Construction of co-expression modules related to survival by WGCNA and identification of potential prognostic biomarkers in glioblastoma

Role of cell division cycle-associated proteins in regulating cell cycle and promoting tumor progression

The cell division cycle-associated protein (CDCA) family is important in regulating cell division. High CDCA expression is significantly linked to tumor development. This review summarizes clinical and basic studies on CDCAs conducted in recent decades. Furthermore, it systematically introduces the molecular expression and function, key mechanisms, cell cycle regulation, and roles of CDCAs in tumor development, cell proliferation, drug resistance, invasion, and metastasis. Additionally, it presents the latest research on tumor diagnosis, prognosis, and treatment targeting CDCAs. These findings are pivotal for further in-depth studies on the role of CDCAs in promoting tumor development and provide theoretical support for their application as new anti-tumor targets.

KIF2C/MCAK a prognostic biomarker and its oncogenic potential in malignant progression, and prognosis of cancer patients: a systematic review and meta-analysis as biomarker

KIF2C/MCAK (KIF2C) is the most well-characterized member of the kinesin-13 family, which is critical in the regulation of microtubule (MT) dynamics during mitosis, as well as interphase. This systematic review briefly describes the important structural elements of KIF2C, its regulation by multiple molecular mechanisms, and its broad cellular functions. Furthermore, it systematically summarizes its oncogenic potential in malignant progression and performs a meta-analysis of its prognostic value in cancer patients. KIF2C was shown to be involved in multiple crucial cellular processes including cell migration and invasion, DNA repair, senescence induction and immune modulation, which are all known to be critical during the development of malignant tumors. Indeed, an increasing number of publications indicate that KIF2C is aberrantly expressed in multiple cancer entities. Consequently, we have highlighted its involvement in at least five hallmarks of cancer, namely: genome instability, resisting cell death, activating invasion and metastasis, avoiding immune destruction and cellular senescence. This was followed by a systematic search of KIF2C/MCAK's expression in various malignant tumor entities and its correlation with clinicopathologic features. Available data were pooled into multiple weighted meta-analyses for the correlation between KIF2Chigh protein or gene expression and the overall survival in breast cancer, non-small cell lung cancer and hepatocellular carcinoma patients. Furthermore, high expression of KIF2C was correlated to disease-free survival of hepatocellular carcinoma. All meta-analyses showed poor prognosis for cancer patients with KIF2Chigh expression, associated with a decreased overall survival and reduced disease-free survival, indicating KIF2C's oncogenic potential in malignant progression and as a prognostic marker. This work delineated the promising research perspective of KIF2C with modern in vivo and in vitro technologies to further decipher the function of KIF2C in malignant tumor development and progression. This might help to establish KIF2C as a biomarker for the diagnosis or evaluation of at least three cancer entities.

CWGCNA: an R package to perform causal inference from the WGCNA framework

WGCNA (weighted gene co-expression network analysis) is a very useful tool for identifying co-expressed gene modules and detecting their correlations to phenotypic traits. Here, we explored more possibilities about it and developed the R package CWGCNA (causal WGCNA), which works from the traditional WGCNA pipeline but mines more information. It couples a mediation model with WGCNA, so the causal relationships among WGCNA modules, module features, and phenotypes can be found, demonstrating whether the module change causes the phenotype change or vice versa. After that, when annotating the module gene set functions, it uses a novel network-based method, considering the modules' topological structures and capturing their influence on the gene set functions. In addition to conducting these biological explorations, CWGCNA also contains a machine learning section to perform clustering and classification on multi-omics data, given the increasing popularity of this data type. Some basic functions, such as differential feature identification, are also available in our package. Its effectiveness is proved by the performance on three single or multi-omics datasets, showing better performance than existing methods. CWGCNA is available at: https://github.com/yuabrahamliu/CWGCNA.

Single-cell atlas reveals the immunosuppressive microenvironment and Treg cells landscapes in recurrent Glioblastoma

Patients diagnosed with glioblastoma (GBM) have the most aggressive tumor progression and lethal recurrence. Research on the immune microenvironment landscape of tumor and cerebrospinal fluid (CSF) is limited. At the single-cell level, we aim to reveal the recurrent immune microenvironment of GBM and the potential CSF biomarkers and compare tumor locations. We collected four clinical samples from two patients: malignant samples from one recurrent GBM patient and non-malignant samples from a patient with brain tumor. We performed single-cell RNA sequencing (scRNA-seq) to reveal the immune landscape of recurrent GBM and CSF. T cells were enriched in the malignant tumors, while Treg cells were predominately found in malignant CSF, which indicated an inhibitory microenvironment in recurrent GBM. Moreover, macrophages and neutrophils were significantly enriched in malignant CSF. This indicates that they an important role in GBM progression. S100A9, extensively expressed in malignant CSF, is a promising biomarker for GBM diagnosis and recurrence. Our study reveals GBM's recurrent immune microenvironment after chemoradiotherapy and compares malignant and non-malignant CSF samples. We provide novel targets and confirm the promise of liquid CSF biopsy for patients with GBM.

