Weighted gene co-expression network analysis identified MYL9 and CNN1 are associated with recurrence in colorectal cancer

Multivariate Cox regression analysis of prognostic genes and therapeutic mechanisms of gastric cancer

Gastric cancer (GC) is a common malignant tumor, which originated from the epithelial cells of the stomach. It has the characteristics of high incidence and poor prognosis. Therefore, it is urgent to find new prognostic markers for the diagnosis and treatment of GC. Download gene expression matrix and clinical data from TCGA database and GSE84437 dataset. Through independent prognostic analysis and clinical correlation analysis, 74 prognostic related genes (PRG) were screened out. A PPI network was established for PRG to identify four key genes (KG), namely LMOD1, CRYAB, VCL and MYL9. Survival analysis showed that patients with high expression of KG had poor prognosis. Multivariate Cox regression analysis showed that KG was an independent prognostic factor. TCGA database verifies the importance and significance of KG as a prognostic indicator. Functional enrichment analysis showed that KG was mainly involved in cell adhesion molecules, adhesion spots and PI3K/AKT signaling pathway. KG may be a potential therapeutic target for gastric cancer.

Weighted gene co-expression network analysis for hub genes in colorectal cancer

BACKGROUND

This study is designed to explore hub genes participating in colorectal cancer (CRC) development through weighted gene co-expression network analysis (WGCNA).

METHODS

Expression profiles of CRC and normal samples were retrieved from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA), and were subjected to WGCNA to filter differentially expressed genes with significant association with CRC. Functional enrichment analysis and protein-protein interaction (PPI) analysis were carried out to filter the candidate genes, further and survival analysis was performed for the candidate genes to obtain potential regulatory hub genes in CRC. Expression analysis was conducted for the candidate genes and a multifactor model was established.

RESULTS

After differential analysis and WGCNA, 289 candidate genes were filtered from the GEO and TCGA. Further functional enrichment analysis demonstrated possible regulatory pathways and functions. PPI analysis filtered 15 hub genes and survival analysis indicated a significant correlation of CLCA1, CLCA4, and CPT1A with prognosis of patients with CRC. The multifactor Cox risk model established based on the three genes revealed that if the three genes were a gene set, they had well predictive capacity for the prognosis of patients with CRC.

CONCLUSIONS

CLCA1, CLCA4, and CPT1A express at low levels in CRC and function as core anti-tumor genes. As a gene set, they can predict prognosis well.

MYL9 expressed in cancer-associated fibroblasts regulate the immune microenvironment of colorectal cancer and promotes tumor progression in an autocrine manner

BACKGROUND

The tumor microenvironment (TME) is an important factor that regulates the progression of colorectal cancer (CRC). Cancer-associated fibroblasts (CAFs) are the main mesenchymal cells in the TME and play a vital role in tumor progression; however, the specific underlying mechanisms require further study.

METHODS

Multiple single-cell and transcriptome data were analyzed and validated. Primary CAFs isolation, CCK8 assay, co-culture assay, western blotting, multiple immunofluorescence, qRT-PCR, ELISA, immunoprecipitation, ChIP, double luciferase, and animal experiments were used to explore the potential mechanism of MYL9 regulation in CRC.

RESULTS

Our findings revealed that MYL9 was predominantly localized and expressed in CAFs rather than in CRC cells, and bioinformatics analysis revealed that high MYL9 expression was strongly associated with poor overall and disease-free survival in various tumors. In addition, high MYL9 expression is closely associated with M2 macrophage infiltration, which can lead to an immunosuppressive microenvironment in CRC, making it insensitive to immunotherapy. Mechanically, MYL9 can regulate the secretion of CAFs on CCL2 and TGF-β1, thus affecting the immune microenvironment and progression of CRC. In addition, MYL9 bounded with IQGAP1 to regulate CCL2 and TGF-β1 secretion through the ERK 1/2 pathway, and CCL2 and TGF-β1 synergistically promoted CRC cells progression through the PI3K-AKT pathway. Furthermore, MYL9 promotes epithelial-mesenchymal transition (EMT) in CRC. During the upstream regulation of MYL9 in CAFs, we found that the EMT transcription factor ZEB1 could bind to the MYL9 promoter in CAFs, enhancing the activity and function of MYL9. Therefore, MYL9 is predominantly expressed in CAFs and can indirectly influence tumor biology and EMT by affecting CAFs protein expression in CRC.

