Gene co-expression network reveals shared modules predictive of stage and grade in serous ovarian cancers

Integrative biological analyses of responses to food deprivation reveal resilience mechanisms in sea urchin larvae

A fundamental question in ecology is how organisms survive food deprivation. In the ocean, climate change is impacting the phenology of food availability for early life-history stages of animals. In this study, we undertook an integrative analysis of larvae of the sea urchin Strongylocentrotus purpuratus-an important keystone species in marine ecology and a molecular biological model organism in developmental biology. Specifically, to identify the mechanisms of resilience that maintain physiological state and the ability of organisms to recover from food deprivation, a suite of molecular biological, biochemical, physiological and whole organism measurements was completed. Previous studies focused on the importance of energy reserves to sustain larvae during periods of food deprivation. We show, however, that utilization of endogenous energy reserves only supplied 15% of the metabolic requirements of long-term survival (up to 22 days) in the absence of particulate food. This large energy gap was not supplied by larvae feeding on bacteria. Estimates of larval ability to transport dissolved organic matter directly from seawater showed that such substrates could fully supply metabolic needs. Integrative approaches allowed for filtering of gene expression signatures, linked with gene network analyses and measured biochemical and physiological traits, to identify biomarkers of resilience. We identified 14 biomarkers related to nutrition-responsive gene expression, of which a specific putative amino acid transporter gene was quantified in a single larva experiencing continuous nutritional stress. Advances in applications of gene expression technologies offer novel approaches to determine the physiological state of marine larval forms in ecological settings undergoing environmental change.

Pan-cancer identified ARPC1B as a promising target for tumor immunotherapy and prognostic biomarker, particularly in READ

ARPC1B encodes the protein known as actin-related protein 2/3 complex subunit 1 B (ARPC1B), which controls actin polymerization in the human body. Although ARPC1B has been linked to several human malignancies, its function in these cancers remains unclear. TCGA, GTEx, CCLE, Xena, CellMiner, TISIDB, and molecular signature databases were used to analyze ARPC1B expression in cancers. Visualization of data was primarily achieved using R language, version 4.0. Nineteen tumors exhibited high levels of ARPC1B expression, which were associated with different tumor stages and significantly affected the prognosis of various cancers. The level of ARPC1B expression substantially connected the narrative of ARPC1B expression with several TMB cancers and showed significant changes in MSI. Additionally, tolerance to numerous anticancer medications has been linked to high ARPC1B gene expression. Using Gene Set Variation Analysis/Gene Set Enrichment Analysisanalysis and concentrating on Rectum adenocarcinoma (READ), we thoroughly examined the molecular processes of the ARPC1B gene in pan-cancer. Using WGCNA, we examined the co-expression network of READ and ARPC1B. Meanwhile, ten specimens were selected for immunohistochemical examination, which showed high expression of ARPC1B in READ. Human pan-cancer samples show higher ARPC1B expression than healthy tissues. In many malignancies, particularly READ, ARPC1B overexpression is associated with immune cell infiltration and a poor prognosis. These results imply that the molecular biomarker ARPC1B may be used to assess the prognosis and immune infiltration of patients with READ.

Identification and targeting of cancer-associated fibroblast signature genes for prognosis and therapy in Cutaneous melanoma

BACKGROUND

Cancer-associated fibroblasts (CAFs) play pivotal roles in tumor invasion and metastasis. However, studies on CAF biomarkers in Cutaneous Melanoma (CM) are still scarce. This study aimed to explore the potential CAF biomarkers in CM, propose the potential therapeutic targets, and provide new insights for targeted therapy of CAFs in CM.

METHODS

We utilized weighted gene co-expression network analysis to identify CAF signature genes in CM, and conducted comprehensive bioinformatics analysis on the CAF risk score established by these genes. Moreover, single-cell sequencing analysis, spatial transcriptome analysis, and cell experiments were utilized for verifying the expression and distribution pattern of signature genes. Furthermore, molecular docking was employed to screen potential target drugs.

RESULTS

FBLN1 and COL5A1, two crucial CAF signature genes, were screened to establish the CAF risk score. Subsequently, a comprehensive bioinformatic analysis of the CAF risk score revealed that high-risk score group was significantly enriched in pathways associated with tumor progression. Besides, CAF risk score was significantly negatively correlated with clinical prognosis, immunotherapy response, and tumor mutational burden in CM patients. In addition, FBLN1 and COL5A1 were further identified as CAF-specific biomarkers in CM by multi-omics analysis and experimental validation. Eventually, based on these two targets, Mifepristone and Dexamethasone were screened as potential anti-CAFs drugs.

CONCLUSION

The findings indicated that FBLN1 and COL5A1 were the CAF signature genes in CM, which were associated with the progression, treatment, and prognosis of CM. The comprehensive exploration of CAF signature genes is expected to provide new insight for clinical CM therapy.

Genetic deviation associated with photodynamic therapy in HeLa cell

Photodynamic therapy (PDT) is a method that is used in cancer treatment. The main therapeutic effect is the production of singlet oxygen (1O2). Phthalocyanines for PDT produce high singlet oxygen with absorbers of about 600-700 nm.

AIM

It is aimed to analyze cancer cell pathways by flow cytometry analysis and cancer-related genes with q-PCR device by applying phthalocyanine L1ZnPC, which we use as photosensitizer in photodynamic therapy, in HELA cell line. In this study, we investigate the molecular basis of L1ZnPC's anti-cancer activity.

MATERIAL METHOD

The cytotoxic effects of L1ZnPC, a phthalocyanine obtained from our previous study, in HELA cells were evaluated and it was determined that it led to a high rate of death as a result. The result of photodynamic therapy was analyzed using q-PCR. From the data received at the conclusion of this investigation, gene expression values were calculated, and expression levels were assessed using the 2-∆∆Ct method to examine the relative changes in these values. Cell death pathways were interpreted with the FLOW cytometer device. One-Way Analysis of Variance (ANOVA) and the Tukey-Kramer Multiple Comparison Test with Post-hoc Test were used for the statistical analysis.

CONCLUSION

In our study, it was observed that HELA cancer cells underwent apoptosis at a rate of 80% with drug application plus photodynamic therapy by flow cytometry method. According to q-PCR results, CT values ​​of eight out of eighty-four genes were found to be significant and their association with cancer was evaluated. L1ZnPC is a new phthalocyanine used in this study and our findings should be supported by further studies. For this reason, different analyses are needed to be performed with this drug in different cancer cell lines. In conclusion, according to our results, this drug looks promising but still needs to be analyzed through new studies. It is necessary to examine in detail which signaling pathways they use and their mechanism of action. For this, additional experiments are required.

