Identification of hub genes in prostate cancer using robust rank aggregation and weighted gene co-expression network analysis

Bridging the Gap Between Imaging and Molecular Characterization: Current Understanding of Radiomics and Radiogenomics in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the sixth most common malignancy worldwide and the third leading cause of cancer-related deaths. Imaging plays a crucial role in the screening, diagnosis, and monitoring of HCC; however, the potential mechanism regarding phenotypes or molecular subtyping remains underexplored. Radiomics significantly expands the selection of features available by extracting quantitative features from imaging data. Radiogenomics bridges the gap between imaging and genetic/transcriptomic information by associating imaging features with critical genes and pathways, thereby providing biological annotations to these features. Despite challenges in interpreting these connections, assessing their universality, and considering the diversity in HCC etiology and genetic information across different populations, radiomics and radiogenomics offer new perspectives for precision treatment in HCC. This article provides an up-to-date summary of the advancements in radiomics and radiogenomics throughout the HCC care continuum, focusing on the clinical applications, advantages, and limitations of current techniques and offering prospects. Future research should aim to overcome these challenges to improve the prognosis of HCC patients and leverage imaging information for patient benefit.

Bioinformatics-based screening of hub genes for prostate cancer bone metastasis and analysis of immune infiltration

Bioinformatics analysis of genes and immune cells that influence prostate cancer (PCa) bone metastases. Using the gene expression omnibus database, we analyzed a PCa bone metastasis dataset. Differentially expressed genes were identified through the utilization of GEO2R and weighted gene co-expression network analysis. Gene set enrichment analysis software was used to identify important pathways. In addition to creating a network of protein-protein interactions, functional enrichment analyses were conducted using Kyoto encyclopedia of genes databases. To screen hub genes, Cytoscape software was used with the CytoHubba plug-in and performed mRNA and survival curve validation analysis of key genes using the cBioPortal website and GEPIA2 database. Immune infiltration analysis was performed using the CIBERSORTx website, and finally, immune cell correlation analysis was performed for key genes according to the TIMER database. A total of 197 PCa bone metastasis risk genes were screened, "G2M_CHECKPOINT" was significantly enriched in PCa bone metastasis samples according to genomic enrichment analysis. Based on the protein interactions network, we have identified 10 alternative hub genes, and 3 hub genes, CCNA2, NUSAP1, and PBK, were validated by the cBioPortal website and the GEPIA2 database. T cells regulatory and macrophages M0 may influence PCa to metastasize to bones, according to CIBERSORTx immune cell infiltration analysis. TIMER database analysis found different degrees of correlation between 3 key genes and major immune cells. PCa bone metastasis has been associated with CCNA2, NUSAP1, and PBK. T cells regulatory and macrophages (M0) may also be involved.

Aberrant DNA polymerase beta expression is associated with dysregulated tumor immune microenvironment and its prognostic value in gastric cancer

BACKGROUND

Gastric cancer is caused by different exogenous risk factors. Polymerase beta (POLB) is critical to repair oxidative and alkylating-induced DNA damage in genome maintenance. It is unknown whether overexpression of POLB genes in GC modulates tumor immunogenicity and plays a role in its prognostic value.

METHODS

RNA-Seq of GC data retrieved from TCGA and GEO database and patient survival were compared using Kaplan-Meier statistical test. The TIMER algorithm was used to calculate the abundance of tumor-infiltrating immune cells. Furthermore, ROC analysis was applied to evaluate the prognostic value of POLB overexpression.

RESULTS

Our data analysis of TCGA and GEO gastric cancer genomics datasets reveals that POLB overexpression is significantly associated with intestinal subtypes of stomach cancer. In addition, POLB overexpression is associated with low expression of innate immune signaling genes. In contrast, POLB-overexpressed tumor harbors high mutation frequency and MSI score. Furthermore, POLB-overexpressed tumor with high immune score exhibits a better prognosis. Interestingly, our ROC analysis results suggested that POLB overexpression has a potential for prognostic markers for stomach cancer.

CONCLUSIONS

Our analysis suggests that aberrant POLB overexpression in stomach cancer impacts the diverse aspects of tumor immune microenvironment. In addition, POLB might be a potential prognosis marker and/or an attractive target for immune-based therapy in GC. However, our observation still requires further experimental-based scientific validation studies.

Identification of the Key Gene DfCCoAOMT1 through Comparative Analysis of Lignification in Dendrocalamus farinosus XK4 and ZPX Bamboo Shoots during Cold Storage

Dendrocalamus farinosus bamboo shoots, a species with rich nutritional value, are important in Southwest China. Lignin is an important factor affecting the postharvest flavor quality of bamboo shoots; however, the underlying mechanism of lignin deposition in D. farinosus bamboo shoots during cold storage is still not fully understood. In this study, the mutant D. farinosus XK4 with low lignin content at 3.11% and the cultivated variety ZPX at 4.47% were used as experimental materials. The lignin content of D. farinosus XK4 and ZPX, as well as the gene expression differences between them, were compared and analyzed during cold storage using transcriptomic and physiological methods. Our analysis revealed several key genes and found that D. farinosus CCoAOMT1 plays a key role in the regulatory network of bamboo shoots during cold storage. Tobacco heterologous transformation experiments demonstrated that overexpression of DfCCoAOMT1 significantly increases lignin content. This study provides a novel foundation for future research aimed at improving the postharvest quality and flavor of D. farinosus bamboo shoots through targeted genetic manipulation during cold storage.

Identification of a metabolism-linked genomic signature for prognosis and immunotherapeutic efficiency in metastatic skin cutaneous melanoma

Metastatic skin cutaneous melanoma (MSCM) is the most rapidly progressing/invasive skin-based malignancy, with median survival rates of about 12 months. It appears that metabolic disorders accelerate disease progression. However, correlations between metabolism-linked genes (MRGs) and prognosis in MSCM are unclear, and potential mechanisms explaining the correlation are unknown. The Cancer Genome Atlas (TCGA) was utilized as a training set to develop a genomic signature based on the differentially expressed MRGs (DE-MRGs) between primary skin cutaneous melanoma (PSCM) and MSCM. The Gene Expression Omnibus (GEO) was utilized as a validation set to verify the effectiveness of genomic signature. In addition, a nomogram was established to predict overall survival based on genomic signature and other clinic-based characteristics. Moreover, this study investigated the correlations between genomic signature and tumor micro-environment (TME). This study established a genomic signature consisting of 3 genes (CD38, DHRS3, and TYRP1) and classified MSCM patients into low and high-risk cohorts based on the median risk scores of MSCM cases. It was discovered that cases in the high-risk cohort had significantly lower survival than cases in the low-risk cohort across all sets. Furthermore, a nomogram containing this genomic signature and clinic-based parameters was developed and demonstrated high efficiency in predicting MSCM case survival times. Interestingly, Gene Set Variation Analysis results indicated that the genomic signature was involved in immune-related physiological processes. In addition, this study discovered that risk scoring was negatively correlated with immune-based cellular infiltrations in the TME and critical immune-based checkpoint expression profiles, indicating that favorable prognosis may be influenced in part by immunologically protective micro-environments. A novel 3-genomic signature was found to be reliable for predicting MSCM outcomes and may facilitate personalized immunotherapy.

Mechanism and role of nuclear laminin B1 in cell senescence and malignant tumors

Nuclear lamin B1 (LMNB1) is a member of the nuclear lamin protein family. LMNB1 can maintain and ensure the stability of nuclear structure and influence the process of cell senescence by regulating chromatin distribution, DNA replication and transcription, gene expression, cell cycle, etc. In recent years, several studies have shown that the abnormal expression of LMNB1, a classical biomarker of cell senescence, is highly correlated with the progression of various malignant tumors; LMNB1 is therefore considered a new potential tumor marker and therapeutic target. However, the mechanism of action of LMNB1 is influenced by many factors, which are difficult to clarify at present. This article focuses on the recent progress in understanding the role of LMNB1 in cell senescence and malignant tumors and offers insights that could contribute to elucidating the mechanism of action of LMNB1 to provide a new direction for further research.

Uncovering the shared neuro-immune-related regulatory mechanisms between spinal cord injury and osteoarthritis

Adults with spinal cord injury (SCI), a destructive neurological injury, have a significantly higher incidence of osteoarthritis (OA), a highly prevalent chronic joint disorder. This study aimed to dissect the neuroimmune-related regulatory mechanisms of SCI and OA using bioinformatics analysis. Using microarray data from the Gene Expression Omnibus database, differentially expressed genes (DEGs) were screened between SCI and sham samples and between OA and control samples. Common DEGs were used to construct a protein-protein interaction (PPI) network. Weighted gene co-expression network analysis (WGCNA) was used to mine SCI- and OA-related modules. Shared miRNAs were identified, and target genes were predicted using the Human MicroRNA Disease Database (HMDD) database. A miRNA-gene-pathway regulatory network was constructed with overlapping genes, miRNAs, and significantly enriched pathways. Finally, the expression of the identified genes and miRNAs was verified using RT-qPCR. In both the SCI and OA groups, 185 common DEGs were identified, and three hub clusters were obtained from the PPI network. WGCNA revealed three SCI-related modules and two OA-related modules. There were 43 overlapping genes between the PPI network clusters and the WGCNA network modules. Seventeen miRNAs shared between patients with SCI and OA were identified. A regulatory network consisting of five genes, six miRNAs, and six signaling pathways was constructed. Upregulation of CD44, TGFBR1, CCR5, and IGF1, while lower levels of miR-125b-5p, miR-130a-3p, miR-16-5p, miR-204-5p, and miR-204-3p in both SCI and OA were successfully verified using RT-qPCR. Our study suggests that a miRNA-gene-pathway network is implicated in the neuroimmune-related regulatory mechanisms of SCI and OA. CD44, TGFBR1, CCR5, and IGF1, and their related miRNAs (miR-125b-5p, miR-130a-3p, miR-16-5p, miR-204-5p, and miR-204-3p) may serve as promising biomarkers and candidate therapeutic targets for SCI and OA.