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

CDCA5 promoted cell invasion and migration by activating TGF-β1 pathway in human ovarian cancer cells

BACKGROUND

The gene cell division cycle associated 5 (CDCA5), also called sororin, has oncogenic characteristics and is upregulated in various carcinomas. Nevertheless, the involvement of CDCA5 in ovarian cancer (OC), a highly aggressive form of cancer, and the underlying mechanism of metastasis remain inadequately investigated.

RESULTS

The bioinformatics data revealed a negative correlation between the patient's survival and CDCA5 expression, which was overexpressed in OC. Functional assays also confirmed high expression levels of CDCA5 in OC tissues and cells. This suggests that CDCA5 may potentially enhance the motility, migration, and proliferation of OC cells invitro. It impedes DNA damage and apoptosis in OC cells, inhibiting xenograft development in nude mice. The RNA sequencing results suggest CDCA5 is majorly associated with biological functions related to the extracellular matrix (ECM) and influences the transforming growth factor (TGF) signaling pathway. Moreover, subsequent functional investigations elucidated that CDCA5 facilitated the migration and invasion of OC cells viathe TGF-β1/Smad2/3 signaling pathway activation.

CONCLUSIONS

CDCA5 may be a strong potential therapeutic target for the treatment and management of OC.

Integrated Multi-omics Analyses Identify CDCA5 as a Novel Biomarker Associated with Alternative Splicing, Tumor Microenvironment, and Cell Proliferation in Colon Cancer Via Pan-cancer Analysis

Background: CDCA5 has been reported as a gene involved in the cell cycle, however current research provides little details. Our goal was to figure out its functions and probable mechanisms in pan-cancer. Methods: Pan-cancer bulk sequencing data and web-based analysis tools were applied to analyze CDCA5's correlations with the gene expression, clinical prognosis, genetic alterations, promoter methylation, alternative splicing, immune checkpoints, tumor microenvironment and enrichment. Real‑time PCR, cell clone formation assay, CCK-8 assay, cell proliferation assay, migration assay, invasion assay and apoptosis assay were used to evaluate the effect of CDCA5 silencing on colon cancer cell lines. Results: CDCA5 is highly expressed in most tumors, which has been linked to a poor prognosis. Immune checkpoints analysis revealed that CDCA5 was associated with the immune gene CD276 in various tumors. Single-cell analysis showed that CDCA5 correlated with proliferating T cell infiltration in COAD. Enrichment analysis demonstrated that CDCA5 may modify cell cycle genes to influence p53 signaling. The examination of DLD1 cells revealed that CDCA5 increased the proliferation and blocked cell apoptosis. Conclusion: This study contributes to the knowledge of the role of CDCA5 in carcinogenesis, highlighting the prognostic potential and carcinogenic involvement of CDCA5 in pan-cancer.

Identification of molecular subtypes of ischaemic stroke based on immune-related genes and weighted co-expression network analysis

Immune system has been reported to play a key role in the development of ischaemic stroke (IS). Nevertheless, its exact immune-related mechanism has not yet been fully revealed. Gene expression data of IS and healthy control samples was downloaded from Gene Expression Omnibus database and differentially expressed genes (DEGs) was obtained. Immune-related genes (IRGs) data was downloaded from the ImmPort database. The molecular subtypes of IS were identified based on IRGs and weighted co-expression network analysis (WGCNA). 827 DEGs and 1142 IRGs were obtained in IS. Based on 1142 IRGs, 128 IS samples were clustered into two molecular subtypes: clusterA and clusterB. Based on the WGCNA, the authors found that the blue module had the highest correlation with IS. In the blue module, 90 genes were screened as candidate genes. The top 55 genes were selected as the central nodes according to gene degree in protein-protein interactions network of all genes in blue module. Through taking overlap, nine real hub genes were obtained that might distinguish between clusterA subtype and clusterB subtype of IS. The real hub genes (IL7R, ITK, SOD1, CD3D, LEF1, FBL, MAF, DNMT1, and SLAMF1) may be associated with molecular subtypes and immune regulation of IS.

Identification of key genes for IgA nephropathy based on machine learning algorithm and correlation analysis of immune cells

INTRODUCTION

The pathogenesis and progression mechanism of Immunoglobulin A nephropathy (IgAN) is not fully understood. There is a lack of panoramic analysis of IgAN immune cell infiltration and algorithms that are more efficient and accurate for screening key pathogenic genes.

METHODS

RNA sequencing (RNA-seq) data sets on IgAN were downloaded from the Gene Expression Omnibus (GEO) database, including GSE93798, GSE35489, and GSE115857. The RNA-seq data set of kidney tissue as control samples were downloaded from the Genotype-Tissue Expression (GTEx) database. Three machine learning algorithms-weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine-were used to identify the key pathogenic gene sets of the IgAN disease. The ssGSEA method was applied to calculate the immune cell infiltration (ICI) of IgAN samples, whereas the Spearman test was used for correlation analysis. The receiver operator characteristic curve (ROC) was used to evaluate the diagnostic efficacy of key genes. The correlation between the key genes and ICI was analyzed using the Spearman test.