CONCLUSIONS

MYL9 regulates the secretion of cytokines and chemokines in CAFs, which can affect the immune microenvironment of CRC and promote CRC progression. The relationship between MYL9 expression and CRC clinical staging and immunotherapy is closer in CAFs than in tumor cells; therefore, studies using CAFs as a model deserve more attention when exploring tumor molecular targets in clinical research.

Identification of diagnostic biomarkers via weighted correlation network analysis in colorectal cancer using a system biology approach

Colorectal cancer (CRC) is the third most frequent cancer to be diagnosed in both females and males necessitating identification of effective biomarkers. An in-silico system biology approach called weighted gene co-expression network analysis (WGCNA) can be used to examine gene expression in a complicated network of regulatory genes. In the current study, the co-expression network of DEGs connected to CRC and their target genes was built using the WGCNA algorithm. GO and KEGG pathway analysis were carried out to learn more about the biological role of the DEmRNAs. These findings revealed that the genes were mostly enriched in the biological processes that were involved in the regulation of hormone levels, extracellular matrix organization, and extracellular structure organization. The intersection of genes between hub genes and DEmRNAs showed that DKC1, PA2G4, LYAR and NOLC1 were the clinically final hub genes of CRC.

Assessment of Colorectal Cancer Risk Factors through the Application of Network-Based Approaches in a Racially Diverse Cohort of Colon Organoid Stem Cells

Numerous demographic factors have been associated with colorectal cancer (CRC) risk. To better define biological mechanisms underlying these associations, we performed RNA sequencing of stem-cell-enriched organoids derived from the healthy colons of seven European Americans and eight African Americans. A weighted gene co-expression network analysis was performed following RNA sequencing. Module-trait relationships were determined through the association testing of each module and five CRC risk factors (age, body mass index, sex, smoking history, and race). Only modules that displayed a significantly positive correlation for gene significance and module membership were considered for further investigation. In total, 16 modules were associated with known CRC risk factors (p < 0.05). To contextualize the role of risk modules in CRC, publicly available RNA-sequencing data from TCGA-COAD were downloaded and re-analyzed. Differentially expressed genes identified between tumors and matched normal-adjacent tissue were overlaid across each module. Loci derived from CRC genome-wide association studies were additionally overlaid across modules to identify robust putative targets of risk. Among them, MYBL2 and RXRA represented strong plausible drivers through which cigarette smoking and BMI potentially modulated CRC risk, respectively. In summary, our findings highlight the potential of the colon organoid system in identifying novel CRC risk mechanisms in an ancestrally diverse and cellularly relevant population.

Potential prognostic and immunotherapeutic value of calponin 1: A pan-cancer analysis