Bioinformatics and Machine Learning Methods Identified MGST1 and QPCT as Novel Biomarkers for Severe Acute Pancreatitis

Severe acute pancreatitis (SAP) is a life-threatening gastrointestinal emergency. The study aimed to identify biomarkers and investigate molecular mechanisms of SAP. The GSE194331 dataset from GEO database was analyzed using bioinformatics. Differentially expressed genes (DEGs) associated with SAP were identified, and a protein-protein interaction network (PPI) was constructed. Machine learning algorithms were used to determine potential biomarkers. Gene set enrichment analysis (GSEA) explored molecular mechanisms. Immune cell infiltration were analyzed, and correlation between biomarker expression and immune cell infiltration was calculated. A competing endogenous RNA network (ceRNA) was constructed, and biomarker expression levels were quantified in clinical samples using RT-PCR. 1101 DEGs were found, with two modules most relevant to SAP. Potential biomarkers in peripheral blood samples were identified as glutathione S-transferase 1 (MGST1) and glutamyl peptidyltransferase (QPCT). GSEA revealed their association with immunoglobulin regulation, with QPCT potentially linked to pancreatic cancer development. Correlation between biomarkers and immune cell infiltration was demonstrated. A ceRNA network consisting of 39 nodes and 41 edges was constructed. Elevated expression levels of MGST1 and QPCT were verified in clinical samples. In conclusion, peripheral blood MGST1 and QPCT show promise as SAP biomarkers for diagnosis, providing targets for therapeutic intervention and contributing to SAP understanding.

Predicting prostate cancer recurrence: Introducing PCRPS, an advanced online web server

Background

Prostate cancer (PCa) is one of the leading causes of cancer death in men. About 30% of PCa will develop a biochemical recurrence (BCR) following initial treatment, which significantly contributes to prostate cancer-related deaths. In clinical practice, accurate prediction of PCa recurrence is crucial for making informed treatment decisions. However, the development of reliable models and biomarkers for predicting PCa recurrence remains a challenge. In this study, the aim is to establish an effective and reliable tool for predicting the recurrence of PCa.

Methods

We systematically screened and analyzed potential datasets to predict PCa recurrence. Through quality control analysis, low-quality datasets were removed. Using meta-analysis, differential expression analysis, and feature selection, we identified key genes associated with recurrence. We also evaluated 22 previously published signatures for PCa recurrence prediction. To assess prediction performance, we employed nine machine learning algorithms. We compared the predictive capabilities of models constructed using clinical variables, expression data, and their combinations. Subsequently, we implemented these machine learning models into a user-friendly web server freely accessible to all researchers.

Results

Based on transcriptomic data derived from eight multicenter studies consisting of 733 PCa patients, we screened 23 highly influential genes for predicting prostate cancer recurrence. These genes were used to construct the Prostate Cancer Recurrence Prediction Signature (PCRPS). By comparing with 22 published signatures and four important clinicopathological features, the PCRPS exhibited a robust and significantly improved predictive capability. Among the tested algorithms, Random Forest demonstrated the highest AUC value of 0.72 in predicting PCa recurrence in the testing dataset. To facilitate access and usage of these machine learning models by all researchers and clinicians, we also developed an online web server (https://urology1926.shinyapps.io/PCRPS/) where the PCRPS model can be freely utilized. The tool can also be used to (1) predict the PCa recurrence by clinical information or expression data with high accuracy. (2) provide the possibility of PCa recurrence by nine machine learning algorithms. Furthermore, using the PCRPS scores, we predicted the sensitivity of 22 drugs from GDSC2 and 95 drugs from CTRP2 to the samples. These predictions provide valuable insights into potential drug sensitivities related to the PCRPS score groups.

Conclusion

Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment for PCa.

The Effect of HMGB1 and HMGB2 on Transcriptional Regulation Differs in Neuroendocrine and Adenocarcinoma Models of Prostate Cancer

Human high-mobility group-B (HMGB) proteins regulate gene expression in prostate cancer (PCa), a leading cause of oncological death in men. Their role in aggressive PCa cancers, which do not respond to hormonal treatment, was analyzed. The effects of HMGB1 and HMGB2 silencing upon the expression of genes previously related to PCa were studied in the PCa cell line PC-3 (selected as a small cell neuroendocrine carcinoma, SCNC, PCa model not responding to hormonal treatment). A total of 72% of genes analyzed, using pre-designed primer panels, were affected. HMGB1 behaved mostly as a repressor, but HMGB2 as an activator. Changes in SERPINE1, CDK1, ZWINT, and FN1 expression were validated using qRT-PCR after HMGB1 silencing or overexpression in PC-3 and LNCaP (selected as an adenocarcinoma model of PCa responding to hormonal treatment) cell lines. Similarly, the regulatory role of HMGB2 upon SERPINE1, ZWINT, FN1, IGFPB3, and TYMS expression was validated, finding differences between cell lines. The correlation between the expression of HMGB1, HMGB2, and their targets was analyzed in PCa patient samples and also in PCa subgroups, classified as neuroendocrine positive or negative, in public databases. These results allow a better understanding of the role of HMGB proteins in PCa and contribute to find specific biomarkers for aggressive PCa.

Differentially expressed platelet activation-related genes in dogs with stage B2 myxomatous mitral valve disease

BACKGROUND

Peripheral blood carries a reservoir of mRNAs that regulate cardiac structure and function potential. Although it is well recognized that the typical symptoms of Myxomatous Mitral Valve Disease (MMVD) stage B2 are long-standing hemodynamic disorder and cardiac structure remodeling caused by mitral regurgitation, the transcriptomic alterations in blood from such dogs are not understood.

RESULTS

In the present study, comparative high-throughput transcriptomic profiling of blood was performed from normal control (NC) and naturally-occurring MMVD stage B2 (MMVD) dogs. Using Weighted Gene Co-expression Network Analyses (WGCNA), Gene Ontology (GO), and Kyoto Encyclopedia of Gene and Genomes (KEGG), we identified that the turquoise module was the most highly correlated with echocardiographic features and found 64 differentially expressed genes (DEGs) that were significantly enriched in platelet activation related pathways. Therefore, from the turquoise module, we selected five DEGs (MDM2, ROCK1, RIPK1, SNAP23, and ARHGAP35) that, according to real-time qPCR, exhibited significant enrichment in platelet activation related pathways for validation. The results showed that the blood transcriptional abundance of MDM2, ROCK1, RIPK1, and SNAP23 differed significantly (P < 0.01) between NC and MMVD dogs. On the other hand, Correlation Analysis revealed that MDM2, ROCK1, RIPK1, and SNAP23 genes negatively regulated the heart structure parameters, and followed the same trend as observed in WGCNA.

CONCLUSION

We screened four platelet activation related genes, MDM2, ROCK1, RIPK1, and SNAP23, which may be considered as the candidate biomarkers for the diagnosis of MMVD stage B2. These findings provided new insights into MMVD pathogenesis.

Construction and Analysis of ceRNA Networks Reveal the Key Genes Associated with Bovine Herpesvirus Type 1 Infection

Background

Virus infection can cause the changes of lncRNA expression levels to regulate the interaction between virus and host, but the relationship between BHV-1 infection and lncRNA has not been reported.

Methods

In this study, in order to reveal the molecular mechanism of RNA in BoHV-1 infection, the Madin-Darby bovine kidney (MDBK) cells were infected with BoHV-1, transcriptome sequencing were performed by next-generation sequencing at 18 h or 24 h or 33 h of viral infection and then based on the competitive endogenous RNA (ceRNA) theory, lncRNA-miRNA-mRNA networks were constructed using these high-throughput sequencing data. The network analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed for functional annotation and exploration of ncRNA ceRNAs in BoHV-1 infection.

Results

The results showed that 48 lncRNAs, 123 mRNAs and 20 miRNAs as differentially expressed genes, and the mitogen activated protein kinase (MAPK) pathway and calcium signaling pathway were significantly enriched in the ceRNA network. Some differentially expressed lncRNA genes were randomly selected for verification by RT-qPCR, and the results showed that their expression trend was consistent with the results of transcriptome sequencing data.

Conclusion

This study revealed that BoHV-1 infection can affect the expression of RNAs in MDBK cells and the regulation of ceRNA network to carry out corresponding biological functions in the host, but further experimental studies are still necessary to prove the hub genes function in ceRNA network and the molecular mechanism in BoHV-1 infection.

Potential regulatory genes of light induced anthocyanin accumulation in sweet cherry identified by combining transcriptome and metabolome analysis

Anthocyanins exist widely in various plant tissues and organs, and they play an important role in plant reproduction, disease resistance, stress resistance, and protection of human vision. Most fruit anthocyanins can be induced to accumulate by light. Here, we shaded the "Hong Deng" sweet cherry and performed an integrated analysis of its transcriptome and metabolome to explore the role of light in anthocyanin accumulation. The total anthocyanin content of the fruit and two of its anthocyanin components were significantly reduced after the shading. Transcriptome and metabolomics analysis revealed that PAL, 4CL, HCT, ANS and other structural genes of the anthocyanin pathway and cyanidin 3-O-glucoside, cyanidin 3-O-rutinoside, and other metabolites were significantly affected by shading. Weighted total gene network analysis and correlation analysis showed that the upstream and middle structural genes 4CL2, 4CL3, and HCT2 of anthocyanin biosynthesis may be the key genes affecting the anthocyanin content variations in fruits after light shading. Their expression levels may be regulated by transcription factors such as LBD, ERF4, NAC2, NAC3, FKF1, LHY, RVE1, and RVE2. This study revealed for the first time the possible role of LBD, FKF1, and other transcription factors in the light-induced anthocyanin accumulation of sweet cherry, thereby laying a preliminary foundation for further research on the role of light in anthocyanin accumulation of deep red fruit varieties and the genetic breeding of sweet cherry.