RESULTS

A total of 177 genes were screened out as differentially expressed genes (DEGs) for IgAN, including 135 up-regulated genes and 42 down-regulated genes. The DEGs were significantly enriched in the inflammatory- or immune-related pathways (gene sets). Activating transcription factor 3 (AFT3), C-X-C Motif Chemokine Ligand 6 (CXCL6), and v-fos FBJ murine osteosarcoma viral oncogene homolog B (FOSB) were identified using WGCNA, support vector machine, and LASSO algorithms. These three genes revealed good diagnostic efficacy in the training and test cohorts. The CXCL6 expression positively correlated with activated B cells and memory B cells.

CONCLUSION

ATF3, FOSB, and CXCL6 genes were identified as potential biomarkers of IgAN. These three genes exhibited good diagnostic efficacy for IgAN. We described the landscape of immune cell infiltration for IgAN. Activated B cells and memory B cells were more highly expressed in the IgAN samples than in the control samples. CXCL6 seems crucial to the pathogenesis of IgAN and may induce IgAN by enriching immune cells. Our study may contribute to developing CXCL6 inhibitors that target B cells for IgAN therapy.

Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis

Background

Uterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated.

Methods

To identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequencing data and clinical data of 543 UCEC samples were obtained from The Cancer Genome Atlas (TCGA) database and then subjected to weighted gene co-expression network analysis (WGCNA). To explore the potential biological function of the hub modules, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Genes differentially expressed in UCEC were screened according to TCGA data using the "gdcDEAnalysis" package in R (The R Foundation for Statistical Computing). After intersecting with hub genes, the shared genes were used for further survival analyses. The relationship between gene expression level and clinical phenotype was analyzed in the TCGA-UCEC cohort in The University of ALabama at Birmingham CANcer data analysis Portal and the Human Protein Atlas. The microarray data set GSE17025 was also analyzed to validate the gene expression profiles.

Results

There were 19 coexpression modules generated by WGCNA. Among them, 2 modules with 198 hub genes were highly correlated with clinical features (especially histologic grade and clinical stage). Meanwhile, 4,003 differentially expressed genes (DEGs) were screened out, and 164 DEGs overlapped with hub genes. Survival analyses revealed that high expression of GINS4 and low expression of ESR1 showed a trend of poor prognosis. Further analyses demonstrated that both messenger RNA (mRNA) and protein expression profiles of GINS4 and ESR1 were significantly associated with UCEC development and progression in TCGA and GSE17025 cohorts.

Conclusions

Based on the integrated bioinformatic analyses, our data indicated that GINS4 and ESR1 might serve as potential prognostic markers and targets for UCEC therapy.

Screening prognostic genes related to leucovorin, fluorouracil, and irinotecan treatment sensitivity by performing co-expression network analysis for colon cancer

Background: Colon cancer is one of the most common malignant tumors in the world. FOLFIRI (leucovorin, fluorouracil, and irinotecan) is a common combination in chemotherapy regimens. However, insensitivity to FOLFIRI is an important factor in the effectiveness of the treatment for advanced colon cancer. Our study aimed to explore precise molecular targets associated with chemotherapy responses in colon cancer.

Methods: Gene expression profiles of 21 patients with advanced colorectal cancer who received chemotherapy based on FOLFIRI were obtained from the Gene Expression Omnibus (GEO) database. The gene co-expression network was constructed by the weighted gene co-expression network analysis (WGCNA) and functional gene modules were screened out. Clinical phenotypic correlation analysis was used to identify key gene modules. Gene Ontology and pathway enrichment analysis were used to screen enriched genes in key modules. Protein–protein interaction (PPI) analysis was used to screen out key node genes. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, the correlation between the expression levels of these genes and the overall survival (OS) and disease-free survival (DFS) of colon cancer patients was investigated, and the hub genes were screened out. Immunohistochemistry of candidate hub genes was identified using the Human Protein Atlas database. Finally, clinical information and RNA sequencing data of colon cancer were obtained from The Cancer Genome Atlas project database (TCGA), the GEPIA, and the Human Atlas databases for validation.

Results: The WGCNA revealed that three hub genes were closely related to chemotherapy insensitivity of colon cancer: AEBP1, BGN, and TAGLN. The protein expression levels of AEBP1, BGN, and TAGLN in tumor tissues were higher than those in normal tissues. In addition, the gene expression levels of AEBP1, BGN, and TAGLN were negatively correlated with OS and DFS in colon cancer patients. Therefore, AEBP1, BGN, and TAGLN have been identified as potential biomarkers related to the response to FOLFIRI treatment of colon cancer.

