PC. Then, we developed and validated a prognostic risk model based on the selected KRAS-associated metabolic genes. Besides, we explored the association between the risk model and the metabolic characteristics as well as gemcitabine-associated chemoresistance in PC. Results 6 KRAS-associated metabolic genes (i.e., CYP2S1, GPX3, FTCD, ENPP2, UGT1A10, and XDH) were selected and enrolled to establish a prognostic risk model. The prognostic model had a high C-index of 0.733 for overall survival (OS) in TCGA pancreatic cancer database. The area under the curve (AUC) values of 1- and 3-year survival were both greater than 0.70. Then, the risk model was validated in two GEO datasets and also presented a satisfactory discrimination and calibration performance. Further, we found that the expression of some KRAS-driven glycolysis-associated genes (PKM, GLUT1, HK2, and LDHA) and gemcitabine-associated chemoresistance genes (i.e., CDA and RMM2) was significantly upregulated in high-risk PC patients evaluated by the risk model. Conclusions We constructed a risk model based on 6 KRAS-associated metabolic genes, which predicted patients' survival with high accuracy and reflected tumor metabolic characteristics and gemcitabine-associated chemoresistance in PC.

Identification of a Novel Cancer Stemness-Associated ceRNA Axis in Lung Adenocarcinoma via Stemness Indices Analysis.

The aim of this study was to identify a novel cancer stemness-related ceRNA regulatory axis in lung adenocarcinoma (LUAD) via weighted gene coexpression network analysis of a stemness index. The RNA sequencing expression profiles of 513 cancer samples and 60 normal samples were obtained from the TCGA database. Differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs) were identified with R software. Functional enrichment analysis was conducted using DAVID 6.8. The ceRNA network was constructed via multiple bioinformatics analyses, and the correlations between possible ceRNAs and prognosis were analyzed using Kaplan–Meier plots. WGCNA was then applied to distinguish key genes related to the mRNA expression-based stemness index (mRNAsi) in LUAD. After combining the weighted gene coexpression and ceRNA networks, a novel ceRNA regulatory axis was identified, and its biological functions were explored in vitro and vivo. In total, 1,825 DElncRNAs, 291 DEmiRNAs, and 3,742 DEmRNAs were identified. Functional enrichment analysis revealed that the DEmRNAs might be associated with LUAD onset and progression. The ceRNA network was constructed with 14 lncRNAs, 10 miRNAs, and 52 mRNAs. Kaplan–Meier analysis identified 2 DEmiRNAs, 5 DElncRNAs, and 41 DEmRNAs with remarkable prognostic power. One gene (MFAP4) in the ceRNA network was found to be closely related to mRNAsi by using WGCNA. Functional investigation further confirmed that the C8orf34-as1/miR-671-5p/MFAP4 regulatory axis has important functions in LUAD cell migration and stemness. This study provides a deeper understanding of the lncRNA–miRNA–mRNA ceRNA network and, more importantly, reveals a novel ceRNA regulatory axis, which may provide new insights into novel molecular therapeutic targets for inhibiting LUAD stem characteristics.

Phosphoproteomics Identifies Significant Biomarkers Associated with the Proliferation and Metastasis of Prostate Cancer.

The spider peptide toxins HNTX-III and JZTX-I are a specific inhibitor and activator of TTX-S VGSCs, respectively. They play important roles in regulating MAT-LyLu cell metastasis in prostate cancer. In order to identify key biomarkers involved in the regulation of MAT-LyLu cell metastasis, iTRAQ-based quantitative phosphoproteomics analysis was performed on cells treated with HNTX-III, JZTX-I and blank. A total of 554 unique phosphorylated proteins and 1779 distinct phosphorylated proteins were identified, while 55 and 36 phosphorylated proteins were identified as differentially expressed proteins in HNTX-III and JZTX-I treated groups compared with control groups. Multiple bioinformatics analysis based on quantitative phosphoproteomics data suggested that the differentially expressed phosphorylated proteins and peptides were significantly associated with the migration and invasion of prostate tumors. Specifically, the toxins HNTX-III and JZTX-I have opposite effects on tumor formation and metastasis by regulating the expression and phosphorylation level of causal proteins. Herein, we highlighted three key proteins EEF2, U2AF2 and FLNC which were down-regulated in HNTX-III treated cells and up-regulated in JZTX-I treated cells. They played significant roles in cancer related physiological and pathological processes. The differentially expressed phosphorylated proteins identified in this study may serve as potential biomarkers for precision medicine for prostate cancer in the near future.