Centralspindlin proteins Pavarotti and Tumbleweed along with WASH regulate nuclear envelope budding

Nuclear envelope (NE) budding is a nuclear pore-independent nuclear export pathway, analogous to the egress of herpesviruses, and required for protein quality control, synapse development, and mitochondrial integrity. The physical formation of NE buds is dependent on the Wiskott-Aldrich Syndrome protein, Wash, its regulatory complex (SHRC), and Arp2/3, and requires Wash's actin nucleation activity. However, the machinery governing cargo recruitment and organization within the NE bud remains unknown. Here, we identify Pavarotti (Pav) and Tumbleweed (Tum) as new molecular components of NE budding. Pav and Tum interact directly with Wash and define a second nuclear Wash-containing complex required for NE budding. Interestingly, we find that the actin-bundling activity of Pav is required, suggesting a structural role in the physical and/or organizational aspects of NE buds. Thus, Pav and Tum are providing exciting new entry points into the physical machineries of this alternative nuclear export pathway for large cargos during cell differentiation and development.

New insights into light spectral quality inhibits the plasticity elongation of maize mesocotyl and coleoptile during seed germination

The plastic elongation of mesocotyl (MES) and coleoptile (COL), which can be repressed by light exposure, plays a vital role in maize seedling emergence and establishment under adverse environmental conditions. Understanding the molecular mechanisms of light-mediated repression of MES and COL elongation in maize will allow us to develop new strategies for genetic improvement of these two crucial traits in maize. A maize variety, Zheng58, was used to monitor the transcriptome and physiological changes in MES and COL in response to darkness, as well as red, blue, and white light. The elongation of MES and COL was significantly inhibited by light spectral quality in this order: blue light > red light > white light. Physiological analyses revealed that light-mediated inhibition of maize MES and COL elongation was closely related to the dynamics of phytohormones accumulation and lignin deposition in these tissues. In response to light exposure, the levels of indole-3-acetic acid, trans-zeatin, gibberellin 3, and abscisic acid levels significantly decreased in MES and COL; by contrast, the levels of jasmonic acid, salicylic acid, lignin, phenylalanine ammonia-lyase, and peroxidase enzyme activity significantly increased. Transcriptome analysis revealed multiple differentially expressed genes (DEGs) involved in circadian rhythm, phytohormone biosynthesis and signal transduction, cytoskeleton and cell wall organization, lignin biosynthesis, and starch and sucrose metabolism. These DEGs exhibited synergistic and antagonistic interactions, forming a complex network that regulated the light-mediated inhibition of MES and COL elongation. Additionally, gene co-expression network analysis revealed that 49 hub genes in one and 19 hub genes in two modules were significantly associated with the elongation plasticity of COL and MES, respectively. These findings enhance our knowledge of the light-regulated elongation mechanisms of MES and COL, and provide a theoretical foundation for developing elite maize varieties with improved abiotic stress resistance.

Endocrine Disrupting Chemicals Influence Hub Genes Associated with Aggressive Prostate Cancer

Prostate cancer (PCa) is one of the most frequently diagnosed cancers among men in the world. Its prevention has been limited because of an incomplete understanding of how environmental exposures to chemicals contribute to the molecular pathogenesis of aggressive PCa. Environmental exposures to endocrine-disrupting chemicals (EDCs) may mimic hormones involved in PCa development. This research aims to identify EDCs associated with PCa hub genes and/or transcription factors (TF) of these hub genes in addition to their protein-protein interaction (PPI) network. We are expanding upon the scope of our previous work, using six PCa microarray datasets, namely, GSE46602, GSE38241, GSE69223, GSE32571, GSE55945, and GSE26126, from the NCBI/GEO, to select differentially expressed genes based on |log2FC| (fold change) ≥ 1 and an adjusted p-value < 0.05. An integrated bioinformatics analysis was used for enrichment analysis (using DAVID.6.8, GO, KEGG, STRING, MCODE, CytoHubba, and GeneMANIA). Next, we validated the association of these PCa hub genes in RNA-seq PCa cases and controls from TCGA. The influence of environmental chemical exposures, including EDCs, was extrapolated using the chemical toxicogenomic database (CTD). A total of 369 overlapping DEGs were identified associated with biological processes, such as cancer pathways, cell division, response to estradiol, peptide hormone processing, and the p53 signaling pathway. Enrichment analysis revealed five up-regulated (NCAPG, MKI67, TPX2, CCNA2, CCNB1) and seven down-regulated (CDK1, CCNB2, AURKA, UBE2C, BUB1B, CENPF, RRM2) hub gene expressions. Expression levels of these hub genes were significant in PCa tissues with high Gleason scores ≥ 7. These identified hub genes influenced disease-free survival and overall survival of patients 60-80 years of age. The CTD studies showed 17 recognized EDCs that affect TFs (NFY, CETS1P54, OLF1, SRF, COMP1) that are known to bind to our PCa hub genes, namely, NCAPG, MKI67, CCNA2, CDK1, UBE2C, and CENPF. These validated differentially expressed hub genes can be potentially developed as molecular biomarkers with a systems perspective for risk assessment of a wide-ranging list of EDCs that may play overlapping and important role(s) in the prognosis of aggressive PCa.

Identification of key pathways and genes in nasopharyngeal carcinoma based on WGCNA

OBJECTIVE

We aim to identify the potential genes and signaling pathways associated with the nasopharyngeal carcinoma (NPC) prognosis using Weighted Gene Co-Expression Network Analysis (WGCNA).

METHODS

Gene Expression Omnibus (GEO) query was utilized to download two NPC mRNA microarray data. WGCNA was conducted on differentially expressed genes (DEGs) to obtain tumor-associated gene modules. Genes in core modules were intersected with DEGs for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis. GSE102349 dataset was devoted to identifying prognostic hub genes by survival analysis and the results were confirmed by quantitative polymerase chain reaction (qPCR).

RESULTS

Co-expression networks were built, and we detected 12 gene modules. The Brown module and Magenta module were extremely associated with NPC samples. GO functional analysis and KEGG pathway analysis was carried out to the genes in the Brown and Magenta modules. Our data indicated that DEGs in Brown module and Magenta module were correlated with the biological regulation, metabolic process, reproduction, and cellular proliferation. Twenty-six hub genes were obtained and were considered to be closely related to NPC. GSE102349 dataset was devoted to identifying prognostic hub genes by survival analysis. The expression of IL33, MPP3 and SLC16A7 in GSE102349 dataset was significantly correlated with the progression-free survival (PFS). The results of qPCR indicated a strong correlation between SLC16A7 expression and the overall survival (OS).

CONCLUSIONS

WGCNA contributed to the detection of gene modules and identification of hub genes and crucial genes. These crucial genes might be potential targets for pharmaceutic therapies with potential clinical significance.

Transcriptomics and co-expression network analysis revealing candidate genes for the laccase activity of Trametes gibbosa

BACKGROUND

Trametes gibbosa, which is a white-rot fungus of the Polyporaceae family found in the cold temperate zone, causes spongy white rot on wood. Laccase can oxidize benzene homologs and is one of the important oxidases for white rot fungi to degrade wood. However, the pathway of laccase synthesis in white rot fungi is unknown.

RESULTS

The peak value of laccase activity reached 135.75 U/min/L on the 9th day. For laccase activity and RNA-seq data, gene expression was segmented into 24 modules. Turquoise and blue modules had greater associations with laccase activity (positively 0.94 and negatively -0.86, respectively). For biology function, these genes were concentrated on the cell cycle, citrate cycle, nicotinate, and nicotinamide metabolism, succinate dehydrogenase activity, flavin adenine dinucleotide binding, and oxidoreductase activity which are highly related to the laccase synthetic pathway. Among them, gene_8826 (MW199767), gene_7458 (MW199766), gene_61 (MW199765), gene_1741 (MH257605), and gene_11087 (MK805159) were identified as central genes.

CONCLUSION

Laccase activity steadily increased in wood degradation. Laccase oxidation consumes oxygen to produce hydrogen ions and water during the degradation of wood. Some of the hydrogen ions produced can be combined by Flavin adenine dinucleotide (FAD) to form reduced Flavin dinucleotide (FADH2), which can be transmitted. Also, the fungus was starved of oxygen throughout fermentation, and the NADH and FADH2 are unable to transfer hydrogen under hypoxia, resulting in the inability of NAD and FAD to regenerate and inhibit the tricarboxylic acid cycle of cells. These key hub genes related to laccase activity play important roles in the molecular mechanisms of laccase synthesis for exploring industrial excellent strains.