Conclusion: We found that AEBP1, BGN, and TAGLN, as potential predictive biomarkers, may play an important role in the response to FOLFIRI treatment of colon cancer and as a precise molecular target associated with chemotherapy response in colon cancer.

Screening of Hub Genes in Hepatocellular Carcinoma Based on Network Analysis and Machine Learning

Hepatocellular carcinoma (LIHC) is the fifth common cancer worldwide, and it requires effective diagnosis and treatment to prevent aggressive metastasis. The purpose of this study was to construct a machine learning-based diagnostic model for the diagnosis of liver cancer. Using weighted correlation network analysis (WGCNA), univariate analysis, and Lasso-Cox regression analysis, protein-protein interactions network analysis is used to construct gene networks from transcriptome data of hepatocellular carcinoma patients and find hub genes for machine learning. The five models, including gradient boosting, random forest, support vector machine, logistic regression, and integrated learning, were to identify a multigene prediction model of patients. Immunological assessment, TP53 gene mutation and promoter methylation level analysis, and KEGG pathway analysis were performed on these groups. Potential drug molecular targets for the corresponding hepatocellular carcinomas were obtained by molecular docking for analysis, resulting in the screening of 2 modules that may be relevant to the survival of hepatocellular carcinoma patients, and the construction of 5 diagnostic models and multiple interaction networks. The modes of action of drug-molecule interactions that may be effective against hepatocellular carcinoma core genes CCNA2, CCNB1, and CDK1 were investigated. This study is expected to provide research ideas for early diagnosis of hepatocellular carcinoma.

ASPM, CDC20, DLGAP5, BUB1B, CDCA8, and NCAPG May Serve as Diagnostic and Prognostic Biomarkers in Endometrial Carcinoma

Uterine Corpus Endometrial Carcinoma (UCEC), the most common gynecologic malignancy in developed countries, remains to be a major public health problem. Further studies are surely needed to elucidate the tumorigenesis of UCEC. Herein, intersecting 203 differentially expressed genes (DEGs) were identified with the GSE17025, GSE63678, and The Cancer Genome Atlas-UCEC datasets. The Gene Ontology/Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis and protein-protein interaction (PPI) network were performed on those 203 DEGs. Intriguingly, 6 of the top 10 nodes in the PPI network were related to unfavorable prognosis, that is, ASPM, CDC20, DLGAP5, BUB1B, CDCA8, and NCAPG. The mRNA and protein expression levels of the 6 hub genes were elevated in UCEC tissues compared to normal tissues. Higher expression of the 6 hub genes was associated with poor prognostic clinicopathological characteristics. The receiver operating characteristic curve suggested the significant diagnostic ability of the 6 hub genes for UCEC. Then, underlying pathogeneses of UCEC including promoter methylation level, TP53 mutation status, genomic genetic variation, and immune cells infiltration were analyzed. The mRNA expression level of the 6 hub genes was also higher in cervical squamous cell carcinoma and endocervical adenocarcinoma, uterine carcinosarcoma, and ovarian serous cystadenocarcinoma tissues than in corresponding normal tissues. In conclusion, ASPM, CDC20, DLGAP5, BUB1B, CDCA8, and NCAPG may be considered diagnostic and prognostic biomarkers in UCEC.

Diagnostic model based on bioinformatics and machine learning to distinguish Kawasaki disease using multiple datasets

Background

Kawasaki disease (KD), characterized by systemic vasculitis, is the leading cause of acquired heart disease in children. Herein, we developed a diagnostic model, with some prognosis ability, to help distinguish children with KD.

Methods

Gene expression datasets were downloaded from Gene Expression Omnibus (GEO), and gene sets with a potential pathogenic mechanism in KD were identified using differential expressed gene (DEG) screening, pathway enrichment analysis, random forest (RF) screening, and artificial neural network (ANN) construction.

Results

We extracted 2,017 DEGs (1,130 with upregulated and 887 with downregulated expression) from GEO. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that the DEGs were significantly enriched in innate/adaptive immune response-related processes. Subsequently, the results of weighted gene co-expression network analysis and DEG screening were combined and, using RF and ANN, a model with eight genes (VPS9D1, CACNA1E, SH3GLB1, RAB32, ADM, GYG1, PGS1, and HIST2H2AC) was constructed. Classification results of the new model for KD diagnosis showed excellent performance for different datasets, including those of patients with KD, convalescents, and healthy individuals, with area under the curve values of 1, 0.945, and 0.95, respectively.

Conclusions

We used machine learning methods to construct and validate a diagnostic model using multiple bioinformatic datasets, and identified molecules expected to serve as new biomarkers for or therapeutic targets in KD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-022-03557-y.