SLC39A10 Upregulation Predicts Poor Prognosis, Promotes Proliferation and Migration, and Correlates with Immune Infiltration in Hepatocellular Carcinoma.

BackgroundRecent evidence has shown that Solute Carrier Family 39 Member 10 (SLC39A10) promoted tumor progression in several cancer types. The study intended to explore the expression and function of SLC39A10 in hepatocellular carcinoma (HCC).MethodsMultiple bioinformatics analyses were used to evaluate SLC39A10 expression and potential role in HCC. Quantitative real-time polymerase chain reaction and immunohistochemistry were used to confirm SLC39A10 expression. Intro studies were performed to assess the effects of SLC39A10 on HCC cells proliferation and migration. Furthermore, flow cytometry was conducted to identify its specific function in apoptosis of HCC.ResultsSLC39A10 was significantly over-expressed in HCC samples from both bioinformatic databases and our cohort. Survival analyses suggested patients with high expression of SLC39A10 had poor overall survival and disease-free survival (P-value <0.01). Further, the expression of SLC39A10 was positively correlated with tumor-infiltrating lymphocytes and some immune checkpoints like CTLA4, TIM3 and TGFB1. In HCC cell lines, SLC39A10 knockdown inhibited cells proliferation and migration, but promoted apoptosis.ConclusionAn increased SLC39A10 expression was found and served as an unfavorable indicator of survival in HCC. Further studies suggested SLC39A10 promotes tumor aggressiveness and may provide a novel target for HCC therapy.

Identification of solute-carrier family 27A molecules (SCL27As) as a potential biomarker of ovarian cancer based on bioinformatics and experiments

BackgroundOvarian cancer is one of the 3 major gynecological malignancies with high mortality, poor prognosis, and lack of specific diagnostic and prognostic markers. Solute-carrier family 27A molecules (SCL27As) play a crucial role in multiple malignant tumors via the regulation of long-chain fatty acid uptake and subsequent regulation of lipid metabolism. To date, the specific mechanisms and roles of SCL27As in epithelial ovarian cancer (EOC) have remained unclear.MethodsThe Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the Kaplan-Meier plotter were used to explore the differential expression and the prognostic value of SCL27As in EOC. The expression of SCL27A6 in 20 normal ovarian tissues and 120 ovarian cancer tissues was detected by immunohistochemistry (IHC). Cell Counting Kit-8 (CCK8) assay and colony-forming experiments were conducted to evaluate the role of SCL27A6 in the proliferation of ovarian cancer cells, so as to verify the clinical application value of SCL27A6 in the diagnosis and prognosis of ovarian cancer. We extracted the data of SCL27A6 for multiple bioinformatics analysis to identify the potential regulatory mechanism of SLC27A6.ResultsThe expression levels of SLC27A1 and SLC27A6 were significantly decreased in ovarian cancer tissues. Prognostic analysis showed that SLC27A2, SLC27A4, SLC27A5, and SLC27A6 expression levels were significantly correlated with overall survival (OS) in EOC patients. Moreover, the expression of the SLC27A6 protein was decreased in EOC tissues, which was related to the prognosis. Additionally, knocking down the expression of SLC27A6 could significantly enhance the malignant biological behavior of ovarian cancer cells. The SLC27A6 gene may be involved in the proteasome, cell cycle, Hippo signaling pathway, and so on.ConclusionsThis study revealed the abnormal expression and prognostic value of SLC27As in EOC. In addition, it was highlighted that SLC27A6 may be a novel biomarker for the diagnosis and prognostication of EOC patients.

Evaluation of Oncogene NUP37 as a Potential Novel Biomarker in Breast Cancer.