Integrative bioinformatic analysis identified IFIT3 as a novel regulatory factor in psoriasis

Psoriasis is an autoimmune skin disease with poor prognosis. Currently, there is no cure for psoriasis and the pathogenic mechanism of psoriasis remains unclear. Our study aims to explore key regulators underlying psoriasis and potential targets for psoriasis treatment. RNA-seq data of psoriasis and normal tissues were extracted from Gene Expression Omnibus database to screen differentially expressed genes (DEGs). Weighted correlation network analysis (WGCNA) was conducted to identify key gene modules correlated with psoriasis. Enrichment analysis was used to characterize identified genes. The expression of identified genes was verified in a data set with various types of psoriasis lesion tissues and six psoriasis and healthy control tissues by quantitative polymerase chain reaction and immunohistochemistry assays. And the biological functions of IFIT3 in keratinocytes were determined by colony formation assays, Cell Counting Kit-8, and enzyme-linked immunosorbent assays. A total of 594 overlapped genes (370 upregulated and 224 downregulated) were selected as DEGs between psoriasis and normal tissues in three independent data sets. These genes were enriched in interferon-related pathway and cytokine-related pathway. Weighted correlation network analysis identified several gene modules that were associated with psoriasis. Overlapped genes between gene modules and DEGs were associated with interferon-related pathway and T cell activities. Among these genes, OAS1, USP18, and IFIT3 had higher expression levels in psoriasis vulgaris (PV) and nonpustular palmoplantar psoriasis (NPPP) tissues but not Palmoplantar Pustular Psoriasis (PPPP). Meanwhile, these results were confirmed in our independent psoriasis tissue cohort. And results of in vitro experiments showed that inhibition of IFIT3 significantly impaired the proliferation capacity and CXCL1, CCL20, IL-1β, and IL-6 secretion of keratinocytes. Our study identified key genes and pathways underlying the pathogenesis of psoriasis through the conduct of integrated analysis. OAS1, USP18, and IFIT3 could be potential targets for the treatment of psoriasis. IFIT3 can promote the proliferation and immune activation of keratinocytes and facilitates the development of psoriasis.

Identification of hub genes and transcription factor regulatory network for heart failure using RNA-seq data and robust rank aggregation analysis

Background

Heart failure (HF) is the end stage of various cardiovascular diseases with a high mortality rate. Novel diagnostic and therapeutic biomarkers for HF are urgently required. Our research aims to identify HF-related hub genes and regulatory networks using bioinformatics and validation assays.

Methods

Using four RNA-seq datasets in the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) of HF using Removal of Unwanted Variation from RNA-seq data (RUVSeq) and the robust rank aggregation (RRA) method. Then, hub genes were recognized using the STRING database and Cytoscape software with cytoHubba plug-in. Furthermore, reliable hub genes were validated by the GEO microarray datasets and quantitative reverse transcription polymerase chain reaction (qRT-PCR) using heart tissues from patients with HF and non-failing donors (NFDs). In addition, R packages “clusterProfiler” and “GSVA” were utilized for enrichment analysis. Moreover, the transcription factor (TF)–DEG regulatory network was constructed by Cytoscape and verified in a microarray dataset.

Results

A total of 201 robust DEGs were identified in patients with HF and NFDs. STRING and Cytoscape analysis recognized six hub genes, among which ASPN, COL1A1, and FMOD were confirmed as reliable hub genes through microarray datasets and qRT-PCR validation. Functional analysis showed that the DEGs and hub genes were enriched in T-cell-mediated immune response and myocardial glucose metabolism, which were closely associated with myocardial fibrosis. In addition, the TF–DEG regulatory network was constructed, and 13 significant TF–DEG pairs were finally identified.

Conclusion

Our study integrated different RNA-seq datasets using RUVSeq and the RRA method and identified ASPN, COL1A1, and FMOD as potential diagnostic biomarkers for HF. The results provide new insights into the underlying mechanisms and effective treatments of HF.

The Prognostic and Therapeutic Implications of the Chemoresistance Gene BIRC5 in Triple-Negative Breast Cancer

Chemoresistance affects TNBC patient treatment responses. Therefore, identifying the chemoresistant gene provides a new approach to understanding chemoresistance in TNBC. BIRC5 was examined in the current study as a tool for predicting the prognosis of TNBC patients and assisting in developing alternative therapies using online database tools. According to the examined studies, BIRC5 was highly expressed in 45 to 90% of TNBC patients. BIRC5 is not only abundantly expressed but also contributes to resistance to chemotherapy, anti-HER2 therapy, and radiotherapy. Patients with increased expression of BIRC5 had a median survival of 31.2 months compared to 85.8 months in low-expression counterparts (HR, 1.73; CI, 1.4−2.13; p = 2.5 × 10−7). The overall survival, disease-free survival, relapse-free survival, distant metastasis-free survival, and the complete pathological response of TNBC patients with high expression of BIRC5 who received any chemotherapy (Taxane, Ixabepilone, FAC, CMF, FEC, Anthracycline) and anti-HER2 therapy (Trastuzumab, Lapatinib) did not differ significantly from those patients receiving any other treatment. Data obtained indicate that the BIRC5 promoter region was substantially methylated, and hypermethylation was associated with higher BIRC5 mRNA expression (p < 0.05). The findings of this study outline the role of BIRC5 in chemotherapy-induced resistance of TNBC, further indicating that BIRC5 may serve as a promising prognostic biomarker that contributes to chemoresistance and could be a possible therapeutic target. Meanwhile, several in vitro studies show that flavonoids were highly effective in inhibiting BIRC5 in genetically diverse TNBC cells. Therefore, flavonoids would be a promising strategy for preventing and treating TNBC patients with the BIRC5 molecule.

Identification of candidate hub genes correlated with the pathogenesis, diagnosis, and prognosis of prostate cancer by integrated bioinformatics analysis

Background

Prostate cancer (PCa) has the second highest morbidity and mortality rates in men. Concurrently, novel diagnostic and prognostic biomarkers of PCa remain crucial.

Methods

This study utilized integrated bioinformatics method to identify and validate the potential hub genes with high diagnostic and prognostic value for PCa.

Results

Four Gene Expression Omnibus (GEO) datasets including 123 PCa samples and 76 normal samples were screened and a total of 368 differentially expressed genes (DEGs), including 120 up-regulated DEGs and 248 down-regulated DEGs, were identified. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were majorly enriched in focal adhesion, chemical carcinogenesis, drug metabolism, and cytochrome P450 pathways. Then, 11 hub genes were identified from the protein-protein interaction (PPI) network of the DEGs; 7 of the 11 genes showed the ability of distinguishing PCa from normal prostate based on receiver operating characteristic (ROC) curve analysis. And 5 of the 11 genes were correlated with clinical attributes. Lower CAV1, KRT5, SNAI2 and MYLK expression level were associated with higer Gleason score, advanced pathological T stage and N stage. Lower KRT5 and MYLK expression level were significantly correlated with poor disease-free survival, and lower KRT5 and PTGS2 expression level were significantly related to biochemical recurrence (BCR) status of PCa patients.

Conclusions

In conclusion, CAV1, KRT5, SNAI2, and MYLK show potential clinical diagnostic and prognostic value and could be used as novel candidate biomarkers and therapeutic targets for PCa.

Gene-Based Network Analysis Reveals Prognostic Biomarkers Implicated in Diabetic Tubulointerstitial Injury

Background

Diabetic nephropathy (DN), a significant cause of chronic kidney disease (CKD), is a devastating disease worldwide.

Objective

The aim of this study was to reveal crucial genes closely linked to the molecular mechanism of tubulointerstitial injury in DN.

Methods

The Gene Expression Omnibus (GEO) database was used to download the datasets. Based on this, a weighted gene coexpression network analysis (WGCNA) network was constructed to detect DN-related modules and hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichments were performed on the selected hub genes and modules. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed on the obtained gene signature.

Results

The WGCNA network was constructed based on 3019 genes, and nine gene coexpression modules were generated. A total of 57 genes, including 34 genes in the magenta module and 23 genes in the purple module, were adapted as hub genes. 61 significantly downregulated and 119 upregulated genes were screened as differentially expressed genes (DEGs). 25 overlapping genes between hub genes chosen from WGCNA and DEG were identified. Through LASSO analysis, a 9-gene signature may be a potential prognostic biomarker for DN. To further explore the potential mechanism of DN, the different immune cell infiltrations between tubulointerstitial samples of DN and healthy samples were estimated.

Conclusions

This bioinformatics study identified CX3CR1, HRG, LTF, TUBA1A, GADD45B, PDK4, CLIC5, NDNF, and SOCS2 as candidate biomarkers for the diagnosis of DN. Moreover, DN tends to own a higher proportion of memory B cell.

Methylation status of TK1 correlated with immune infiltrates in prostate cancer

TK1 is overexpressed in numerous cancers and is associated with to a poor prognosis. However, the relationship between methylation status of TK1 and Immune Infiltrates in Prostate Cancer (PCa) is unknown. The goal of this study was to use comprehensive bioinformatic analyses to elucidate the involvement relationship between methylation status of TK1 and Immune Infiltrates in PCa. TK1 mRNA expression and methylation data in PCa were investigated via GEPIA, TIMER, and UALCAN coupled with MEXPRESS data resources. We employed the LinkedOmics data resource to determine the signaling cascades linked to TK1 expression. Single-cell analysis was performed using the CancerSEA data resource. GeneMANIA and CancerSEA were used to analyze the correlation between TK1 and TK1 coexpressed genes. In addition, TIMER and TISIDB were adopted to assess tumor-invading immune cells and immunomodulators. CTD was utilized to detect the drugs acting on TK1. This study found that TK1 was overexpressed in PCa, and its contents were linked to tumor stage and prognosis. Genes co-expressed with TK1 were enriched in cascades involved in the ribosome, cell cycle, oxidative phosphorylation, DNA replication, oocyte meiosis, and the proteasome. The expression of TK1 along with its methylation status was found to be linked to tumor-invading immune cells, as well as PCa immunomodulators. We also examined the prospect of employing TK1 as a possible target for PCa therapy. This work provides the clinical value of TK1 hypermethylation in PCa and highlights new insights into its novel immunomodulatory functions.