Identification of hub genes related to CD4+ memory T cell infiltration with gene co-expression network predicts prognosis and immunotherapy effect in colon adenocarcinoma

Background: CD4+ memory T cells (CD4+ MTCs), as an important part of the microenvironment affecting tumorigenesis and progression, have rarely been systematically analyzed. Our purpose was to comprehensively analyze the effect of CD4+ MTC infiltration on the prognosis of colon adenocarcinoma (COAD). Methods: Based on RNA-Seq data, weighted gene co-expression network analysis (WGCNA) was used to screen the CD4+ MTC infiltration genes most associated with colon cancer and then identify hub genes and construct a prognostic model using the least absolute shrinkage and selection operator algorithm (LASSO). Finally, survival analysis, immune efficacy analysis, and drug sensitivity analysis were performed to evaluate the role of the prognostic model in COAD. Results: We identified 929 differentially expressed genes (DEGs) associated with CD4+ MTCs and constructed a prognosis model based on five hub genes (F2RL2, TGFB2, DTNA, S1PR5, and MPP2) to predict overall survival (OS) in COAD. Kaplan-Meier analysis showed poor prognosis in the high-risk group, and the analysis of the hub gene showed that overexpression of TGFB2, DTNA, S1PR5, or MPP2 was associated with poor prognosis. Clinical prediction nomograms combining CD4+ MTC-related DEGs and clinical features were constructed to accurately predict OS and had high clinical application value. Immune efficacy and drug sensitivity analysis provide new insights for individualized treatment. Conclusion: We constructed a prognostic risk model to predict OS in COAD and analyzed the effects of risk score on immunotherapy efficacy or drug sensitivity. These studies have important clinical significance for individualized targeted therapy and prognosis.

Effect of CDCA5 on Proliferation and Metastasis of Triple Negative Breast Cancer Cells under shRNA Interference Technology

Objective

It was to explore the effect of cell division cycle associated 5 (CDCA5) under shRNA interference on proliferation and metastasis of triple negative breast cancer (TNBC) cells.

Methods

MDA-ME-231 and BT549 cells were selected as the research objects. According to the different interference methods and CDCA5 interference sequences, they were divided into the interference group 1_MDA-ME-231, the interference group 2_MDA-ME-231, the interference group 1_BT549, the interference group 2_BT549 (using shRNA technology), the control group _MDA-ME-231, and the control group _BT549 (breast cancer cells under normal culture conditions). MCF10A cells were routinely cultured as the negative control group to analyze the effect of CDCA5 expression on the proliferation and migration of cancer cells.

Results

The expression of CDCA5 protein in _MDA-ME-231 and _BT549 cells in control group was significantly higher than that in negative control group (P < 0.05). Compared with the control group, the inhibition rates of CDCA5 expression in 1_MDA-ME-231, 2_MDA-ME-231, 1_BT549, and 2_BT549 cells in the interference group were 39.01%, 42.98%, 49.57%, and 60.98%, respectively (P < 0.05). From 12 h, the proliferation level of TNBC cells at different culture time was lower than that of the control group (P < 0.05). Compared with the number of staining cells in the control group, the positive staining cells in 1_MDA-ME-231 (61.42%), 2_MDA-ME-231 (72.06%), 1_BT549 (52.53%), and 2_BT549 (59.65%) in the interference group were significantly decreased (P < 0.05).

Conclusion

The results show that the expression of CDCA5 in TNBC is increased, which plays an important role in the proliferation and migration of cancer cells. shRNA interference technology can knock down the expression of CDCA5 and inhibit its “promoting cancer” effect.

Identification of unique transcriptomic signatures and key genes through RNA sequencing and integrated WGCNA and PPI network analysis in HIV infected lung cancer

With the widespread use of highly active antiretroviral therapy (HARRT), the survival time of AIDS patients has been greatly extended. However, the incidence of lung cancer in HIV-infected patients is increasing and has become a major problem threatening the survival of AIDS patients. The aim of this study is to use Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene analysis to find possible key genes involved in HIV-infected lung cancer. In this study, using lung tissue samples from five pairs of HIV-infected lung cancer patients, second-generation sequencing was performed and transcriptomic data were obtained. A total of 132 HIV-infected lung cancer-related genes were screened out by WGCNA and differential gene expression analysis methods. Based on gene annotation analysis, these genes were mainly enriched in mitosis-related functions and pathways. In addition, in protein-protein interaction (PPI) analysis, a total of 39 hub genes were identified. Among them, five genes (ASPM, CDCA8, CENPF, CEP55, and PLK1) were present in both three hub gene lists (intersection gene, DEGs, and WCGNA module) suggesting that these five genes may become key genes involved in HIV-infected lung cancer.