Structure of co-expression networks of Bifidobacterium species in response to human milk oligosaccharides

Biological systems respond to environmental perturbations and a large diversity of compounds through gene interactions, and these genetic factors comprise complex networks. Experimental information from transcriptomic studies has allowed the identification of gene networks that contribute to our understanding of microbial adaptations. In this study, we analyzed the gene co-expression networks of three Bifidobacterium species in response to different types of human milk oligosaccharides (HMO) using weighted gene co-expression analysis (WGCNA). RNA-seq data obtained from Geo Datasets were obtained for Bifidobacterium longum subsp. Infantis, Bifidobacterium bifidum and Bifidobacterium longum subsp. Longum. Between 10 and 20 co-expressing modules were obtained for each dataset. HMO-associated genes appeared in the modules with more genes for B. infantis and B. bifidum, in contrast with B. longum. Hub genes were identified in each module, and in general they participated in conserved essential processes. Certain modules were differentially enriched with LacI-like transcription factors, and others with certain metabolic pathways such as the biosynthesis of secondary metabolites. The three Bifidobacterium transcriptomes showed distinct regulation patterns for HMO utilization. HMO-associated genes in B. infantis co-expressed in two modules according to their participation in galactose or N-Acetylglucosamine utilization. Instead, B. bifidum showed a less structured co-expression of genes participating in HMO utilization. Finally, this category of genes in B. longum clustered in a small module, indicating a lack of co-expression with main cell processes and suggesting a recent acquisition. This study highlights distinct co-expression architectures in these bifidobacterial genomes during HMO consumption, and contributes to understanding gene regulation and co-expression in these species of the gut microbiome.

Current Progress and Perspectives of CDC20 in Female Reproductive Cancers

The cancers of the cervix, endometrium, ovary, and breast are great threats to women's health. Cancer is characterized by the uncontrolled proliferation of cells and deregulated cell cycle progression is one of the main causes of malignancy. Agents targeting cell cycle regulators may have potential anti-tumor effects. CDC20 (cell division cycle 20 homologue) is a co-activator of the anaphase-promoting complex/cyclosome (APC/C) and thus acts as a mitotic regulator. In addition, CDC20 serves as a subunit of the mitotic checkpoint complex (MCC) whose function is to inhibit APC/C. Recently, higher expression of CDC20 has been reported in these cancers and was closely associated with their clinicopathological parameters, indicating CDC20 a potential target for cancer treatment that is worth further study. In the present review, we summarized current progress and put forward perspectives of CDC20 in female reproductive cancers.

Looking for the Genes Related to Lung Cancer From Nasal Epithelial Cells by Network and Pathway Analysis

Previous studies have indicated that the airway epithelia of lung cancer-associated injury can extend to the nose and it was associated with abnormal gene expression. The aim of this study was to find the possible lung cancer-related genes from the nasal epithelium as bio-markers for lung cancer detection. WGCNA was performed to calculate the module–trait correlations of lung cancer based on the public microarray dataset, and their data were processed by statistics of RMA and t-test. Four specific modules associated with clinical features of lung cancer were constructed, including blue, brown, yellow, and light blue. Of which blue or brown module showed strong connection to genetic connectivity. From the brown module, it was found that HCK, NCF1, TLR8, EMR3, CSF2RB, and DYSF are the hub genes, and from the blue module, it was found that SPEF2, ANKFN1, HYDIN, DNAH5, C12orf55, and CCDC113 are the pivotal genes corresponding to the grade. These genes can be taken as the bio-markers to develop a noninvasive method of diagnosing early lung cancer.

CDC20 in and out of mitosis: a prognostic factor and therapeutic target in hematological malignancies

Cell division cycle 20 homologue (CDC20) is a well-known regulator of cell cycle, as it controls the correct segregation of chromosomes during mitosis. Many studies have focused on the biological role of CDC20 in cancer development, as alterations of its functionality have been linked to genomic instability and evidence demonstrated that high CDC20 expression levels are associated with poor overall survival in solid cancers. More recently, novel CDC20 functions have been demonstrated or suggested, including the regulation of apoptosis and stemness properties and a correlation with immune cell infiltration. Here, we here summarize and discuss the role of CDC20 inside and outside mitosis, starting from its network of interacting proteins. In the last years, CDC20 has also attracted more interest in the blood cancer field, being overexpressed and showing an association with prognosis both in myeloid and lymphoid malignancies. Preclinical findings showed that selective CDC20 and APC/C^CDC20/APC/C^CDH1 inhibitors, namely Apcin and proTAME, are effective against lymphoma and multiple myeloma cells, resulting in mitotic arrest and apoptosis and synergizing with clinically-relevant drugs. The evidence and hypothesis presented in this review provide the input for further biological and chemical studies aiming to dissect novel potential CDC20 roles and targeting strategies in hematological malignancies.

Long Noncoding RNA: Shining Stars in the Immune Microenvironment of Gastric Cancer

Gastric cancer (GC) is a kind of malignant tumor disease that poses a serious threat to human health. The GC immune microenvironment (TIME) is a very complex tumor microenvironment, mainly composed of infiltrating immune cells, extracellular matrix, tumor-associated fibroblasts, cytokines and chemokines, all of which play a key role in inhibiting or promoting tumor development and affecting tumor prognosis. Long non-coding RNA (lncRNA) is a non-coding RNA with a transcript length is more than 200 nucleotides. LncRNAs are expressed in various infiltrating immune cells in TIME and are involved in innate and adaptive immune regulation, which is closely related to immune escape, migration and invasion of tumor cells. LncRNA-targeted therapeutic effect prediction for GC immunotherapy provides a new approach for clinical research on the disease.

Radix ranunculus temate saponins sensitizes ovarian cancer to Taxol via upregulation of miR‑let‑7b

A common cause of treatment failure in ovarian cancer is acquired drug resistance. Therefore, effective novel drugs against chemoresistance need to be developed. MicroRNAs (miRNAs or miRs) serve key regulatory roles in tumorigenesis and chemoresistance. The objective of the present study was to explore the role of miR-let-7b in ovarian cancer chemoresistance, and to develop novel strategy for the treatment of drug-resistant ovarian cancer. For this purpose, reverse transcription-quantitative PCR was performed to evaluate the expression level of miR-let-7b in fresh ovarian cancer tissues and cell lines. miR-let-7b mimic was transfected into ovarian cancer cell lines. Functional experiments, cell apoptosis and cell viability assays were carried out to identify the tumor-suppressor function of miR-let-7b. The treatment effect of Radix ranunculus temate saponins (RRTS), one of the primary constituents extracted from the traditional Chinese medicine radix Ranunculi ternati, was identified in vitro and in vivo. The results revealed that miR-let-7b was downregulated significantly in chemoresistant ovarian cancer patients. miR-let-7b overexpression suppressed cell growth and invasion and enhanced sensitivity to Taxol of ovarian cancer cells. Furthermore, miR-let-7b levels in ovarian cancer tissue were inversely associated with collagen type III α1 chain (COL3A1) levels. COL3A1, a non-fibrillar collagen associated with chemoresistance, was targeted by miR-let-7b. RRTS showed cytotoxic effects on ovarian cancer cells through inducing miR-let-7b expression and decreasing COL3A1 expression. In addition, RRTS sensitized ovarian cancer to Taxol both in vitro and in vivo. In conclusion, the present results revealed synergistic cytotoxicity of RRTS and Taxol on against ovarian cancer cells via upregulating expression of miR-let-7b. Combination of Taxol and RRTS may be a novel treatment strategy for patients with TR ovarian cancer.