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

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

Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma

Medulloblastoma (MB) is the most common pediatric malignant central nervous system tumor. Overall survival in MB depends on treatment tuning. There is aneed for biomarkers of residual disease and recurrence. We analyzed the proteome of waste cerebrospinal fluid (CSF) from extraventricular drainage (EVD) from six children bearing various subtypes of MB and six controls needing EVD insertion for unrelated causes. Samples included total CSF, microvesicles, exosomes, and proteins captured by combinatorial peptide ligand library (CPLL). Liquid chromatography-coupled tandem mass spectrometry proteomics identified 3560 proteins in CSF from control and MB patients, 2412 (67.7%) of which were overlapping, and 346 (9.7%) and 805 (22.6%) were exclusive. Multidimensional scaling analysis discriminated samples. The weighted gene co-expression network analysis (WGCNA) identified those modules functionally associated with the samples. A ranked core of 192 proteins allowed distinguishing between control and MB samples. Machine learning highlighted long-chain fatty acid transport protein 4 (SLC27A4) and laminin B-type (LMNB1) as proteins that maximized the discrimination between control and MB samples. Machine learning WGCNA and support vector machine learning were able to distinguish between MB versus non-tumor/hemorrhagic controls. The two potential protein biomarkers for the discrimination between control and MB may guide therapy and predict recurrences, improving the MB patients' quality of life.

Systems Genetic Identification of Mitochondrion-Associated Alzheimer's Disease Genes and Implications for Disease Risk Prediction

Cumulative evidence has revealed the association between mitochondrial dysfunction and Alzheimer’s disease (AD). Because the number of mitochondrial genes is very limited, the mitochondrial pathogenesis of AD must involve certain nuclear genes. In this study, we employed systems genetic methods to identify mitochondrion-associated nuclear genes that may participate in the pathogenesis of AD. First, we performed a mitochondrial genome-wide association study (MiWAS, n = 809) to identify mitochondrial single-nucleotide polymorphisms (MT-SNPs) associated with AD. Then, epistasis analysis was performed to examine interacting SNPs between the mitochondrial and nuclear genomes. Weighted co-expression network analysis (WGCNA) was applied to transcriptomic data from the same sample (n = 743) to identify AD-related gene modules, which were further enriched by mitochondrion-associated genes. Using hub genes derived from these modules, random forest models were constructed to predict AD risk in four independent datasets (n = 743, n = 542, n = 161, and n = 540). In total, 9 potentially significant MT-SNPs and 14,340 nominally significant MT-nuclear interactive SNPs were identified for AD, which were validated by functional analysis. A total of 6 mitochondrion-related modules involved in AD pathogenesis were found by WGCNA, from which 91 hub genes were screened and used to build AD risk prediction models. For the four independent datasets, these models perform better than those derived from AD genes identified by genome-wide association studies (GWASs) or differential expression analysis (DeLong’s test, p  <  0.05). Overall, through systems genetics analyses, mitochondrion-associated SNPs/genes with potential roles in AD pathogenesis were identified and preliminarily validated, illustrating the power of mitochondrial genetics in AD pathogenesis elucidation and risk prediction.

LMNB1, a potential marker for early prostate cancer progression

Although prostate cancer (PC) is the most common cancer among men in the Western world, there are no good biomarkers that can reliably differentiate between potentially aggressive and indolent PC. This leads to overtreatment, even for patients who can be managed conservatively. Previous studies have suggested that nuclear lamin proteins-especially lamin B1 (LMNB1)-play important roles in PC progression. However, the results of these studies are inconsistent. Here, we transfected the LMNB1 gene into the telomerase reverse transcriptase-immortalized benign prostatic epithelial cell line, EP156T to generate a LMNB1-overexpressing EP156T (LMN-EP156T) cell line with increased cellular proliferation. However, LMN-EP156T cells could neither form colonies in soft agar, nor establish subcutaneous growth or metastasis in the xenograft NOD/SCID mouse model. In addition, immunohistochemical staining of LMNB1 in PC specimens from 143 patients showed a statistically significant trend of stronger LMNB1 staining with higher Gleason scores. A univariate analysis of the clinicopathological parameters of 85 patients with PC who underwent radical prostatectomy revealed that pathological stage, resection margin, and extracapsular extension were significant predictors for biochemical recurrence (BCR). However, LMNB1 staining showed only a non-significant trend of association with BCR (high vs. low staining: hazard ratio (HR), 1.83; 95% confidence interval (CI), 0.98-3.41; P = 0.059). In multivariate analysis, only pathological stage was a significant independent predictor of BCR (pT3 vs. pT2: HR, 2.29; 95% CI, 1.18-4.43; P = 0.014). In summary, LMNB1 may play a role in the early steps of PC progression, and additional molecular alterations may be needed to confer full malignancy potential to initiated cells.

Identification of potential key molecules and signaling pathways for psoriasis based on weighted gene co-expression network analysis

BACKGROUND

Psoriasis is a chronic inflammatory skin disease, the pathogenesis of which is more complicated and often requires long-term treatment. In particular, moderate to severe psoriasis usually requires systemic treatment. Psoriasis is also associated with many diseases, such as cardiometabolic diseases, malignant tumors, infections, and mood disorders. Psoriasis can appear at any age, and lead to a substantial burden for individuals and society. At present, psoriasis is still a treatable, but incurable, disease. Previous studies have found that microRNAs (miRNAs) play an important regulatory role in the progression of various diseases. Currently, miRNAs studies in psoriasis and dermatology are relatively new. Therefore, the identification of key miRNAs in psoriasis is helpful to elucidate the molecular mechanism of psoriasis.

AIM

To identify key molecular markers and signaling pathways to provide potential basis for the treatment and management of psoriasis.

METHODS

The miRNA and mRNA data were obtained from the Gene Expression Omnibus database. Then, differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNAs (DEmiRNAs) were screened out by limma R package. Subsequently, DEmRNAs were analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomics functional enrichment. The “WGCNA” R package was used to analyze the co-expression network of all miRNAs. In addition, we constructed miRNA-mRNA regulatory networks based on identified hub miRNAs. Finally, in vitro validation was performed. All experimental procedures were approved by the ethics committee of Chinese PLA General Hospital (S2021-012-01).

RESULTS

A total of 639 DEmRNAs and 84 DEmiRNAs were identified. DEmRNAs screening criteria were adjusted P (adj. P) value < 0.01 and |logFoldChange| (|logFC|) > 1. DEmiRNAs screening criteria were adj. P value < 0.01 and |logFC| > 1.5. KEGG functional analysis demonstrated that DEmRNAs were significantly enriched in immune-related biological functions, for example, toll-like receptor signaling pathway, cytokine-cytokine receptor interaction, and chemokine signaling pathway. In weighted gene co-expression network analysis, turquoise module was the hub module. Moreover, 10 hub miRNAs were identified. Among these 10 hub miRNAs, only 8 hub miRNAs predicted the corresponding target mRNAs. 97 negatively regulated miRNA-mRNA pairs were involved in the miRNA-mRNA regulatory network, for example, hsa-miR-21-5p-claudin 8 (CLDN8), hsa-miR-30a-3p-interleukin-1B (IL-1B), and hsa-miR-181a-5p/hsa-miR-30c-2-3p-C-X-C motif chemokine ligand 9 (CXCL9). Real-time polymerase chain reaction results showed that IL-1B and CXCL9 were up-regulated and CLDN8 was down-regulated in psoriasis with statistically significant differences.

CONCLUSION

The identification of potential key molecular markers and signaling pathways provides potential research directions for further understanding the molecular mechanisms of psoriasis. This may also provide new research ideas for the prevention and treatment of psoriasis in the future.

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.

Identification of novel biomarkers in prostate cancer diagnosis and prognosis

Prostate cancer (PCa) is a common urinary malignancy. The lack of specific and sensitive biomarkers for the early diagnosis and prognosis of PCa makes it important to seek alternatives. R software was used to analyze the PCa expression profile from data sets in Gene Expression Omnibus. Core differential genes were identified by String and Cytoscape and further validated by Gene Expression Profiling Interactive Analysis (GEPIA) and The Human Protein Atlas (HPA). Gene Ontology analysis was done in the DIVID database and visualization analysis was conducted by Hiplot. Pathway enrichment was analyzed by IPA. To identify potential competitive endogenous RNAs (ceRNA) networks, the experimentally validated microRNA-target interactions database (miRTarBase), The Encyclopedia of RNA Interactomes (StarBase), lncBase, and GEPIA were used. The lncLocator was utilized to perform subcellular localization of long noncoding RNAs (lncRNAs). Both miRTarBase and StarBase were used to find the binding site of mRNAs-miRNAs and miRNAs-lncRNAs. Visualization of the ceRNA network was performed with Cytoscape. Nine genes closely related to the diagnosis and prognosis of PCa were obtained, including four identified biomarkers by HPA, CENPF, TPX2, TK1, and CCNB1, and five novel PCa biomarkers, RRM2, UBE2C, TOP2A, BIRC5, and ZWINT. Pathway analysis indicated that PCa carcinogenesis was highly correlated with liver fibrosis pathways, ILK signaling, and NRF2-mediated oxidative stress response. Two sets of ceRNA networks, BIRC5/hsa-miR-218-5p/NEAT1 and UBE2C/hsa-miR-483-3p/NEAT1 were found to be novel biomarkers for the identification of PCa. The quantitative real-time polymerase chain reaction results verified that UBE2C, BIRC5, and NEAT1 were upregulated and hsa-miR-218-5p and hsa-miR-483-3p were downregulated in human PCa cells compared with normal prostate epithelial cells. The novel identified biomarkers in this study would be valuable for the diagnosis and prognosis of PCa.