Identification and Validation of a GPX4-Related Immune Prognostic Signature for Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a commonly occurring histological subtype of lung cancer. Glutathione peroxidase 4 (GPX4) is an important regulatory factor of ferroptosis and is involved in the development of many cancers, but its prognostic significance has not been systematically described in LUAD. In this study, we focused on developing a robust GPX4-related prognostic signature (GPS) for LUAD. Data for the training cohort was extracted from The Cancer Genome Atlas, and that for the validation cohort was sourced from the GSE72094 dataset including 863 LUAD patients. GPX4-related genes were screened out by weighted gene coexpression network analysis and Spearman’s correlation analysis. Then, Cox regression and least absolute shrinkage and selection operator regression analyses were employed to construct a GPS. The ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and GSEA were utilized to evaluate the relationship between GPS and the tumor microenvironment (TME). We constructed and validated a GPS premised on four GPX4-related genes (KIF14, LATS2, PRKCE, and TM6SF1), which could classify LUAD patients into low- and high-score cohorts. The high-risk cohort presented noticeably poorer overall survival (OS) as opposed to the low-risk cohort, meaning that the GPS may be utilized as an independent predictor of the OS of LUAD. The GPS was also adversely correlated with multiple tumor-infiltrating immune cells and immune-related processes and pathways in TME. Furthermore, greater sensitivity to erlotinib and lapatinib were identified in the low-risk cohort based on the GDSC database. Our findings suggest that the GPS can effectively forecast the prognosis of LUAD patients and may possibly regulate the TME of LUAD.

Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma

BACKGROUND

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5-year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA-seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice.

METHODS

We studied single-cell RNA-seq dataset and The Cancer Genome Atlas-glioblastoma multiforme (TCGA-GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model.

RESULTS

We identified 2128 DEGs from the single-cell RNA-seq dataset and 6461 DEGs from the bulk RNA-seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low-risk group showed better OS than those in the high-risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients.

CONCLUSION

We constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA-seq dataset and a bulk RNA-seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients.

Upregulated of ANXA3, SORL1, and Neutrophils May Be Key Factors in the Progressionof Ankylosing Spondylitis

Introduction

The specific pathogenesis of ankylosing spondylitis (AS) remains unclear, and our study aimed to investigate the possible pathogenesis of AS.

Materials and Methods

Two datasets were downloaded from the GEO database to perform differentially expressed gene analysis, GO enrichment analysis, KEGG pathway analysis, DO enrichment analysis, GSEA analysis of differentially expressed genes, and construction of diagnostic genes using SVM and WGCNA along with Hypoxia-related genes. Also, drug sensitivity analysis was performed on diagnostic genes. To identify the differentially expressed immune genes in the AS and control groups, we analyzed the composition of immune cells between them. Then, we examined differentially expressed genes in three AS interspinous ligament specimens and three Degenerative lumbar spine specimens using high-throughput sequencing while the immune cells were examined using the neutrophil count data from routine blood tests of 1770 HLA-B27-positive samples and 7939 HLA-B27-negative samples. To assess the relationship between ANXA3 and SORL1 and disease activity, we took the neutrophil counts of the first 50 patients with above-average BASDAI scores and the last 50 patients with below-average BASDAI scores for statistical analysis. We used immunohistochemistry to verify the expression of ANXA3 and SORL1 in AS and in controls.

Results

ANXA3 and SORL1 were identified as new diagnostic genes for AS. These two genes showed a significant differential expression between AS and controls, along with showing a significant positive correlation with the neutrophil count. The results of high-throughput sequencing verified that these two gene deletions were indeed differentially expressed in AS versus controls. Data from a total of 9707 routine blood tests showed that the neutrophil count was significantly higher in AS patients than in controls (p < 0.001). Patients with AS with a high BASDAI score had a much higher neutrophil count than those with a low score, and the difference was statistically significant (p < 0.001). The results of immunohistochemistry showed that the expression of ANXA3 and SORL1 in AS was significantly higher than that in the control group.

Conclusion

Upregulated of ANXA3, SORL1, and neutrophils may be a key factor in the progression of Ankylosing spondylitis.

ZNF143 Expression is Associated with COPD and Tumor Microenvironment in Non-Small Cell Lung Cancer

Background

Chronic obstructive pulmonary disease (COPD) is an inflammatory-related disease highly associated with increased lung cancer risk. Studies have explored the tumor promoting roles for zinc finger protein 143 (ZNF143). However, the role of ZNF143 in COPD and tumor microenvironment of non-small cell lung cancer (NSCLC) has not been fully elucidated.

Methods

COPD-related key genes were identified by differential gene expression evaluation, WGCNA and SVM-RFE analysis using mRNA expression data retrieved from public databases. ROC analysis was conducted to evaluate the diagnostic value of ZNF143. Correlation between ZNF143 and clinic-pathological features, associations with tumor-infiltrating immune cells (TICs) and the relationship with predictors of immunotherapy efficacy were explored. ZNF143 gene expression was validated by qRT-PCR using an independent cohort.