Background: Emerging evidence has suggested a pro-oncogenic role of calponin 1 (CNN1) in the initiation of a variety of cancers. Despite this, CNN1 remains unknown in terms of its effects and mechanisms on angiogenesis, prognosis, and immunology in cancer. Materials and Methods: The expression of CNN1 was extracted and analyzed using the TIMER, UALCAN, and GEPIA databases. Meanwhile, we analyzed the diagnostic value of CNN1 by using PrognoScan and Kaplan-Meier plots. To elucidate the value of CNN1 in immunotherapy, we used the TIMER 2.0 database, TISIDB database, and Sangerbox database. Gene set enrichment analysis (GSEA) was used to analyze the expression pattern and bio-progression of CNN1 and the vascular endothelium growth factor (VEGF) in cancer. The expressions of CNN1 and VEGF in gastric cancer were confirmed using immunohistochemistry. We used Cox regression analysis to investigate the association between pathological characteristics, clinical prognosis, and CNN1 and VEGF expressions in patients with gastric cancer. Results: CNN1 expression was higher in normal tissues than it was in tumor tissues of most types of cancers. However, the expression level rebounds during the development of tumors. High levels of CNN1 indicate a poor prognosis for 11 tumors, which include stomach adenocarcinoma (STAD). There is a relationship between CNN1 and tumor-infiltrating lymphocytes (TILs), and the marker genes NRP1 and TNFRSF14 of TILs are significantly related to CNN1 expression in gastric cancers. The GSEA results confirmed the lower expression of CNN1 in tumors when compared to normal tissues. However, CNN1 again showed an increasing trend during tumor development. In addition, the results also suggest that CNN1 is involved in angiogenesis. The immunohistochemistry results validated the GSEA result (take gastric cancer as an example). Cox analysis suggested that high CNN1 expression and high VEGF expression are closely associated with poor clinical prognosis. Conclusion: Our study has shown that CNN1 expression is aberrantly elevated in various cancers and positively correlates with angiogenesis and the immune checkpoint, contributing to cancer progression and poor prognosis. These results suggest that CNN1 could serve as a promising candidate for pan-cancer immunotherapy.

Up‐regulation of PRKDC was associated with poor renal dysfunction after renal transplantation: A multi‐centre analysis

Renal transplantation is the only efficacious treatment for end-stage kidney disease. However, some people have developed renal insufficiency after transplantation, the mechanisms of which have not been well clarified. Previous studies have focused on patient factors, while the effect of gene expression in the donor kidney on post-transplant renal function has been less studied. Donor kidney clinical data and mRNA expression status were extracted from the GEO database (GSE147451). Weight gene co-expression network analysis (WGCNA) and differential gene enrichment analysis were performed. For external validation, we collected data from 122 patients who accepted renal transplantation at several hospitals and measured the level of target genes by qPCR. This study included 192 patients from the GEO data set, and 13 co-expressed genes were confirmed by WGCNA and differential gene enrichment analysis. Then, the PPI network contained 17 edges as well as 12 nodes, and four central genes (PRKDC, RFC5, RFC3 and RBM14) were identified. We found by collecting data from 122 patients who underwent renal transplantation in several hospitals and by multivariate logistic regression that acute graft-versus-host disease postoperative infection, PRKDC [Hazard Ratio (HR) = 4.44; 95% CI = [1.60, 13.68]; p = 0.006] mRNA level correlated with the renal function after transplantation. The prediction model constructed had good predictive accuracy (C-index = 0.886). Elevated levels of donor kidney PRKDC are associated with renal dysfunction after transplantation. The prediction model of renal function status for post-transplant recipients based on PRKDC has good predictive accuracy and clinical application.

An epithelial-mesenchymal transition-related mRNA signature associated with the prognosis, immune infiltration and therapeutic response of colon adenocarcinoma