Identification of Robust and Key Differentially Expressed Genes during C2C12 Cell Myogenesis Based on Multiomics Data

Myogenesis is a central step in prenatal myofiber formation, postnatal myofiber hypertrophy, and muscle damage repair in adulthood. RNA-Seq technology has greatly helped reveal the molecular mechanism of myogenesis, but batch effects in different experiments inevitably lead to misinterpretation of differentially expressed genes (DEGs). We previously applied the robust rank aggregation (RRA) method to effectively circumvent batch effects across multiple RNA-Seq datasets from 3T3-L1 cells. Here, we also used the RRA method to integrate nine RNA-Seq datasets from C2C12 cells and obtained 3140 robust DEGs between myoblasts and myotubes, which were then validated with array expression profiles and H3K27ac signals. The upregulated robust DEGs were highly enriched in gene ontology (GO) terms related to muscle cell differentiation and development. Considering that the cooperative binding of transcription factors (TFs) to enhancers to regulate downstream gene expression is a classical epigenetic mechanism, differentially expressed TFs (DETFs) were screened, and potential novel myogenic factors (MAF, BCL6, and ESR1) with high connection degree in protein-protein interaction (PPI) network were presented. Moreover, KLF5 cooperatively binds with the three key myogenic factors (MYOD, MYOG, and MEF2D) in C2C12 cells. Motif analysis speculates that the binding of MYOD and MYOG is KLF5-independent, while MEF2D is KLF5-dependent. It was revealed that KLF5-binding sites could be exploited to filter redundant MYOD-, MYOG-, and MEF2D-binding sites to focus on key enhancers for myogenesis. Further functional annotation of KLF5-binding sites suggested that KLF5 may regulate myogenesis through the PI3K-AKt signaling pathway, Rap1 signaling pathway, and the Hippo signaling pathway. In general, our study provides a wealth of untapped candidate targets for myogenesis and contributes new insights into the core regulatory mechanisms of myogenesis relying on KLF5-binding signal.

Assessing the Potential Prognostic and Immunological Role of TK1 in Prostate Cancer

Background: It has been reported that thymidine kinase 1 (TK1) was up-regulated in multiple malignancies and participated in the regulation of tumor malignant behavior. However, its specific role in prostate cancer (PCa) remains unclear. Methods: TK1 expression in PCa patients and cell lines was identified via crossover analysis of the public datasets. A series of in vitro experiments and in vivo models was applied to investigate the function of TK1 in PCa. Functional enrichment analyses were further conducted to explore the underlying mechanism. Additionally, TISIDB was applied to explore the correlation between TK1 expression and tumor-infiltrating lymphocytes, immune subtypes, and immune regulatory factors. Results: TK1 expression was significantly up-regulated in PCa patients and cell lines. TK1 ablation inhibited tumor cell proliferation and migration potential, and in vivo experiments showed that TK1 inactivation can significantly restrain tumor growth. Functional enrichment analysis revealed TK1-related hub genes (AURKB, CCNB2, CDC20, CDCA5, CDK1, CENPA, CENPM, KIF2C, NDC80, NUF2, PLK1, SKA1, SPC25, ZWINT), and found that TK1 was closely involved in the regulation of cell cycle. Moreover, elevated mRNA expression of TK1 was related with higher Gleason score, higher clinical stage, higher pathological stage, higher lymph node stage, shorter overall survival, and DFS in PCa patients. Particularly, TK1 represented attenuated expression in C3 PCa and was related with infiltration of CD4+, CD8+ T cells, and dendritic cells as well as immunomodulator expression. Conclusion: Our study indicates that TK1 is a prognostic predictor correlated with poor outcomes of PCa patients, and for the first time represented that TK1 can promote the progression of PCa. Therefore, TK1 may be a potential diagnostic and prognostic biomarker, as well as a therapeutic target for PCa.

Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma

Background

A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA).

Methods

From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs), WGCNA was performed to identify gene modules and screen those associated with COAD progression. Then, via protein-protein interaction (PPI) network construction of module genes, hub genes were obtained, which were then subjected to the least absolute shrinkage and selection operator (LASSO) and Cox regression to build a hub gene-based prognostic scoring model. The receiver operating characteristic curve (ROC curve) was plotted for the optimal cutoff (OCO) of the risk score, based on which, patients were assigned to high or low-risk groups. Areas under the ROC curve (AUCs) were calculated, and model performance was visualized using Kaplan-Meier (KM) survival curves and verified in the external dataset GSE29621. Finally, the model's independent prognostic value was evaluated by univariate and multivariate Cox regression analyses, and a nomogram was built.

Results

Totally 2840 DEGs were screened from COAD dataset of TCGA, including 1401 upregulated ones and 1439 downregulated ones, which were divided into 10 modules by WGCNA. The eigenvalue of the black module was found to have a high correlation with COAD progression. PPI interaction networks were constructed for genes in the black module, and 34 hub genes were obtained by using the MCODE plug-in. A LASSO-Cox regression approach was utilized to analyze the hub genes, and a prognostic risk score model based on the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) was constructed. KM analysis identified shorter overall lower survival in the high-risk group. The model was verified to have favorable predictive ability through training set and validation set. The nomogram, composed of tumor node metastasis (TNM) staging and risk score, was of good predictability.

Conclusions

The COAD prognostic risk model constructed upon the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) can effectively predict the survival status of COAD patients.

Environmental Phenol and Paraben Exposure Risks and Their Potential Influence on the Gene Expression Involved in the Prognosis of Prostate Cancer

Prostate cancer (PCa) is one of the leading malignant tumors in US men. The lack of understanding of the molecular pathology on the risk of food supply chain exposures of environmental phenol (EP) and paraben (PB) chemicals limits the prevention, diagnosis, and treatment options. This research aims to utilize a risk assessment approach to demonstrate the association of EP and PB exposures detected in the urine samples along with PCa in US men (NHANES data 2005−2015). Further, we employ integrated bioinformatics to examine how EP and PB exposure influences the molecular pathways associated with the progression of PCa. The odds ratio, multiple regression model, and Pearson coefficients were used to evaluate goodness-of-fit analyses. The results demonstrated associations of EPs, PBs, and their metabolites, qualitative and quantitative variables, with PCa. The genes responsive to EP and PB exposures were identified using the Comparative Toxicogenomic Database (CTD). DAVID.6.8, GO, and KEGG enrichment analyses were used to delineate their roles in prostate carcinogenesis. The plug-in CytoHubba and MCODE completed identification of the hub genes in Cytoscape software for their roles in the PCa prognosis. It was then validated by using the UALCAN database by evaluating the expression levels and predictive values of the identified hub genes in prostate cancer prognosis using TCGA data. We demonstrate a significant association of higher levels of EPs and PBs in the urine samples, categorical and numerical confounders, with self-reported PCa cases. The higher expression levels of the hub genes (BUB1B, TOP2A, UBE2C, RRM2, and CENPF) in the aggressive stages (Gleason score > 8) of PCa tissues indicate their potential role(s) in the carcinogenic pathways. Our results present an innovative approach to extrapolate and validate hub genes responsive to the EPs and PBs, which may contribute to the severity of the disease prognosis, especially in the older population of US men.

Pan-cancer analysis identifies LMNB1 as a target to redress Th1/Th2 imbalance and enhance PARP inhibitor response in human cancers

Background

Emerging evidence suggests that LMNB1 is involved in the development of multiple cancer types. However, there is no study reporting the potential role of LMNB1 in a systematic pan-cancer manner.

Methods

The gene expression level and potential oncogenic roles of LMNB1 in The Cancer Genome Atlas (TCGA) database were analyzed with Tumor Immune Estimation Resource version 2 (TIMER2.0), Gene Expression Profiling Interactive Analysis version 2 (GEPIA2), UALCAN and Sangerbox tools. Pathway enrichment analysis was carried out to explore the possible mechanism of LMNB1 on tumorigenesis and tumor progression. The therapeutic effects of LMNB1 knockdown combined with PARP inhibition on human cancers were further investigated in vitro.

Results

LMNB1 upregulation is generally observed in the tumor tissues of most TCGA cancer types, and is verified in kidney renal clear cell carcinoma using clinical specimens of our institute. High level of LMNB1 expression usually predicts poor overall survival and disease free survival for patients with tumors. Mechanically, LMNB1 level is positively correlated with CD4+ Th2 cell infiltration and DNA homologous recombination repair gene expression. In vitro experiments reveal that targeting LMNB1 has a synergistic effect on prostate cancer with PARP inhibitor treatment.

Conclusions

LMNB1 is a biomarker of CD4+ Th2 cell infiltration and DNA homologous recombination repair in human cancers. Blockage of LMNB1 combined with PARP inhibitor treatment could be a promising therapeutic strategy for patients with cancers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02467-4.

Identification of biomarkers for early diagnosis of multiple myeloma by weighted gene co-expression network analysis and their clinical relevance

BACKGROUND

Multiple myeloma is an incurable hematologic malignancy, its early diagnosis is important. However, the biomarker for early diagnosis is limited; hence more need to be identified. The present study aimed to explore the easily tested new biomarker in multiple myeloma by weighted gene co-expression network analysis (WGCNA).

METHODS

Differentially expressed genes (DEGs) were screened using GSE47552. WGCNA was used to screen hub genes. Subsequently. Hub genes of multiple myeloma were obtained by intersection of DEGs and WGCNA. We used the T-test to screen highly expressed genes. Then, the diagnostic value of key genes was evaluated by the receiver operating characteristic (ROC) curve. Finally, expression levels of key genes were tested and proved by RT-PCR.

RESULTS

278 DEGs were screened by Limma package. Three modules were most significantly correlated with multiple myeloma. 238 key genes were screened after the intersection of WGCNA with DEGs. In addition, SNORNA is rarely studied in multiple myeloma, and ROC curve analysis in our prediction model showed that SNORA71A had a good prediction effect (p = 0.07). The expression of SNORA71A was increased in samples of multiple myeloma (P = 0.05). RT-PCR results showed that SNORA71A was upregulated in 51 patient specimens compared to the healthy group (P < 0.05). Linear correlation analysis showed that creatinine was positively correlated with SNORA71A (r = 0.49 P = 0.0002).