PurposeThere is an urgent need to identify oncogenes that may be beneficial to diagnose and develop target therapy for breast cancer.MethodsBased on the GEO database, DECenter was used to screen the differentially overexpressed genes in breast cancer samples. Search Tool for the Retrieval of Interacting Genes and Cytoscape were performed to construct the PPI network to predict the hub gene. Functional and pathway enrichment were performed based on GO analysis. GEO2R, Oncomine, human tissue microarray staining, and western blot were applied to confirm the expression of NUP37. The association between NUP37 expression and prognosis in patients with breast cancer were assessed using the Kaplan–Meier plotter online tool and OncoLnc. siRNAs were used to knock down NUP37 and evaluate proliferation, migration, and stemness in breast cancer cells.ResultsWe found that 138 genes were differentially upregulated in breast cancer samples, mainly comprising components of the nucleus and involved in the cell cycle process. NUP37 was identified as a hub gene that is upregulated in breast cancer patients related to a significantly worse survival rate. Furthermore, we confirmed that the downregulation of NUP37 in breast cancer cells results in the inhibition of cell growth, migration, and stemness.ConclusionsHigh expression of NUP37 in breast cancer patients is associated with a poorer prognosis and promotion of cell growth, migration, and stemness. The multiple bioinformatics and experimental analysis help provide a comprehensive understanding of the roles of NUP37 as a potential marker for diagnosis and prognosis and as a novel therapeutic target in breast cancer.

Biallelic variants in CPAMD8 are associated with primary open-angle glaucoma and primary angle-closure glaucoma

Background/AimsThis study aims to assess the contribution of biallelic CPAMD8 variants in patients with different forms of glaucoma, especially primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG), based on...

Bioinformatics identification of hub genes and signaling pathways regulated by intravenous immunoglobulin treatment in acute Kawasaki disease.

Kawasaki disease (KD) is an acute, self-limiting form of vasculitis commonly encountered in infants and young children. Intravenous immunoglobulin (IVIG) is the primary drug used for the treatment of KD, which may significantly reduce the occurrence of coronary artery lesions. However, the specific molecular profile changes of KD caused by IVIG treatment have remained elusive and require further research. The present study was designed to identify key genes, pathways and immune cells affected by IVIG treatment using multiple bioinformatics analysis methods. The results suggested that myeloid cells and neutrophils were affected by IVIG treatment. Kyoto Encyclopedia of Genes and Genomes pathway analysis identified that hematopoietic cell lineages and osteoclast differentiation may have an important role in the mechanism of action of IVIG treatment. Immune cell analysis indicated that the levels of monocytes, M1 macrophages, neutrophils and platelets were markedly changed in patients with KD after vs. prior to IVIG treatment. The key upregulated genes, including ZW10 interacting kinetochore protein, GINS complex subunit 1 and microRNA-30b-3p in whole blood cells of patients with KD following treatment with IVIG indicated that these IVIG-targeted molecules may have important roles in KD. In addition, these genes were further examined by literature review and indicated to be involved in cell proliferation, apoptosis and virus-related immune response in patients with KD. Therefore, the present results may provide novel insight into the mechanisms of action of IVIG treatment for KD.

Identification of Hub Genes and Their Correlation With Immune Infiltration Cells in Hepatocellular Carcinoma Based on GEO and TCGA Databases.

Hepatocellular carcinoma (HCC) is a primary liver cancer with extremely high mortality in worldwide. HCC is hard to diagnose and has a poor prognosis due to the less understanding of the molecular pathological mechanisms and the regulation mechanism on immune cell infiltration during hepatocarcinogenesis. Herein, by performing multiple bioinformatics analysis methods, including the RobustRankAggreg (RRA) rank analysis, weighted gene co-expression network analysis (WGCNA), and a devolution algorithm (CIBERSORT), we first identified 14 hub genes (NDC80, DLGAP5, BUB1B, KIF20A, KIF2C, KIF11, NCAPG, NUSAP1, PBK, ASPM, FOXM1, TPX2, UBE2C, and PRC1) in HCC, whose expression levels were significantly up-regulated and negatively correlated with overall survival time. Moreover, we found that the expression of these hub genes was significantly positively correlated with immune infiltration cells, including regulatory T cells (Treg), T follicular helper (TFH) cells, macrophages M0, but negatively correlated with immune infiltration cells including monocytes. Among these hub genes, KIF2C and UBE2C showed the most significant correlation and were associated with immune cell infiltration in HCC, which was speculated as the potential prognostic biomarker for guiding immunotherapy.