Identifying Mitotic Kinesins as Potential Prognostic Biomarkers in Ovarian Cancer Using Bioinformatic Analyses

Ovarian cancer (OC) is characterized by late-stage presentation, chemoresistance, and poor survival. Evaluating the prognosis of OC patients via effective biomarkers is essential to manage OC progression and to improve survival; however, it has been barely established. Here, we intend to identify differentially expressed genes (DEGs) as potential prognostic biomarkers of OC via bioinformatic analyses. Initially, a total of thirteen DEGs were extracted from different public databases as candidates. The expression of KIF20A, one of the DEGs, was correlated with a worse outcome of OC patients. The functional correlation of the DEGs with mitosis and the prognostic value of KIF20A imply a high correlation between mitotic kinesins (KIFs) and OC development. Finally, we found that KIF20A, together with the other nine mitotic KIFs (4A, 11, 14, 15, 18A, 18B, 23, C1, and2C) were upregulated and activated in OC tissues. Among the ten, seven overexpressed mitotic KIFs (11, 14, 18B, 20A, 23, and C1) were correlated with unfavorable clinical prognosis. Moreover, KIF20A and KIF23 overexpression was associated with worse prognosis in OC patients treated with platinum/taxol chemotherapy, while OCs overexpressing mitotic KIFs (11, 15, 18B, and C1) were resistant to MAPK pathway inhibitors. In conclusion, worse outcomes of OC patients were correlated with overexpression of several mitotic KIFs, which may serve both as prognostic biomarkers and therapeutic targets for OC.

Gene Co-Expression Network Analysis Identifies Vitamin D-Associated Gene Modules in Adult Normal Rectal Epithelium Following Supplementation

Colorectal cancer (CRC) is a common, multifactorial disease. While observational studies have identified an association between lower vitamin D and higher CRC risk, supplementation trials have been inconclusive and the mechanisms by which vitamin D may modulate CRC risk are not well understood. We sought to perform a weighted gene co-expression network analysis (WGCNA) to identify modules present after vitamin D supplementation (when plasma vitamin D level was sufficient) which were absent before supplementation, and then to identify influential genes in those modules. The transcriptome from normal rectal mucosa biopsies of 49 individuals free from CRC were assessed before and after 12 weeks of 3200IU/day vitamin D (Fultium-D3) supplementation using paired-end total RNAseq. While the effects on expression patterns following vitamin D supplementation were subtle, WGCNA identified highly correlated genes forming gene modules. Four of the 17 modules identified in the post-vitamin D network were not preserved in the pre-vitamin D network, shedding new light on the biochemical impact of supplementation. These modules were enriched for GO terms related to the immune system, hormone metabolism, cell growth and RNA metabolism. Across the four treatment-associated modules, 51 hub genes were identified, with enrichment of 40 different transcription factor motifs in promoter regions of those genes, including VDR:RXR. Six of the hub genes were nominally differentially expressed in studies of vitamin D effects on adult normal mucosa organoids: LCN2, HLA-C, AIF1L, PTPRU, PDE4B and IFI6. By taking a gene-correlation network approach, we have described vitamin D induced changes to gene modules in normal human rectal epithelium in vivo, the target tissue from which CRC develops.

Changes in gene-gene interactions associated with cancer onset and progression are largely independent of changes in gene expression

Recent findings indicate that changes underlying cancer onset and progression are not only attributable to changes in DNA structure and expression of individual genes but to changes in interactions among these genes as well. We examined co-expression changes in gene-network structure occurring during the onset and progression of nine different cancer types. Network complexity is generally reduced in the transition from normal precursor tissues to corresponding primary tumors. Cross-tissue cancer network similarity generally increases in early-stage cancers followed by a subsequent loss in cross-tissue cancer similarity as tumors reacquire cancer-specific network complexity. Gene-gene connections remaining stable through cancer development are enriched for “housekeeping” gene functions, whereas newly acquired interactions are associated with established cancer-promoting functions. Surprisingly, >90% of changes in gene-gene network interactions in cancers are not associated with changes in the expression of network genes relative to normal precursor tissues.

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Gene-gene network complexity is reduced in the transition from normal to cancer

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Network similarity across cancer types is higher in early-stage versus late-stage cancers

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Network interactions among housekeeping genes are stable through cancer development

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<10% of changes in network interactions in cancer involve changes in gene expression

Human iPSC-derived fallopian tube organoids with BRCA1 mutation recapitulate early-stage carcinogenesis

Germline pathogenic mutations in BReast CAncer (BRCA1) genes are thought to drive normal fallopian tube epithelial (FTE) cell transformation to high-grade serous ovarian cancer. No human models capture the sequence of events for disease initiation and progression. Here, we generate induced pluripotent stem cells (iPSCs) from healthy individuals and young ovarian cancer patients with germline pathogenic BRCA1 mutations (BRCA1^mut). Following differentiation into FTE organoids, BRCA1^mut lines exhibit cellular abnormalities consistent with neoplastic transformation compared to controls. BRCA1^mut organoids show an increased production of cancer-specific proteins and survival following transplantation into mice. Organoids from women with the most aggressive ovarian cancer show the greatest pathology, indicating the potential value to predict clinical severity prior to disease onset. These human FTE organoids from BRCA1^mut carriers provide a faithful physiological in vitro model of FTE lesion generation and early carcinogenesis. This platform can be used for personalized mechanistic and drug screening studies.

Yucer et al. generate a human BRCA1 mutant iPSC-derived fallopian tube organoid model, which recapitulates BRCA1 mutant ovarian carcinogenesis in vitro and shows tumors in vivo. This model provides a biologically relevant platform to validate drugs and a basis for personalized early detection and preventative strategies for women carrying BRCA1 mutations.

Extracellular Vesicle Molecular Signatures Characterize Metastatic Dynamicity in Ovarian Cancer

BACKGROUND

Late-stage diagnosis of ovarian cancer, a disease that originates in the ovaries and spreads to the peritoneal cavity, lowers 5-year survival rate from 90% to 30%. Early screening tools that can: i) detect with high specificity and sensitivity before conventional tools such as transvaginal ultrasound and CA-125, ii) use non-invasive sampling methods and iii) longitudinally significantly increase survival rates in ovarian cancer are needed. Studies that employ blood-based screening tools using circulating tumor-cells, -DNA, and most recently tumor-derived small extracellular vesicles (sEVs) have shown promise in non-invasive detection of cancer before standard of care. Our findings in this study show the promise of a sEV-derived signature as a non-invasive longitudinal screening tool in ovarian cancer.

METHODS

Human serum samples as well as plasma and ascites from a mouse model of ovarian cancer were collected at various disease stages. Small extracellular vesicles (sEVs) were extracted using a commercially available kit. RNA was isolated from lysed sEVs, and quantitative RT-PCR was performed to identify specific metastatic gene expression.

CONCLUSION

This paper highlights the potential of sEVs in monitoring ovarian cancer progression and metastatic development. We identified a 7-gene panel in sEVs derived from plasma, serum, and ascites that overlapped with an established metastatic ovarian carcinoma signature. We found the 7-gene panel to be differentially expressed with tumor development and metastatic spread in a mouse model of ovarian cancer. The most notable finding was a significant change in the ascites-derived sEV gene signature that overlapped with that of the plasma-derived sEV signature at varying stages of disease progression. While there were quantifiable changes in genes from the 7-gene panel in serum-derived sEVs from ovarian cancer patients, we were unable to establish a definitive signature due to low sample number. Taken together our findings show that differential expression of metastatic genes derived from circulating sEVs present a minimally invasive screening tool for ovarian cancer detection and longitudinal monitoring of molecular changes associated with progression and metastatic spread.