CONCLUSIONS

This study found that SNORA71A was up-regulated and associated with the clinical stages in multiple myeloma; it suggests that SNORA71A could be used as a novel biomarker for early diagnosis and a potential therapeutic target in multiple myeloma.

Resveratrol inhibits the progression of premature senescence partially by regulating v-rel avian reticuloendotheliosis viral oncogene homolog A (RELA) and sirtuin 1 (SIRT1)

Objective

To explore the effect of resveratrol in premature senescence and reveal its anti-premature senescence mechanisms through network pharmacology.

Methods

In this study, the H₂O₂-induced bone marrow mesenchymal stem cells (BMMSCs) premature senescence model is applied. Cell counting kit-8 assay, β-galactosidase staining and flow cytometry are conducted to detect the proliferation, senescence and apoptosis of BMMSCs. Bioinformatics analyses are used to screen and validate molecular targets of resveratrol acting on premature senescence. Dual-luciferase reporter assay is conducted to verify the interaction between v-rel avian reticuloendotheliosis viral oncogene homolog A (RELA) and sirtuin 1 (SIRT1). RT-qPCR and western blot are adopted to detect mRNA and protein levels of RELA, SIRT1, senescence-related genes and apoptosis-related genes.

Results

First, we proved that resveratrol alleviated the H₂O₂-induced senescence of BMMSCs. Then, bioinformatics analysis revealed that RELA was the downstream target of resveratrol and SIRT1 was the downstream target of RELA, respectively, involved in premature aging. RELA/SIRT1 may be the potential target of resveratrol for premature senescence. Notably, rescue experiments indicated that resveratrol inhibited premature senescence partially through targeting regulation RELA/SIRT1.

Conclusion

In our study, we confirm the functional role of the resveratrol-RELA- SIRT1 axis in the progression of premature senescence, which provides a latent target for premature senescence treatment.

Comprehensive bioinformatics analysis identifies LAPTM5 as a potential blood biomarker for hypertensive patients with left ventricular hypertrophy

Left ventricular hypertrophy (LVH) is a pivotal manifestation of hypertensive organ damage associated with an increased cardiovascular risk. However, early diagnostic biomarkers for assessing LVH in patients with hypertension (HT) remain indefinite. Here, multiple bioinformatics tools combined with an experimental verification strategy were used to identify blood biomarkers for hypertensive LVH. GSE74144 mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database to screen candidate biomarkers, which were used to perform weighted gene co-expression network analysis (WGCNA) and establish the least absolute shrinkage and selection operator (LASSO) regression model, combined with support vector machine-recursive feature elimination (SVM-RFE) algorithms. Finally, the potential blood biomarkers were verified in an animal model. A total of 142 hub genes in peripheral blood leukocytes were identified between HT with LVH and HT without LVH, which were mainly involved in the ATP metabolic process, oxidative phosphorylation, and mitochondrial structure and function. Notably, lysosomal associated transmembrane protein 5 (LAPTM5) was identified as the potential diagnostic marker of hypertensive LVH, which showed strong correlations with diverse marker sets of reactive oxygen species (ROS) and autophagy. RT-PCR validation of blood samples and cardiac magnetic resonance imaging (CMRI) showed that the expression of LAPTM5 was significantly higher in the HT with LVH model than in normal controls, LAPTM5 demonstrated a positive association with the left ventricle wall thickness as well as electrocardiogram (ECG) parameters widths of the QRS complex and QTc interval. In conclusion, LAPTM5 may be a potential biomarker for the diagnosis of LVH in patients with HT, and it can provide new insights for future studies on the occurrence and the molecular mechanisms of hypertensive LVH.

Comprehensive Analysis of Immune-Related Prognosis of TK1 in Hepatocellular Carcinoma

Increased expression of TK1 is associated with the progression of a variety of tumors. However, the relationship of TK1 expression with immune cell infiltration and its prognostic value in hepatocellular carcinoma (HCC) are still unknown. In this study the TCGA database was used to evaluate TK1 expression and its impact on survival in patients with HCC. Compared with normal tissue, TK1 in the liver tissue of patients with HCC was significantly up-regulated at both the mRNA and protein levels. Furthermore, TK1 expression was significantly related to pathological stage, tumor stage and lymph node metastasis, with high TK1 expression being an unfavorable prognostic factor for HCC. TK1 expression was also significantly associated with the infiltration of B cells, T cells, and dendritic cells in HCC. Single-cell sequencing analysis revealed that TK1 was associated with relatively large changes in T cells, especially gamma-delta T cells. A prognostic risk score based on TK1-related immune genes (CD40LG and TNFRSF4) was established using COX regression analysis. By integrating the immune-related risk score model with clinical features, a nomogram was constructed to predict the survival rate of HCC patients (1 year, 3-year and 5-year AUC of 0.782, 0.783 and 0.771, respectively). Knockdown of the target gene for TK1 was found to have significant anti-apoptosis and pro-proliferation effects on HepG2 cells. The level of TK1 in the serum and liver tissue of patients with HCC was significantly increased relative to healthy controls. These findings highlight the role of TK1 in the tumor immune response of HCC patients and in the proliferation and apoptosis of HepG2 cells. TK1 could therefore be a potential immunotherapy target for HCC patients, while the two immune genes related to TK1 (CD40LG And TNFRSF4) may be promising prognostic biomarkers in HCC.

Uncovering of Key Pathways and miRNAs for Intracranial Aneurysm Based on Weighted Gene Co-Expression Network Analysis

BACKGROUND

Intracranial aneurysm (IA) is a serious cerebrovascular disease. The identification of key regulatory genes can provide research directions for early diagnosis and treatment of IA.

METHODS

Initially, the miRNA and mRNA data were downloaded from the Gene Expression Omnibus database. Subsequently, the limma package in R was used to screen for differentially expressed genes. In order to investigate the function of the differentially expressed genes, a functional enrichment analysis was performed. Moreover, weighted gene co-expression network analysis (WGCNA) was performed to identify the hub module and hub miRNAs. The correlations between miRNAs and mRNAs were assessed by constructing miRNA-mRNA regulatory networks. In addition, in vitro validation was performed. Finally, diagnostic analysis and electronic expression verification were performed on the GSE122897 dataset.

RESULTS

In the present study, 955 differentially expressed mRNAs (DEmRNAs, 480 with increased and 475 with decreased expression) and 46 differentially expressed miRNAs (DEmiRNAs, 36 with increased and 10 with decreased expression) were identified. WGCNA demonstrated that the yellow module was the hub module. Moreover, 16 hub miRNAs were identified. A total of 1,124 negatively regulated miRNA-mRNA relationship pairs were identified. Functional analysis demonstrated that DEmRNAs in the targeted network were enriched in vascular smooth muscle contraction and focal adhesion pathways. In addition, the area under the curve of 16 hub miRNAs was >0.8. It is implied that 16 hub miRNAs may be used as potential diagnostic biomarkers of IA.

CONCLUSION

Hub miRNAs and key signaling pathways were identified by bioinformatics analysis. This evidence lays the foundation for understanding the underlying molecular mechanisms of IA and provided potential therapeutic targets for the treatment of this disease.

Network Analysis of Microarray Data

DNA microarrays are widely used to investigate gene expression. Even though the classical analysis of microarray data is based on the study of differentially expressed genes, it is well known that genes do not act individually. Network analysis can be applied to study association patterns of the genes in a biological system. Moreover, it finds wide application in differential coexpression analysis between different systems. Network based coexpression studies have for example been used in (complex) disease gene prioritization, disease subtyping, and patient stratification.In this chapter we provide an overview of the methods and tools used to create networks from microarray data and describe multiple methods on how to analyze a single network or a group of networks. The described methods range from topological metrics, functional group identification to data integration strategies, topological pathway analysis as well as graphical models.

A pan-cancer analysis based on weighted gene co-expression network analysis identifies the biomarker utility of lamin B1 in human tumors

Emerging evidence has revealed a relationship between lamin B1 (LMNB1) and several cancers such as cervical cancer, liver cancer, and prostate cancer. But no systematic pan-cancer analysis is available. Little is known about the clinical significance and biomarker utility of LMNB1. In this study, we first revealed the key role of LMNB1 in esophageal carcinoma (ESCA) through weighted gene co-expression network analysis (WGCNA) and disease-free survival (DFS) analysis. Based on this result and the datasets of the cancer genome atlas (TCGA), we explored the biomarker utility of LMNB1 across thirty-three tumors. We found that LMNB1 was highly expressed in most of the cancers and significant associations existed between LMNB1 expression and prognosis of cases of nearly half of the cancers. We also found that LMNB1 expression was associated with the infiltration level of Macrophages M1 and T cells CD4 memory activated in some cancers. Moreover, LMNB1 was mainly involved in the functional mechanisms of MRNA binding, olfactory transduction, and gene silencing. Our study first provides a pan-cancer study of LMNB1, thereby offering a relatively comprehensive understanding of the biomarker utility of LMNB1 across thirty-three tumors.

Identification of hub genes and construction of an mRNA-miRNA-lncRNA network of gastric carcinoma using integrated bioinformatics analysis

Background

Gastric carcinoma (GC) is one of the most common cancer globally. Despite its worldwide decline in incidence and mortality over the past decades, gastric cancer still has a poor prognosis. However, the key regulators driving this process and their exact mechanisms have not been thoroughly studied. This study aimed to identify hub genes to improve the prognostic prediction of GC and construct a messenger RNA-microRNA-long non-coding RNA(mRNA-miRNA-lncRNA) regulatory network.