Background: Epithelial-mesenchymal transition (EMT) is closely associated with cancer cell metastasis. Colon adenocarcinoma (COAD) is one of the most common malignancies in the world, and its metastasis leading to poor prognosis remains a challenge for clinicians. The purpose of this study was to explore the prognostic value of EMT-related genes (EMTRGs) by bioinformatics analysis and to develop a new EMTRGs prognostic signature for COAD. Methods: The TCGA-COAD dataset was downloaded from the TCGA portal as the training cohort, and the GSE17538 and GSE29621 datasets were obtained from the GEO database as the validation cohort. The best EMTRGs prognostic signature was constructed by differential expression analysis, Cox, and LASSO regression analysis. Gene set enrichment analysis (GSEA) is used to reveal pathways that are enriched in high-risk and low-risk groups. Differences in tumor immune cell levels were analyzed using microenvironmental cell population counter and single sample gene set enrichment analysis. Subclass mapping analysis and Genomics of Drug Sensitivity in Cancer were applied for prediction of immunotherapy response and chemotherapy response, respectively. Results: A total of 77 differentially expressed EMTRGs were identified in the TCGA-COAD cohort, and they were significantly associated with functions and pathways related to cancer cell metastasis, proliferation, and apoptosis. We constructed EMTRGs prognostic signature with COMP, MYL9, PCOLCE2, SCG2, and TIMP1 as new COAD prognostic biomarkers. The high-risk group had a poorer prognosis with enhanced immune cell infiltration. The GSEA demonstrated that the high-risk group was involved in "ECM Receptor Interaction," "WNT Signaling Pathway" and "Colorectal Cancer." Furthermore, patients with high risk scores may respond to anti-CTLA4 therapy and may be more resistant to targeted therapy agents BI 2536 and ABT-888. Conclusion: Together, we developed a new EMTRGs prognostic signature that can be an independent prognostic factor for COAD. This study has guiding implications for individualized counseling and treatment of COAD patients.

Identification of hub genes predicting the development of prostate cancer from benign prostate hyperplasia and analyzing their clinical value in prostate cancer by bioinformatic analysis

Prostate cancer (PCa) and benign prostate hyperplasia (BPH) are commonly encountered diseases in males. Studies showed that genetic factors are responsible for the occurrences of both diseases. However, the genetic association between them is still unclear. Gene Expression Omnibus (GEO) database can help determine the differentially expressed genes (DEGs) between BPH and PCa. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were utilized to find pathways DEGs enriched. The STRING database can provide a protein-protein interaction (PPI) network, and find hub genes in PPI network. R software was used to analyze the clinical value of hub genes in PCa. Finally, the function of these hub genes was tested in different databases, clinical samples, and PCa cells. Fifteen up-regulated and forty-five down-regulated genes were found from GEO database. Seven hub genes were found in PPI network. The expression and clinical value of hub genes were analyzed by The Cancer Genome Atlas (TCGA) data. Except CXCR4, all hub genes expressed differently between tumor and normal samples. Exclude CXCR4, other hub genes have diagnostic value in predicting PCa and their mutations can cause PCa. The expression of CSRP1, MYL9 and SNAI2 changed in different tumor stage. CSRP1 and MYH11 could affect disease-free survival (DFS). Same results reflected in different databases. The expression and function of MYC, MYL9, and SNAI2, were validated in clinical samples and PCa cells. In conclusion, seven hub genes among sixty DEGs may be achievable targets for predicting which BPH patients may later develop PCa and they can influence the progression of PCa.

Up-Regulation of TRIM32 Associated With the Poor Prognosis of Acute Myeloid Leukemia by Integrated Bioinformatics Analysis With External Validation

Background

Acute myeloid leukemia (AML) is a malignant and molecularly heterogeneous disease. It is essential to clarify the molecular mechanisms of AML and develop targeted treatment strategies to improve patient prognosis.

Methods

AML mRNA expression data and survival status were extracted from TCGA and GEO databases (GSE37642, GSE76009, GSE16432, GSE12417, GSE71014). Weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis were performed. Functional enrichment analysis and protein-protein interaction (PPI) network were used to screen out hub genes. In addition, we validated the expression levels of hub genes as well as the prognostic value and externally validated TRIM32 with clinical data from our center. AML cell lines transfected with TRIM32 shRNA were also established to detect the proliferation in vitro.

Results

A total of 2192 AML patients from TCGA and GEO datasets were included in this study and 20 differentially co-expressed genes were screened by WGCNA and differential gene expression analysis methods. These genes were mainly enriched in phospholipid metabolic processes (biological processes, BP), secretory granule membranes (cellular components, CC), and protein serine/threonine kinase activity (molecular functions, MF). In addition, the protein-protein interaction (PPI) network contains 15 nodes and 15 edges and 10 hub genes (TLE1, GLI2, HDAC9, MICALL2, DOCK1, PDPN, RAB27B, SIX3, TRIM32 and TBX1) were identified. The expression of 10 central genes, except TLE1, was associated with survival status in AML patients (p<0.05). High expression of TRIM32 was tightly associated with poor relapse-free survival (RFS) and overall survival (OS) in AML patients, which was verified in the bone marrow samples from our center. In vitro, knockdown of TRIM32 can inhibit the proliferation of AML cell lines.