Four Prognosis-Associated lncRNAs Serve as Biomarkers in Ovarian Cancer

Long non-coding RNAs (lncRNAs) play crucial roles in ovarian cancer (OC) development. However, prognosis-associated lncRNAs (PALs) for OC have not been completely elucidated. Our study aimed to identify the PAL signature of OC. A total of 663 differentially expressed lncRNAs were identified in the databases. According to the weighted gene coexpression analysis, the highly correlated genes were clustered into seven modules related to the clinical phenotype of OC. A total of 25 lncRNAs that were significantly related to overall survival were screened based on univariate Cox regression analysis. The prognostic risk model constructed contained seven PALs based on the parameter λ_min, which could stratify OC patients into two risk groups. The results showed that the risk groups had different overall survival rates in both The Cancer Genome Atlas (TCGA) and two verified Gene Expression Omnibus (GEO) databases. Univariate and multivariate Cox regression analyses confirmed that the risk model was an independent risk factor for OC. Gene enrichment analysis revealed that the identified genes were involved in some pathways of malignancy. The competitive endogenous RNA (ceRNA) network included five PALs, of which four were selected for cell function assays. The four PALs were downregulated in 33 collected OC tissues and 3 OC cell lines relative to the control. They were shown to regulate the proliferative, migratory, and invasive potential of OC cells via Cell Counting Kit-8 (CCK-8) and transwell assays. Our study fills the gaps of the four PALs in OC, which are worthy of further study.

Cyclin H Regulates Lung Cancer Progression as a Carcinoma Inducer

Introduction

Studies have previously shown that Cyclin H (CCNH) is involved in the tumorigenesis and development of many cancers. The increasing research in CCNH is associated with the poor prognosis of most human cancers. Early diagnosis and clinical treatment are still the main challenges for lung cancer treatment. However, the exact role of CCNH in the tumorigenesis of lung cancer remains unclear.

Methods

The Tumor Genome Atlas (TCGA) database and the Clinical Proteomics Tumor Analysis Association (CPTAC) database were analyzed to detect key genes that might play an important role in lung cancer. The biological functions of CCNH were further revealed through bioinformatics experiments. The Kaplan-Meier method was applied to explore the relationship between CCNH expression and prognosis. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to detect the expression levels of CCNH in 6 lung cancer tissues and 3 cancer cell lines. The effect of CCNH expression on lung cancer progression was studied by in vitro functional experiments.

Results

Database analysis screened out candidate oncogenes, and CCNH was of great significance to the tumorigenesis of lung cancer. The higher the expression of CCNH was, the lower the survival rate of lung cancer patients were. The qRT-PCR data illustrated that the CCNH expression level was largely increased in lung cancer tissues and cells. The reduction of CCNH inhibited cell proliferation, invasion, and migration.

Conclusion

CCNH was related to poor prognosis, suggesting that CCNH regulated the tumorigenesis and development of lung cancer. Our study suggested that CCNH was a promising biomarker and target in the treatment of lung cancer.

Identification of the Hub Genes Associated with the Prognosis of Ovarian Cancer Patients via Integrated Bioinformatics Analysis and Experimental Validation

Background

This study aimed to identify the hub genes associated with prognosis of patients with ovarian cancer by using integrated bioinformatics analysis and experimental validation.

Methods

Four microarray datasets (GSE12470, GSE14407, GSE18521 and GSE46169) were analyzed by the GEO2R tool to screen common differentially expressed genes (DEGs). Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, the (KEGG) pathway and Reactome pathway enrichment analysis, protein–protein interaction (PPI) construction, and the identification of hub genes were performed. Furthermore, we performed the survival and expression analysis of the hub genes. In vitro functional assays were performed to assess the effects of hub genes on ovarian cancer cell proliferation, caspase-3/7 activity and invasion.

Results

A total of 89 common DEGs were identified among these four datasets. The KEGG and Reactome pathway results showed that the DEGs were mainly associated with cell cycle, mitotic and p53 signaling pathway. A total of 20 hub genes were identified from the PPI network by using sub-module analysis. The survival analysis revealed that high expression of six hub genes (AURKA, BUB1B, CENPF, KIF11, KIF23 and TOP2A) were significantly correlated with shorter overall survival and progression-free survival of patients with ovarian cancer. Furthermore, the expression of the six hub genes were validated by the GEPIA database and Human Protein Atlas, and functional studies revealed that knockdown of KIF11 and KIF23 suppressed the SKOV3 cell proliferation, increased caspase-3/7 activity and attenuated invasive potentials of SKOV3 cells. In addition, knockdown of KIF11 and KIF23 up-regulated E-cadherin mRNA expression but down-regulated N-cadherin and vimentin mRNA expression in SKOV3 cells.

Conclusion

Our results showed that six hub genes were up-regulated in ovarian cancer tissues and may predict poor prognosis of patients with ovarian cancer. KIF11 and KIF23 may play oncogenic roles in ovarian cancer cell progression via promoting ovarian cancer cell proliferation and invasion.

Construction and Validation of a Novel Glycometabolism-Related Gene Signature Predicting Survival in Patients With Ovarian Cancer

Among all fatal gynecological malignant tumors, ovarian cancer has the highest mortality rate. The purpose of this study was to develop a stable and personalized glycometabolism-related prognostic signature to predict the overall survival of ovarian cancer patients. The gene expression profiles and clinical information of ovarian cancer patients were derived from four public GEO datasets, which were divided into training and testing cohorts. Glycometabolism-related genes significantly associated with prognosis were selected. A risk score model was established and validated to evaluate its predictive value. We found 5 genes significantly related to prognosis and established a five-mRNA signature. The five-mRNA signature significantly divided patients into a low-risk group and a high-risk group in the training set and validation set. Survival analysis showed that high risk scores obtained by the model were significantly correlated with adverse survival outcomes and could be regarded as an independent predictor for patients with ovarian cancer. In addition, the five-mRNA signature can predict the overall survival of ovarian cancer patients in different subgroups. In summary, we successfully constructed a model that can predict the prognosis of patients with ovarian cancer, which provides new insights into postoperative treatment strategies, promotes individualized therapy, and provides potential new targets for immunotherapy.

AEBP1 is a Novel Oncogene: Mechanisms of Action and Signaling Pathways

Adipocyte enhancer-binding protein 1 (AEBP1) is a transcriptional repressor involved in the regulation of critical biological processes including adipogenesis, mammary gland development, inflammation, macrophage cholesterol homeostasis, and atherogenesis. Several years ago, we first reported the ability of AEBP1 to exert a positive control over the canonical NF-κB pathway. Indeed, AEBP1 positively regulates NF-κB activity via its direct interaction with IκBα, a key NF-κB inhibitor. AEBP1 overexpression results in uncontrollable activation of NF-κB, which may have severe pathogenic outcomes. Recently, the regulatory relationship between AEBP1 and NF-κB pathway has been of great interest to many researchers primarily due to the implication of NF-κB signaling in critical cellular processes such as inflammation and cancer. Since constitutive activation of NF-κB is widely implicated in carcinogenesis, AEBP1 overexpression is associated with tumor development and progression. Recent studies sought to explore the effects of the overexpression of AEBP1, as a potential oncogene, in different types of cancer. In this review, we analyze the effects of AEBP1 overexpression in a variety of malignancies (e.g., breast cancer, glioblastoma, bladder cancer, gastric cancer, colorectal cancer, ovarian cancer, and skin cancer), with a specific focus on the AEBP1-mediated control over the canonical NF-κB pathway. We also underscore the ability of AEBP1 to regulate crucial cancer-related events like cell proliferation and apoptosis in light of other key pathways (e.g., PI3K-Akt, sonic hedgehog (Shh), p53, parthanatos (PARP-1), and PTEN). Identifying AEBP1 as a potential biomarker for cancer prognosis may lead to a novel therapeutic target for the prevention and/or treatment of various types of cancer.