Methods

The GSE66229 dataset, from the Gene Expression Omnibus (GEO) database, and The Cancer Genome Atlas (TCGA) database were used for the bioinformatic analysis. Differential gene expression analysis methods and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify a common set of differentially co-expressed genes in GC. The genes were validated using samples from TCGA database and further validation using the online tools GEPIA database and Kaplan-Meier(KM) plotter database. Gene set enrichment analysis(GSEA) was used to identify hub genes related to signaling pathways in GC. The RNAInter database and Cytoscape software were used to construct an mRNA-miRNA-lncRNA network.

Results

A total of 12 genes were identified as the common set of differentially co-expressed genes in GC. After verification of these genes, 3 hub genes, namely CTHRC1, FNDC1, and INHBA, were found to be upregulated in tumor and associated with poor GC patient survival. In addition, an mRNA-miRNA-lncRNA regulatory network was established, which included 12 lncRNAs, 5 miRNAs, and the 3 hub genes.

Conclusions

In summary, the identification of these hub genes and the establishment of the mRNA-miRNA-lncRNA regulatory network provide new insights into the underlying mechanisms of gastric carcinogenesis. In addition, the identified hub genes, CTHRC1, FNDC1, and INHBA, may serve as novel prognostic biomarkers and therapeutic targets.

FNDC3B and BPGM Are Involved in Human Papillomavirus-Mediated Carcinogenesis of Cervical Cancer

Human papillomavirus (HPV)-mediated cervical carcinogenesis is a multistep progressing from persistent infection, precancerous lesion to cervical cancer (CCa). Although molecular alterations driven by viral oncoproteins are necessary in cervical carcinogenesis, the key regulators behind the multistep process remain not well understood. It is pivotal to identify the key genes involved in the process for early diagnosis and treatment of this disease. Here we analyzed the mRNA expression profiles in cervical samples including normal, cervical intraepithelial neoplasia (CIN), and CCa. A co-expression network was constructed using weighted gene co-expression network analysis (WGCNA) to reveal the crucial modules in the dynamic process from HPV infection to CCa development. Furthermore, the differentially expressed genes (DEGs) that could distinguish all stages of progression of CCa were screened. The key genes involved in HPV-CCa were identified. It was found that the genes involved in DNA replication/repair and cell cycle were upregulated in CIN compared with normal control, and sustained in CCa, accompanied by substantial metabolic shifts. We found that upregulated fibronectin type III domain-containing 3B (FNDC3B) and downregulated bisphosphoglycerate mutase (BPGM) could differentiate all stages of CCa progression. In patients with CCa, a higher expression of FNDC3B or lower expression of BPGM was closely correlated with a shorter overall survival (OS) and disease-free survival (DFS). A receiver operating characteristic (ROC) analysis of CIN and CCa showed that FNDC3B had the highest sensitivity and specificity for predicting CCa development. Taken together, the current data showed that FNDC3B and BPGM were key genes involved in HPV-mediated transformation from normal epithelium to precancerous lesions and CCa.

[Identification and validation of hub genes in prostate cancer progression based on weighted gene co-expression network analysis]

OBJECTIVE

To identify the key hub genes in prostate cancer metastasis based on weighted gene co-expression network analysis (WGCNA) and verify the identified genes.

METHODS

Whole-genome chip data GSE6919 of prostate cancer study were analyzed using principal component analysis (PCA), and the differentially expressed genes (DEGs) were analyzed using R software. WGCNA was performed to construct a gene co-expression network for screening the key genes. TCGA database was used to explore the expressions of the DEGs and their association with the prognosis. To validate the results, we designed siRNA fragments targeting the metastasis-related gene HNRNPA2B1, and observed its effect on growth, apoptosis, clone formation, migration and invasion of prostate cancer cell lines using MTT assay, flow cytometry, clone formation assay, and Transwell assay.

RESULTS

PCA analysis showed obvious clustering of significant DEGs in metastatic cancer group. The modules obtained by WGCNA analysis in metastasis group involved stem cell differentiation, amino acid metabolism and immune response. Further screening of the genes identified 3 genes related with prostate cancer occurrence (BDH1, PAK4 and EXTL3) and another 3 with prostate cancer metastasis (NKTR, CTBP2 and HNRNPA2B1), which were shown to have differential expressions in TCGA database and were correlated with the patient's overall survival. In the cell experiment, PC3 and LNCap cells transfected with the siRNA fragment targeting HNRNPA2B1 showed obvious growth inhibition with increased cell apoptosis, lowered clone formation ability, and suppressed capacities for migration and invasion.

CONCLUSION

We identified 3 hub genes related with the occurrence (BDH1, PAK4 and EXTL3) and another 3 with metastasis of prostate cancer (NKTR, CTBP2 and HNRNPA2B1) using WGCNA, which provides a new approach for studying the regulatory mechanisms of prostate cancer.

GeneCoNet: A web application server for constructing cancer patient-specific gene correlation networks with prognostic gene pairs

BACKGROUND AND OBJECTIVE

Most prognostic gene signatures that have been known for cancer are either individual genes or combination of genes. Both individual genes and combination of genes do not provide information on gene-gene relations, and often have less prognostic significance than random genes associated with cell proliferation. Several methods for generating sample-specific gene networks have been proposed, but programs implementing the methods are not publicly available.

METHODS

We have developed a method that builds gene correlation networks specific to individual cancer patients and derives prognostic gene correlations from the networks. A gene correlation network specific to a patient is constructed by identifying gene-gene relations that are significantly different from normal samples. Prognostic gene pairs are obtained by carrying out the Cox proportional hazards regression and the log-rank test for every gene pair.

RESULTS

We built a web application server called GeneCoNet with thousands of tumor samples in TCGA. Given a tumor sample ID of TCGA, GeneCoNet dynamically constructs a gene correlation network specific to the sample as output. As an additional output, it provides information on prognostic gene correlations in the network. GeneCoNet found several prognostic gene correlations for six types of cancer, but there were no prognostic gene pairs common to multiple cancer types.

CONCLUSION

Extensive analysis of patient-specific gene correlation networks suggests that patients with a larger subnetwork of prognostic gene pairs have shorter survival time than the others and that patients with a subnetwork that contains more genes participating in prognostic gene pairs have shorter survival time than the others. GeneCoNet can be used as a valuable resource for generating gene correlation networks specific to individual patients and for identifying prognostic gene correlations. It is freely accessible at http://geneconet.inha.ac.kr.

Urinary sediment CCL5 messenger RNA as a potential prognostic biomarker of diabetic nephropathy

Background

Urinary sediment messenger RNAs (mRNAs) have been shown as novel biomarkers of kidney disease. We aimed to identify targeted urinary mRNAs in diabetic nephropathy (DN) based on bioinformatics analysis and clinical validation.

Methods

Microarray studies of DN were searched in the GEO database and Nephroseq platform. Gene modules negatively correlated with estimated glomerular filtration rate (eGFR) were identified by informatics methods. Hub genes were screened within the selected modules. In validation cohorts, a quantitative polymerase chain reaction assay was used to compare the expression levels of candidate mRNAs. Patients with renal biopsy–confirmed DN were then followed up for a median time of 21 months. End-stage renal disease (ESRD) was defined as the primary endpoint. Multivariate Cox proportional hazards regression was developed to evaluate the prognostic values of candidate mRNAs.

Results

Bioinformatics analysis revealed four chemokines (CCL5, CXCL1, CXLC6 and CXCL12) as candidate mRNAs negatively correlated with eGFR, of which CCL5 and CXCL1 mRNA levels were upregulated in the urinary sediment of patients with DN. In addition, urinary sediment mRNA of CXCL1 was negatively correlated with eGFR (r = −0.2275, P = 0.0301) and CCL5 level was negatively correlated with eGFR (r = −0.4388, P &lt; 0.0001) and positively correlated with urinary albumin:creatinine ratio (r = 0.2693, P = 0.0098); also, CCL5 and CXCL1 were upregulated in patients with severe renal interstitial fibrosis. Urinary sediment CCL5 mRNA was an independent predictor of ESRD [hazard ratio 1.350 (95% confidence interval 1.045–1.745)].

Conclusions

Urinary sediment CCL5 and CXCL1 mRNAs were upregulated in DN patients and associated with a decline in renal function and degree of renal interstitial fibrosis. Urinary sediment CCL5 mRNA could be used as a potential prognostic biomarker of DN.

Comprehensive Analysis of a Nine-Gene Signature Related to Tumor Microenvironment in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common malignancy, leading to more than 1 million related deaths each year. Due to low long-term survival rates, the exploration of molecular mechanisms underlying LUAD progression and novel prognostic predictors is urgently needed to improve LUAD treatment. In our study, cancer-specific differentially expressed genes (DEGs) were identified using the robust rank aggregation (RRA) method between tumor and normal tissues from six Gene Expression Omnibus databases (GSE43458, GSE62949, GSE68465, GSE115002, GSE116959, and GSE118370), followed by a selection of prognostic modules using weighted gene co-expression network analysis. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were applied to identify nine hub genes (CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6) that constructed a prognostic risk model. The RNA expressions of nine hub genes were validated in tumor and normal tissues by RNA-sequencing and single-cell RNA-sequencing, while immunohistochemistry staining from the Human Protein Atlas database showed consistent results in the protein levels. The risk model revealed that high-risk patients were associated with poor prognoses, including advanced stages and low survival rates. Furthermore, a multivariate Cox regression analysis suggested that the prognostic risk model could be an independent prognostic factor for LUAD patients. A nomogram that incorporated the signature and clinical features was additionally built for prognostic prediction. Moreover, the levels of hub genes were related to immune cell infiltration in LUAD microenvironments. A CMap analysis identified 13 small molecule drugs as potential agents based on the risk model for LUAD treatment. Thus, we identified a prognostic risk model including CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6 as novel biomarkers and validated their prognostic and predicted values for LUAD.