Conclusion

TRIM32 was associated with the progression and prognosis of AML patients and could be a potential therapeutic target and biomarker for AML in the future.

Weighted correlation network analysis revealed novel long non-coding RNAs for colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide, which after breast, lung and, prostate cancers, is the fourth prevalent cancer in the United States. Long non-coding RNAs (lncRNAs) have an essential role in the pathogenesis of CRC. Therefore, bioinformatics studies on lncRNAs and their target genes have potential importance as novel biomarkers. In the current study, publicly available microarray gene expression data of colorectal cancer (GSE106582) was analyzed with the Limma, Geoquery, Biobase package. Afterward, identified differentially expressed lncRNAs and their target genes were inserted into Weighted correlation network analysis (WGCNA) to obtain modules and hub genes. A total of nine differentially expressed lncRNAs (LINC01018, ITCH-IT, ITPK1-AS1, FOXP1-IT1, FAM238B, PAXIP1-AS1, ATP2B1-AS1, MIR29B2CHG, and SNHG32) were identified using microarray data analysis. The WGCNA has identified several hub genes for black (LMOD3, CDKN2AIPNL, EXO5, ZNF69, BMS1P5, METTL21A, IL17RD, MIGA1, CEP19, FKBP14), blue (CLCA1, GUCA2A, UGT2B17, DSC2, CA1, AQP8, ITLN1, BEST4, KLF4, IQCF6) and turquoise (PAFAH1B1, LMNB1, CACYBP, GLO1, PUM3, POC1A, ASF1B, SDCCAG3, ASNS, PDCD2L) modules. The findings of the current study will help to improve our understanding of CRC. Moreover, the hub genes that we have identified could be considered as possible prognostic/diagnostic biomarkers. This study led to the determination of nine lncRNAs with no previous association with CRC development.

Myosin light chain 9 promotes the proliferation, invasion, migration and angiogenesis of colorectal cancer cells by binding to Yes-associated protein 1 and regulating Hippo signaling

Colorectal cancer is a common type of cancer with high incidence and poor prognosis. Increased expression of myosin light chain 9 (MYL9) has been reported in early-stage and recurrent colorectal cancer tissues. This study aimed to investigate the precise role of MYL9 on the progression of colorectal cancer. MYL9 expression in several colorectal cancer cell lines was detected by Western blotting and RT-qPCR. Following MYL9 overexpression or knockdown, MYL9 expression was determined via RT-qPCR. Cell proliferation was detected with Cell Counting Kit-8 assay. Cell invasion, migration and angiogenesis were, respectively, examined with transwell, wound healing and tube formation assays. The binding between MYL9 and Yes-associated protein 1 (YAP1) was verified by a co-immunoprecipitation assay. The expression of YAP1, connective tissue growth factor and cysteine-rich angiogenic inducer 61 was examined by Western blotting. Subsequently, YAP1 silencing or Hippo antagonist was performed to clarify the regulatory mechanisms of MYL9 in colorectal cancer progression. Experimental results showed that MYL9 expression was elevated in colorectal cancer cell lines. MYL9 overexpression promoted cell proliferation, invasion, migration and angiogenesis, while silencing of MYL9 exerted the opposite effects. Results of co-immunoprecipitation assay indicated that MYL9 could bind to YAP1. Further experiments revealed that MYL9 affected the expression of YAP1 and its downstream signaling proteins. Afterward, YAP1 knockdown or the addition of Hippo antagonist inhibited the proliferation, invasion, migration and angiogenesis of colorectal cancer cells. Overall, MYL9 promotes the proliferation, invasion, migration and angiogenesis of colorectal cancer cells by binding to YAP1 and thereby activating Hippo signaling.