A gene signature for immune subtyping of desert, excluded, and inflamed ovarian tumors

PROBLEM

The current tumor immunology paradigm emphasizes the role of the immune tumor microenvironment and distinguishes several histologically and transcriptionally different immune tumor subtypes. However, the experimental validation of such classification is so far limited to selected cancer types. Here, we aimed to explore the existence of inflamed, excluded, and desert immune subtypes in ovarian cancer, as well as investigate their association with the disease outcome.

METHOD OF STUDY

We used the publicly available ovarian cancer dataset from The Cancer Genome Atlas for developing subtype assignment algorithm, which was next verified in a cohort of 32 real-world patients of a known tumor subtype.

RESULTS

Using clinical and gene expression data of 489 ovarian cancer patients in the publicly available dataset, we identified three transcriptionally distinct clusters, representing inflamed, excluded, and desert subtypes. We developed a two-step subtyping algorithm with COL5A2 serving as a marker for separating excluded tumors, and CD2, TAP1, and ICOS for distinguishing between inflamed and desert tumors. The accuracy of gene expression-based subtyping algorithm in a real-world cohort was 75%. Additionally, we confirmed that patients bearing inflamed tumors are more likely to survive longer.

CONCLUSION

Our results highlight the presence of transcriptionally and histologically distinct immune subtypes among ovarian tumors and emphasize the potential benefit of immune subtyping as a clinical tool for treatment tailoring.

Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer

Background

Ovarian cancer (OC) ranks fifth as a cause of gynecological cancer-associated death globally. Until now, the molecular mechanisms underlying the tumorigenesis and prognosis of OC have not been fully understood. This study aims to identify hub genes and therapeutic drugs involved in OC.

Methods

Four gene expression profiles (GSE54388, GSE69428, GSE36668, and GSE40595) were downloaded from the Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) in OC tissues and normal tissues with an adjusted P-value < 0.05 and a |log fold change (FC)| > 1.0 were first identified by GEO2R and FunRich software. Next, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses were performed for functional enrichment analysis of these DEGs. Then, the hub genes were identified by the cytoHubba plugin and the other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, survival analysis, and miRNA-hub gene network construction was also performed. Finally, the GEPIA2 and DGIdb databases were utilized to verify the expression levels of hub genes and to select the candidate drugs for OC, respectively.

Results

A total of 171 DEGs were identified, including 114 upregulated and 57 downregulated DEGs. The results of the GO analysis indicated that the upregulated DEGs were mainly involved in cell division, nucleus, and protein binding, whereas the biological functions showing enrichment in the downregulated DEGs were mainly negative regulation of transcription from RNA polymerase II promoter, protein complex and apicolateral plasma membrane, and glycosaminoglycan binding. As for the KEGG-pathway, the upregulated DEGs were mainly associated with metabolic pathways, biosynthesis of antibiotics, biosynthesis of amino acids, cell cycle, and HTLV-I infection. Additionally, 10 hub genes (KIF4A, CDC20, CCNB2, TOP2A, RRM2, TYMS, KIF11, BIRC5, BUB1B, and FOXM1) were identified and survival analysis of these hub genes showed that OC patients with the high-expression of CCNB2, TYMS, KIF11, KIF4A, BIRC5, BUB1B, FOXM1, and CDC20 were statistically more likely to have poorer progression free survival. Meanwhile, the expression levels of the hub genes based on GEPIA2 were in accordance with those based on GEO. Finally, DGIdb database was used to identify 62 small molecules as the potentially targeted drugs for OC treatment.

Conclusions

In summary, the data may produce new insights regarding OC pathogenesis and treatment. Hub genes and candidate drugs may improve individualized diagnosis and therapy for OC in future.

COL1A1 and MZB1 as the hub genes influenced the proliferation, invasion, migration and apoptosis of rectum adenocarcinoma cells by weighted correlation network analysis

OBJECTIVES

The influences of COL1A1 and MZB1 on the proliferation, migration, invasion and apoptosis abilities of rectum adenocarcinoma was aimed to explore in this study.

METHODS

Gene expression levels in rectum adenocarcinoma and adjacent tissues were analyzed by differential analysis. Weighted gene correlation network analysis (WGCNA) was employed to investigate rectal adenocarcinoma (READ) hub genes. MCODE was performed to screen the modules of protein-protein interaction network in Cytoscape software. Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool is considered to be the most effective tool in gene ontology (GO) enrichment and Kyoto Gene and Genomics Encyclopedia (KEGG) pathway analysis. Survival analysis was performed using READ patient information from TCGA-READ project database. Quantitative Real-time Polymerase Chain Reaction (qRT-PCR) and western blot were employedto examinemRNA and protein expressions of COL1A1 and MZB1 in tumor tissues and cell lines. After transfecting various interference sequences by liposome-mediated transfection, the influence of COL1A1 and MZB1 on the proliferation, apoptosis, migration and invasion of rectal cancer cells were observed by plate clone formation assay, flow cytometry, wound healing assay and transwell assay respectively. Moreover, xenograft tumor growth assay in vivo validated the results.

RESULTS

Higher expression levels of COL1A1 and lower expression levels of MZB1 were discovered in tumor tissues of patients with colorectal adenocarcinoma. Overexpression of MZB1 and silencing COL1A1 significantly inhibited proliferation, migration and invasion, while cell apoptosis was promoted. Overexpression of MZB1 and silencing COL1A1 inhibited the orthotopic growth of tumor in vivo.

CONCLUSION

COL1A1 promoted proliferation, migration and invasion but inhibited apoptosis of rectal adenocarcinoma cells while MZB1 was totally on the contrary.

Immunohistochemical validation of COL3A1, GPR158 and PITHD1 as prognostic biomarkers in early-stage ovarian carcinomas

Background

Ovarian cancer is the main cause of gynecological cancer-associated death. However, 5-year survival rates differ dramatically between the five main ovarian carcinoma histotypes. Therefore, we need to have a better understanding of the mechanisms that promote histotype-specific ovarian carcinogenesis and identify novel prognostic biomarkers.

Methods

Here, we evaluated the prognostic role of 29 genes for early-stage (I and II) ovarian carcinomas (n = 206) using immunohistochemistry (IHC).