Results

Bioinformatic and machine learning analysis showed that ZNF143 was a COPD-related gene. ZNF143 expression was significantly upregulated in COPD and is a potential diagnostic biomarker in COPD with AUC > 0.85. ZNF143 expression was significantly upregulated in lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). ZNF143 expression levels were significantly higher in LUAD patients with COPD relative to the levels in patients only with LUAD. Upregulation of ZNF143 in patients with comorbidity of NSCLC and COPD was further confirmed by qRT-PCR analysis. High expression of ZNF143 was significantly correlated with advanced TNM stage in LUSC. High ZNF143 expression was associated with activated TICs in both LUAD and LUSC samples. Moreover, ZNF143 expression was significantly correlated with the levels of several known predictors of immunotherapy efficacy, including PD-L1, PD-L2, TMB and TIDE in NSCLC.

Conclusion

ZNF143 is a novel COPD biomarker. High expression level of ZNF143 is associated with immune microenvironment and high risk of progression of COPD to NSCLC.

Identification of Key LncRNAs and Pathways in Prediabetes and Type 2 Diabetes Mellitus for Hypertriglyceridemia Patients Based on Weighted Gene Co-Expression Network Analysis

Aims

Prevalence of prediabetes and type 2 diabetes mellitus(T2DM) are increasing worldwide. Key lncRNAs were detected to provide a reference for searching potential biomarkers of prediabetes and T2DM in hypertriglyceridemia patients.

Methods

The study included 18 hypertriglyceridemia patients: 6 newly diagnosed type 2 diabetes patients, 6 samples with prediabetes and 6 samples with normal blood glucose. Weighted gene co-expression network analysis (WGCNA) was conducted to construct co-expression network and obtain modules related to blood glucose, thus detecting key lncRNAs.

Results

The green, yellow and yellow module was significantly related to blood glucose in T2DM versus normal controls, T2DM versus prediabetes, prediabetes versus normal controls, respectively. ENST00000503273, ENST00000462720, ENST00000480633 and ENST00000485392 were detected as key lncRNAs for the above three groups, respectively.

Conclusions

For hypertriglyceridemia patients with different blood glucose levels, ENST00000503273, ENST00000462720 and ENST00000480633 could be potential biomarkers of T2DM.

Gene correlation network analysis to identify regulatory factors in sciatic nerve injury

BACKGROUND

Sciatic nerve injury (SNI), which frequently occurs under the traumatic hip and hip fracture dislocation, induces serious complications such as motor and sensory loss, muscle atrophy, or even disabling. The present work aimed to determine the regulating factors and gene network related to the SNI pathology.

METHODS

Sciatic nerve injury dataset GSE18803 with 24 samples was divided into adult group and neonate group. Weighted gene co-expression network analysis (WGCNA) was carried out to identify modules associated with SNI in the two groups. Moreover, differentially expressed genes (DEGs) were determined from every group, separately. Subsequently, co-expression network and protein-protein interaction (PPI) network were overlapped to identify hub genes, while functional enrichment and Reactome analysis were used for a comprehensive analysis of potential pathways. GSE30165 was used as the test set for investigating the hub gene involvement within SNI. Gene set enrichment analysis (GSEA) was performed separately using difference between samples and gene expression level as phenotype label to further prove SNI-related signaling pathways. In addition, immune infiltration analysis was accomplished by CIBERSORT. Finally, Drug-Gene Interaction database (DGIdb) was employed for predicting the possible therapeutic agents.

RESULTS

14 SNI status modules and 97 DEGs were identified in adult group, while 15 modules and 21 DEGs in neonate group. A total of 12 hub genes was overlapping from co-expression and PPI network. After the results from both test and training sets were overlapped, we verified that the ten real hub genes showed remarkably up-regulation within SNI. According to functional enrichment of hub genes, the above genes participated in the immune effector process, inflammatory responses, the antigen processing and presentation, and the phagocytosis. GSEA also supported that gene sets with the highest significance were mostly related to the cytokine-cytokine receptor interaction. Analysis of hub genes possible related signaling pathways using gene expression level as phenotype label revealed an enrichment involved in Lysosome, Chemokine signaling pathway, and Neurotrophin signaling pathway. Immune infiltration analysis showed that Macrophages M2 and Regulatory T cells may participate in the development of SNI. At last, 25 drugs were screened from DGIdb to improve SNI treatment.

CONCLUSIONS

The gene expression network is determined in the present work based on the related regulating factors within SNI, which sheds more light on SNI pathology and offers the possible biomarkers and therapeutic targets in subsequent research.

Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

Purpose

Exploring the molecular mechanisms of lung adenocarcinoma (LUAD) is beneficial for developing new therapeutic strategies and predicting prognosis. This study was performed to select core genes related to LUAD and to analyze their prognostic value.

Methods

Microarray datasets from the GEO (GSE75037) and TCGA-LUAD datasets were analyzed to identify differentially coexpressed genes in LUAD using weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. Functional enrichment analysis was conducted, and a protein–protein interaction (PPI) network was established. Subsequently, hub genes were identified using the CytoHubba plug-in. Overall survival (OS) analyses of hub genes were performed. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (THPA) databases were used to validate our findings. Gene set enrichment analysis (GSEA) of survival-related hub genes were conducted. Immunohistochemistry (IHC) was carried out to validate our findings.