Research progress in myosin light chain 9 in malignant tumors

Myosin light chain 9 (MYL9) is a regulatory light chain of myosin, which plays an important role in various biological processes including cell contraction, proliferation and invasion. MYL9 expresses abnormally in several malignancies including lung cancer, breast cancer, prostate cancer, malignant melanoma and others, which is closely related to the poor prognosis, but the clinical significance for its expression varies with different types of cancer tissues. Further elucidating the molecular mechanism of MYL9 in various types of malignant tumor metastasis is of great significance for cancer prevention and treatment. At the same time, as a molecular marker and potential target, MYL9 may have great clinical value in the early diagnosis, prognosis prediction, and targeted treatment of malignant tumors.

Modularity in Biological Networks

Network modeling, from the ecological to the molecular scale has become an essential tool for studying the structure, dynamics and complex behavior of living systems. Graph representations of the relationships between biological components open up a wide variety of methods for discovering the mechanistic and functional properties of biological systems. Many biological networks are organized into a modular structure, so methods to discover such modules are essential if we are to understand the biological system as a whole. However, most of the methods used in biology to this end, have a limited applicability, as they are very specific to the system they were developed for. Conversely, from the statistical physics and network science perspective, graph modularity has been theoretically studied and several methods of a very general nature have been developed. It is our perspective that in particular for the modularity detection problem, biology and theoretical physics/network science are less connected than they should. The central goal of this review is to provide the necessary background and present the most applicable and pertinent methods for community detection in a way that motivates their further usage in biological research.

Reference Module-Based Analysis of Ovarian Cancer Transcriptome Identifies Important Modules and Potential Drugs

Ovarian cancer (OVC) is often diagnosed at the advanced stage resulting in a poor overall outcome for the patient. The disease mechanisms, prognosis, and treatment require imperative elucidation. A rank-based module-centric framework was proposed to analyze the key modules related to the development, prognosis, and treatment of OVC. The ovarian cancer cell line microarray dataset GSE43765 from the Gene Expression Omnibus database was used to construct the reference modules by weighted gene correlation network analysis. Twenty-three reference modules were tested for stability and functionally annotated. Furthermore, to demonstrate the utility of reference modules, two more OVC datasets were collected, and their gene expression profiles were projected to the reference modules to generate a module-level expression. An epithelial-mesenchymal transition module was activated in OVC compared to the normal epithelium, and a pluripotency module was activated in ovarian cancer stroma compared to ovarian cancer epithelium. Seven differentially expressed modules were identified in OVC compared to the normal ovarian epithelium, with five up-regulated, and two down-regulated. One module was identified to be predictive of patient overall survival. Four modules were enriched with SNP signals. Based on differentially expressed modules and hub genes, five candidate drugs were screened. The hub genes of those modules merit further investigation. We firstly propose the reference module-based analysis of OVC. The utility of the analysis framework can be extended to transcriptome data of other kinds of diseases.

Identification of Hub Genes and Construction of a Transcriptional Regulatory Network Associated With Tumor Recurrence in Colorectal Cancer by Weighted Gene Co-expression Network Analysis

Tumor recurrence is one of the most important risk factors that can negatively affect the survival rate of colorectal cancer (CRC) patients. However, the key regulators dictating this process and their exact mechanisms are understudied. This study aimed to construct a gene co-expression network to predict the hub genes affecting CRC recurrence and to inspect the regulatory network of hub genes and transcription factors (TFs). A total of 177 cases from the GSE17536 dataset were analyzed via weighted gene co-expression network analysis to explore the modules related to CRC recurrence. Functional annotation of the key module genes was assessed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. The protein and protein interaction network was then built to screen hub genes. Samples from the Cancer Genome Atlas (TCGA) were further used to validate the hub genes. Construction of a TFs-miRNAs–hub genes network was also conducted using StarBase and Cytoscape approaches. After identification and validation, a total of five genes (TIMP1, SPARCL1, MYL9, TPM2, and CNN1) were selected as hub genes. A regulatory network of TFs-miRNAs-targets with 29 TFs, 58 miRNAs, and five hub genes was instituted, including model GATA6-MIR106A-CNN1, SP4-MIR424-TPM2, SP4-MIR326-MYL9, ETS1-MIR22-TIMP1, and ETS1-MIR22-SPARCL1. In conclusion, the identification of these hub genes and the prediction of the Regulatory relationship of TFs-miRNAs-hub genes may provide a novel insight for understanding the underlying mechanism for CRC recurrence.