Results

We provide evidence of aberrant protein expression patterns for Collagen type III alpha 1 chain (COL3A1), G protein-coupled receptor 158 (GPR158) and PITH domain containing 1 (PITHD1). Kaplan-Meier survival analysis revealed that COL3A1 expression was associated with shorter overall survival in the four major histotypes of epithelial ovarian carcinoma patients (P value = 0.026, HR = 2.99 (95% CI 1.089–8.19)). Furthermore, GPR158 and PITHD1 were shown to be histotype-specific prognostic biomarkers, with elevated GPR158 expression patterns in mucinous ovarian carcinoma patients with unfavorable overall survival (P value = 0.00043, HR = 6.13 (95% CI 1.98–18.98)), and an association with lower PITHD1 protein expression and unfavorable overall and disease-specific survival in clear-cell ovarian carcinoma patients (P value = 0.012, HR = 0.22 (95% CI 0.058–0.80); P value = 0.003, HR = 0.17 (95% CI 0.043–0.64)).

Conclusions

The novel biomarkers identified here may improve prognostication at the time of diagnosis and may assist in the development of future individualized therapeutic strategies for ovarian carcinoma patients.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-6084-4) contains supplementary material, which is available to authorized users.

Identification of four hub genes associated with adrenocortical carcinoma progression by WGCNA

Background

Adrenocortical carcinoma (ACC) is a rare and aggressive malignant cancer in the adrenal cortex with poor prognosis. Though previous research has attempted to elucidate the progression of ACC, its molecular mechanism remains poorly understood.

Methods

Gene transcripts per million (TPM) data were downloaded from the UCSC Xena database, which included ACC (The Cancer Genome Atlas, n = 77) and normal samples (Genotype Tissue Expression, n = 128). We used weighted gene co-expression network analysis to identify gene connections. Overall survival (OS) was determined using the univariate Cox model. A protein–protein interaction (PPI) network was constructed by the search tool for the retrieval of interacting genes.

Results

To determine the critical genes involved in ACC progression, we obtained 2,953 significantly differentially expressed genes and nine modules. Among them, the blue module demonstrated significant correlation with the “Stage” of ACC. Enrichment analysis revealed that genes in the blue module were mainly enriched in cell division, cell cycle, and DNA replication. Combined with the PPI and co-expression networks, we identified four hub genes (i.e., TOP2A, TTK, CHEK1, and CENPA) that were highly expressed in ACC and negatively correlated with OS. Thus, these identified genes may play important roles in the progression of ACC and serve as potential biomarkers for future diagnosis.

Identifying cancer prognostic modules by module network analysis

Background

The identification of prognostic genes that can distinguish the prognostic risks of cancer patients remains a significant challenge. Previous works have proven that functional gene sets were more reliable for this task than the gene signature. However, few works have considered the cross-talk among functional gene sets, which may result in neglecting important prognostic gene sets for cancer.

Results

Here, we proposed a new method that considers both the interactions among modules and the prognostic correlation of the modules to identify prognostic modules in cancers. First, dense sub-networks in the gene co-expression network of cancer patients were detected. Second, cross-talk between every two modules was identified by a permutation test, thus generating the module network. Third, the prognostic correlation of each module was evaluated by the resampling method. Then, the GeneRank algorithm, which takes the module network and the prognostic correlations of all the modules as input, was applied to prioritize the prognostic modules. Finally, the selected modules were validated by survival analysis in various data sets. Our method was applied in three kinds of cancers, and the results show that our method succeeded in identifying prognostic modules in all the three cancers. In addition, our method outperformed state-of-the-art methods. Furthermore, the selected modules were significantly enriched with known cancer-related genes and drug targets of cancer, which may indicate that the genes involved in the modules may be drug targets for therapy.

Conclusions

We proposed a useful method to identify key modules in cancer prognosis and our prognostic genes may be good candidates for drug targets.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2674-z) contains supplementary material, which is available to authorized users.

Overexpression of COL5A1 promotes tumor progression and metastasis and correlates with poor survival of patients with clear cell renal cell carcinoma

Background and aims

COL5A1 has been identified to be involved in metastasis of clear cell renal cell carcinoma (ccRCC) by bioinformatic analysis. This study aimed to investigate COL5A1 expression and its clinical significance in ccRCC. The function of COL5A1 in ccRCC was further investigated.

Methods

COL5A1 expression was examined in 256 ccRCC tissues and paired adjacent normal renal tissues by immunohistochemistry and real-time quantitative PCR. The clinical significance of COL5A1 expression was evaluated. Downregulation of COL5A1 was achieved using siRNA. The effects of COL5A1 silencing on cell proliferation, apoptosis, migration, invasion in vitro, and tumor growth in vivo were investigated.

Results

COL5A1 expression was upregulated in the majority of the ccRCC tissues at both protein and mRNA levels. COL5A1 expression was significantly correlated with tumor diameter, tumor stage, tumor grade, distant metastasis, recurrence, necrosis, and sarcomatoid (all P<0.05). COL5A1 expression was also significantly associated with overall survival of ccRCC patients (HR 1.876; P=0.027) and recurrence-free survival of localized ccRCC patients (HR 4.751; P<0.001). The accuracy of TNM, University of California Los Angeles Integrated Staging System, and Mayo clinic stage, size, grade, and necrosis prognostic models was improved when COL5A1 expression was added.

Conclusion

COL5A1 knockdown significantly inhibited cell proliferation, induced cell apoptosis, inhibited cell migration and invasion in vitro, and inhibited tumor growth in vivo. Therefore, COL5A1 may be a novel prognostic biomarker and a promising therapeutic target for ccRCC.

Effects of CPAP on the transcriptional signatures in patients with obstructive sleep apnea via coexpression network analysis

A growing number of studies provide epidemiological evidence linking obstructive sleep apnea (OSA) with a number of chronic disorders. Transcriptional analyses have been conducted to analyze the gene expression data. However, the weighted gene coexpression network analysis (WGCNA) method has not been applied to determine the transcriptional consequence of continuous positive airway pressure (CPAP) therapy in patients with severe OSA. The aim of this study was to identify key pathways and genes in patients with OSA that are influenced by CPAP treatment and uncover/unveil potential molecular mechanisms using WGCNA. We analyzed the microarray data of OSA (GSE 49800) listed in the Gene Expression Omnibus database. Coexpression modules were constructed using WGCNA. In addition, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were also conducted. After the initial data processing, 5101 expressed gene profiles were identified. Next, a weighted gene coexpression network was established and 16 modules of coexpressed genes were identified. The interaction analysis demonstrated a relative independence of gene expression in these modules. The black module, tan module, midnightblue module, pink module, and greenyellow module were significantly associated with the alterations in circulating leukocyte gene expression at baseline and after exposure to CPAP. The five hub genes were considered to be candidate OSA‐related genes after CPAP treatment. Functional enrichment analysis revealed that steroid biosynthesis, amino sugar and nucleotide sugar metabolism, protein processing in the endoplasmic reticulum, and the insulin signaling pathway play critical roles in the development of OSA in circulating leukocyte gene expression at baseline and after exposure to CPAP. Using this new systems biology approach, we identified several genes and pathways that appear to be critical to OSA after CPAP treatment, and these findings provide a better understanding of OSA pathogenesis.