Results

We identified 486 differentially coexpressed genes. Functional enrichment analysis suggested these genes were primarily enriched in the regulation of epithelial cell proliferation, collagen-containing extracellular matrix, transforming growth factor beta binding, and signaling pathways regulating the pluripotency of stem cells. Ten hub genes were detected using the maximal clique centrality (MCC) algorithm, and four genes were closely associated with OS. The CPTAC and THPA databases revealed that CHRDL1 and SPARCL1 were downregulated at the mRNA and protein expression levels in LUAD, whereas SPP1 was upregulated. GSEA demonstrated that DNA-dependent DNA replication and catalytic activity acting on RNA were correlated with CHRDL1 and SPARCL1 expression, respectively. The IHC results suggested that CHRDL1 and SPARCL1 were significantly downregulated in LUAD.

Conclusions

Our study revealed that survival-related hub genes closely correlated with the initiation and progression of LUAD. Furthermore, CHRDL1 and SPARCL1 are potential therapeutic and prognostic indicators of LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01933-9.

Construction of Competitive Endogenous RNA Network and Verification of 3-Key LncRNA Signature Associated With Distant Metastasis and Poor Prognosis in Patients With Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common malignancy with high distant metastasis rate. Long non-coding RNAs (LncRNAs) are reported to be upregulated or downregulated in multiple cancers and play a crucial role in the metastasis of tumors or prognosis. Therefore, the purpose of our study is to construct a prognostic signature for ccRCC based on distant metastasis-related lncRNAs and explore the involved potential competitive endogenous RNA (ceRNA) network. The differentially expressed genes (DEGs) screened from the database of the cancer genome atlas (TCGA) were used to construct a co-expression network and identify the distant metastasis-related module by weighted gene co-expression network analysis (WGCNA). Key genes with metastatic and prognostic significance were identified through rigorous screening, including survival analysis, correlation analysis, and expression analyses in stage, grade, and distant metastasis, and were verified in the data set of gene expression omnibus (GEO) and the database from gene expression profiling interactive analysis (GEPIA). The potential upstream miRNAs and lncRNAs were predicted via five online databases and LncBase. Here, we constructed a ceRNA network of key genes that are significantly associated with the distant metastasis and prognosis of patients with ccRCC. The distant metastasis-related lncRNAs were used to construct a risk score model through the univariate, least absolute shrinkage selection operator (LASSO), and multivariate Cox regression analyses, and the patients were divided into high- and low-risk groups according to the median of the risk score. The Kaplan–Meier survival analysis demonstrated that mortality was significantly higher in the high-risk group than in the low-risk group. Considering the other clinical phenotype, the Cox regression analyses indicated that the lncRNAs model could function as an independent prognostic factor. Quantitative real-time (qRT)-PCR in the tissues and cells of ccRCC verified the high-expression level of three lncRNAs. Gene set enrichment analysis (GSEA) revealed that the lncRNA prognostic signature was mainly enriched in autophagy- and immune-related pathways, indicating that the autophagy and immune functions may play an important role in the distant metastasis of ccRCC. In summary, the constructed distant metastasis-related lncRNA signature could independently predict prognosis in patients with ccRCC, and the related ceRNA network provided a new sight on the potential mechanism of distant metastasis and a promising therapeutic target for ccRCC.

Construction of co-expression modules related to survival by WGCNA and identification of potential prognostic biomarkers in glioblastoma

Glioblastoma (GBM) is a malignant brain tumour with poor prognosis. The potential pathogenesis and therapeutic target are still need to be explored. Herein, TCGA expression profile data and clinical information were downloaded, and the WGCNA was conducted. Hub genes which closely related to poor prognosis of GBM were obtained. Further, the relationship between the genes of interest and prognosis of GBM, and immune microenvironment were analysed. Patients from TCGA were divided into high‐ and low‐risk group. WGCNA was applied to the high‐ and low‐risk group and the black module with the lowest preservation was identified which could distinguish the prognosis level of these two groups. The top 10 hub genes which were closely related to poor prognosis of patients were obtained. GO analysis showed the biological process of these genes mainly enriched in: Cell cycle, Progesterone‐mediated oocyte maturation and Oocyte meiosis. CDCA5 and CDCA8 were screened out as the genes of interest. We found that their expression levels were closely related to overall survival. The difference analysis resulted from the TCGA database proved both CDCA5 and CDCA8 were highly expressed in GBM. After transfection of U87‐MG cells with small interfering RNA, it revealed that knockdown of the CDCA5 and CDCA8 could influence the biological behaviours of proliferation, clonogenicity and apoptosis of GBM cells. Then, single‐gene analysis was performed. CDCA5 and CDCA8 both had good correlations with genes that regulate cell cycle in the p53 signalling pathway. Moreover, it revealed that high amplification of CDCA5 was correlated with CD8⁺ T cells while CDCA8 with CD4⁺ T cells in GBM. These results might provide new molecular targets and intervention strategy for GBM.