CLCA4 and MS4A12 as the significant gene biomarkers of primary colorectal cancer

Background: Primary colorectal cancer (PCRC) is a common digestive tract cancer in the elderly. However, the treatment effect of PCRC is still limited, and the long-term survival rate is low. Therefore, further exploring the pathogenesis of PCRC, and searching for specific molecular targets for diagnosis are the development trends of precise medical treatment, which have important clinical significance.

Methods: The public data were downloaded from Gene Expression Omnibus (GEO) database. Verification for repeatability of intra-group data was performed by Pearson’s correlation test and principal component analysis. Differentially expressed genes (DEGs) between normal and PCRC were identified, and the protein–protein interaction (PPI) network was constructed. Significant module and hub genes were found in the PPI network. A total of 192 PCRC patients were recruited between 2010 and 2019 from the Fourth Hospital of Hebei Medical University. RT-PCR was used to measure the relative expression of CLCA4 and MS4A12. Furthermore, the study explored the effect of expression of CLCA4 and MS4A12 for overall survival.

Results: A total of 53 DEGs were identified between PCRC and normal colorectal tissues. Ten hub genes concerned to PCRC were screened, namely CLCA4, GUCA2A, GCG, SST, MS4A12, PLP1, CHGA, PYY, VIP, and GUCA2B. The PCRC patients with low expression of CLCA4 and MS4A12 has a worse overall survival than high expression of CLCA4 and MS4A12 (P<0.05).

Conclusion: The research of DEGs in PCRC (53 DEGs, 10 hub genes, especially CLCA4 and MS4A12) and related signaling pathways is conducive to the differential analysis of the molecular mechanism of PCRC.

Weighted gene co-expression network analysis and drug-gene interaction bioinformatics uncover key genes associated with various presentations of malaria infection in African children and major drug candidates

Malaria is a fatal parasitic disease with unelucidated pathogenetic mechanism. Herein, we aimed to uncover genes associated with different clinical aspects of malaria based on the GSE1124 dataset that is publicly accessible by using WGCNA. We obtained 16 co-expression modules and their correlations with clinical features. Using the MCODE tool, we identified THEM4, STYX, VPS36, LCOR, KIAA1143, EEA1, RAPGEF6, LOC439994, ZBTB33, PTPN22, ESCO1, and KLF3 as hub genes positively associated with Plasmodium falciparum infection (ASPF). These hub genes were involved in the biological processes of endosomal transport, regulation of natural killer cell proliferation, and KEGG pathways of endocytosis and fatty acid elongation. For the purple module negatively correlated with ASPF, we identified 19 hub genes that were involved in the biological processes of positive regulation of cellular protein catabolic process and KEGG pathways of other glycan degradation. For the salmon module positively correlated with severe malaria anemia (SMA), we identified 17 hub genes that were among those driving the biological processes of positive regulation of erythrocyte differentiation. For the brown module positively correlated with cerebral malaria (CM), we identified eight hub genes and these genes participated in phagolysosome assembly and positive regulation of exosomal secretion, and animal mitophagy pathway. For the tan module negatively correlated with CM, we identified four hub genes that were involved in CD8-positive, alpha-beta T cell differentiation and notching signaling pathway. These findings may provide new insights into the pathogenesis of malaria and help define new diagnostic and therapeutic approaches for malaria patients.