Identification of Key Candidate Genes and Pathways for Relationship between Ovarian Cancer and Diabetes Mellitus Using Bioinformatical Analysis

Ovarian cancer is one of the three major gynecologic cancers in the world. The aim of this study is to find the relationship between ovarian cancer and diabetes mellitus by using the genetic screening technique. By GEO database query and related online tools of analysis, we analyzed 185 cases of ovarian cancer and 10 control samples from GSE26712, and a total of 379 different genes were identified, including 104 up-regulated genes and 275 down-regulated genes. The up-regulated genes were mainly enriched in biological processes, including cell adhesion, transcription of nucleic acid and biosynthesis, and negative regulation of cell metabolism. The down-regulated genes were enriched in cell proliferation, migration, angiogenesis and macromolecular metabolism. Protein-protein interaction was analyzed by network diagram and module synthesis analysis. The top ten hub genes (CDC20, H2AFX, ENO1, ACTB, ISG15, KAT2B, HNRNPD, YWHAE, GJA1 and CAV1) were identified, which play important roles in critical signaling pathways that regulate the process of oxidation-reduction reaction and carboxylic acid metabolism. CTD analysis showed that the hub genes were involved in 1,128 distinct diseases (bonferroni-corrected P<0.05). Further analysis by drawing the Kaplan-Meier survival curve indicated that CDC20 and ISG15 were statistically significant (P<0.05). In conclusion, glycometabolism was related to ovarian cancer and genes and proteins in glycometabolism could serve as potential targets in ovarian cancer treatment.

Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures

Background

In melanoma, like in other cancers, both genetic alterations and epigenetic underlie the metastatic process. These effects are usually measured by changes in both methylome and transcriptome profiles, whose cross-correlation remains uncertain. We aimed to assess at systems scale the significance of epigenetic treatment in melanoma cells with different metastatic potential.

Methods and findings

Treatment by DAC demethylation with 5-Aza-2’-deoxycytidine of two melanoma cell lines endowed with different metastatic potential, SKMEL-2 and HS294T, was performed and high-throughput coupled RNA-Seq and RRBS-Seq experiments delivered differential profiles (DiP) of both transcriptomes and methylomes. Methylation levels measured at both TSS and gene body were studied to inspect correlated patterns with wide-spectrum transcript abundance levels quantified in both protein coding and non-coding RNA (ncRNA) regions. The DiP were then mapped onto standard bio-annotation sources (pathways, biological processes) and network configurations were obtained. The prioritized associations for target identification purposes were expected to elucidate the reprogramming dynamics induced by the epigenetic therapy. The interactomic connectivity maps of each cell line were formed to support the analysis of epigenetically re-activated genes. i.e. those supposedly silenced by melanoma. In particular, modular protein interaction networks (PIN) were used, evidencing a limited number of shared annotations, with an example being MAPK13 (cascade of cellular responses evoked by extracellular stimuli). This gene is also a target associated to the PANDAR ncRNA, therapeutically relevant because of its aberrant expression observed in various cancers. Overall, the non-metastatic SKMEL-2 map reveals post-treatment re-activation of a richer pathway landscape, involving cadherins and integrins as signatures of cell adhesion and proliferation. Relatively more lncRNAs were also annotated, indicating more complex regulation patterns in view of target identification. Finally, the antigen maps matched to DiP display other differential signatures with respect to the metastatic potential of the cell lines. In particular, as demethylated melanomas show connected targets that grow with the increased metastatic potential, also the potential target actionability seems to depend to some degree on the metastatic state. However, caution is required when assessing the direct influence of re-activated genes over the identified targets. In light of the stronger treatment effects observed in non-metastatic conditions, some limitations likely refer to in silico data integration tools and resources available for the analysis of tumor antigens.

Conclusion

Demethylation treatment strongly affects early melanoma progression by re-activating many genes. This evidence suggests that the efficacy of this type of therapeutic intervention is potentially high at the pre-metastatic stages. The biomarkers that can be assessed through antigens seem informative depending on the metastatic conditions, and networks help to elucidate the assessment of possible targets actionability.

GLIS2 redundancy causes chemoresistance and poor prognosis of gastric cancer based on co‑expression network analysis

Gastric cancer is currently the fourth most common cancer and the third leading cause of cancer-associated mortality worldwide. Studies have identified that certain biomarkers contribute to the prognosis, diagnosis and treatment of gastric cancer. However, the biomarkers of gastric cancer are rarely used clinically. Therefore, it is imperative to define novel molecular networks and key genes to guide the further study and clinical treatment of gastric cancer. In the present study, raw RNA sequencing data and clinicopathological information on patients with gastric cancer were downloaded from The Cancer Genome Atlas, and a weighted gene co-expression network analysis was conducted. Additionally, functional enrichment and protein-protein interaction analyses were implemented to further examine the significant modules. As a result, 16 modules of highly correlated genes were acquired and colour coded, and the yellow module containing 174 genes associated with chemotherapy resistance and prognosis in gastric cancer was further analysed. The biological processes of the yellow module were primarily associated with cell adhesion, vasculature development and the regulation of cell proliferation. In addition, the Kyoto Encyclopedia of Genes and Genomes pathways primarily involved the transforming growth factor-β signalling pathway, the cellular tumour antigen p53 signalling pathway, extracellular matrix-receptor interactions and focal adhesions. Notably, survival analysis and cell verification confirmed that high expression of GLIS family zinc finger 2 is significantly associated with chemoresistance and a worse prognosis in gastric cancer, and that this high expression is likely to be an important biomarker for the guidance of clinical treatment and prognostic evaluation.

The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine

Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However, biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but also by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis, and the path for such tools to the clinic.

Gene Expression Analysis Reveals Novel Gene Signatures Between Young and Old Adults in Human Prefrontal Cortex

Human neurons function over an entire lifetime, yet the molecular mechanisms which perform their functions and protecting against neurodegenerative disease during aging are still elusive. Here, we conducted a systematic study on the human brain aging by using the weighted gene correlation network analysis (WGCNA) method to identify meaningful modules or representative biomarkers for human brain aging. Significantly, 19 distinct gene modules were detected based on the dataset GSE53890; among them, six modules related to the feature of brain aging were highly preserved in diverse independent datasets. Interestingly, network feature analysis confirmed that the blue modules demonstrated a remarkably correlation with human brain aging progress. Besides, the top hub genes including PPP3CB, CAMSAP1, ACTR3B, and GNG3 were identified and characterized by high connectivity, module membership, or gene significance in the blue module. Furthermore, these genes were validated in mice of different ages. Mechanically, the potential regulators of blue module were investigated. These findings highlight an important role of the blue module and its affiliated genes in the control of normal brain aging, which may lead to potential therapeutic interventions for brain aging by targeting the hub genes.

Feedback control of the CXCR7/CXCL11 chemokine axis by estrogen receptor α in ovarian cancer

Ovarian cancer (OC) is one of the most intractable diseases, exhibiting tremendous molecular heterogeneity and lacking reliable methods for screening, resulting in late diagnosis and widespread peritoneal dissemination. Menopausal estrogen replacement therapy is a well‐recognized risk factor for OC, but little is known about how estrogen might contribute to this disease at the cellular level. This study identifies chemokine receptor CXCR7/ACKR3 as an estrogen‐responsive gene, whose expression is markedly enhanced by estrogen through direct recruitment of ERα and transcriptional active histone modifications in OC cells. The gene encoding CXCR7 chemokine ligand I‐TAC/CXCL11 was also upregulated by estrogen, resulting in Ser‐118 phosphorylation, activation, and recruitment of estrogen receptor ERα at the CXCR7 promoter locus for positive feedback regulation. Both CXCR7 and CXCL11, but not CXCR3 (also recognized to interact with CXCL11), were found to be significantly increased in stromal sections of microdissected tumors and positively correlated in mesenchymal subtype of OC. Estrogenic induction of mesenchymal markers SNAI1, SNAI2, and CDH2 expression, with a consequent increase in cancer cell migration, was shown to depend on CXCR7, indicating a key role for CXCR7 in mediating estrogen upregulation of mesenchymal markers to induce invasion of OC cells. These findings identify a feed‐forward mechanism that sustains activation of the CXCR7/CXCL11 axis under ERα control to induce the epithelial–mesenchymal transition pathway and metastatic behavior of OC cells. Such interplay underlies the complex gene profile heterogeneity of OC that promotes changes in tumor microenvironment and metastatic acquisition.

A network-based signature to predict the survival of non-smoking lung adenocarcinoma

Background

A substantial increase in the number of non-smoking lung adenocarcinoma (LAC) patients has been drawing extensive attention in the past decade. However, effective biomarkers, which could guide the precise treatment, are still limited for identifying high-risk patients. Here, we provide a network-based signature to predict the survival of non-smoking LAC.

Materials and methods

Gene expression profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus. Significant gene co-expression networks and hub genes were identified by Weighted Gene Co-expression Network Analysis. Potential mechanisms and pathways of co-expression networks were analyzed by Gene Ontology. The predictive signature was constructed by penalized Cox regression analysis and tested in two independent datasets.

Results

Two distinct co-expression modules were significantly correlated with the non-smoking status across 4 Gene Expression Omnibus datasets. Gene Ontology revealed that nuclear division and cell cycle pathways were main mechanisms of the blue module and that genes in the turquoise module were involved in lymphocyte activation and cell adhesion pathways. Seventeen genes were selected from hub genes at an optimal lambda value and built the prognostic signature. The prognostic signature distinguished the survival of non-smoking LAC (training: hazard ratio [HR]=3.696, 95% CI: 2.025–6.748, P<0.001; testing: HR=2.9, 95% CI: 1.322–6.789, P=0.006; HR=2.78, 95% CI: 1.658–6.654, P=0.022) and had moderate predictive abilities in the training and validation datasets.

Conclusion

The prognostic signature is a promising predictor of non-smoking LAC patients, which might benefit clinical practice and precision therapeutic management.

AEBP1 promotes epithelial-mesenchymal transition of gastric cancer cells by activating the NF-κB pathway and predicts poor outcome of the patients

Adipocyte enhancer binding protein 1 (AEBP1) is a transcriptional repressor that plays a critical role in regulating adipogenesis. Recent studies have indicated that AEBP1 might function as a candidate oncogene and is overexpressed in several human malignancies. However, the role of AEBP1 in gastric cancer (GC) remains largely unknown. This study aimed to investigate the expression pattern, prognostic significance and biological function of AEBP1 in human gastric cancer and to explore the underlying mechanism. We found that both the mRNA and protein levels of AEBP1 were significantly increased in human GC tissues. Elevated AEBP1 expression was significantly correlated with poor overall survival in patients with both early-stage (Tumor, Node, Metastases (TNM) TNM I and II) and late-stage (TNM III and IV) GC. Silencing AEBP1 markedly suppressed the proliferation, migration, invasion, metastasis and epithelial-mesenchymal transition of GC cells. Moreover, we demonstrated that knockdown of AEBP1 in GC cells led to inhibition of the NF-κB pathway by hampering the degradation of IκBα. Thus, AEBP1 might be served as a promising prognostic indicator and a potential therapeutic target in human GC.

Role of PLK1 signaling pathway genes in gastrointestinal stromal tumors

In previous studies by the authors, aurora kinase A (AURKA) was demonstrated as an independent poor prognostic marker for the recurrence of localized gastrointestinal stromal tumors (GISTs) and for the progression of advanced GISTs. In the present study, the prognostic effect of genes involved in cell cycle regulation in GISTs was further examined. Leading edge analysis in gene set enrichment analysis was used to identify the most common genes in the top 10 enriched gene sets of high-risk patients with GISTs in a Japanese study. The obtained gene list was uploaded to the Pathway Interaction Database to search for critical pathways. Selected genes within the pathway were subsequently verified through immunohistochemistry (IHC) in another cohort of patients. A total of 5 genes in 'PLK1 signaling events,' namely AURKA, polo-like kinase 1 (PLK1), cell division cycle 25C (CDC25C), budding uninhibited by benzimidazoles (BUB1), and targeting protein for Xklp2 (TPX2), were identified for subsequent study. Among the Japanese cohort, all 5 genes, except BUB1, were significant prognostic factors for poor recurrence-free survival (RFS). Among 141 patients enrolled for the IHC study, all 5 genes exhibited variable expression patterns. In the association study, only AURKA exhibited significant overexpression in non-gastric tumors. Although all 5 genes were considered as risk factors for poor RFS based on a univariate analysis, only the mitotic count and expression levels of CDC25C, BUB1, and TPX2 retained prognostic effects in the multivariate analysis. The PLK1 signaling pathway is crucial in the disease progression of GISTs. Genes within this pathway may serve as predictive markers for adjuvant therapy.

Identifying miRNA and gene modules of colon cancer associated with pathological stage by weighted gene co-expression network analysis

Introduction

Colorectal cancer (CRC) is the fourth most common cause of cancer-related mortality worldwide. The tumor, node, metastasis (TNM) stage remains the standard for CRC prognostication. Identification of meaningful microRNA (miRNA) and gene modules or representative biomarkers related to the pathological stage of colon cancer helps to predict prognosis and reveal the mechanisms behind cancer progression.

Materials and methods

We applied a systems biology approach by combining differential expression analysis and weighted gene co-expression network analysis (WGCNA) to detect the pathological stage-related miRNA and gene modules and construct a miRNA–gene network. The Cancer Genome Atlas (TCGA) colon adenocarcinoma (CAC) RNA-sequencing data and miRNA-sequencing data were subjected to WGCNA analysis, and the GSE29623, GSE35602 and GSE39396 were utilized to validate and characterize the results of WGCNA.

Results

Two gene modules (Gmagenta and Ggreen) and one miRNA module were associated with the pathological stage. Six hub genes (COL1A2, THBS2, BGN, COL1A1, TAGLN and DACT3) were related to prognosis and validated to be associated with the pathological stage. Five hub miRNAs were identified to be related to prognosis (hsa-miR-125b-5p, hsa-miR-145-5p, hsa-let-7c-5p, hsa-miR-218-5p and hsa-miR-125b-2-3p). A total of 18 hub genes and seven hub miRNAs were predominantly expressed in tumor stroma. Proteoglycans in cancer, focal adhesion, extracellular matrix (ECM)–receptor interaction and so on were common pathways of the three modules. Hsa-let-7c-5p was located at the core of miRNA–gene network.

Conclusion

These findings help to advance the understanding of tumor stroma in the progression of CAC and provide prognostic biomarkers as well as therapeutic targets.

COL5A1 may contribute the metastasis of lung adenocarcinoma

BACKGROUND

Lung cancer leads to the largest number of cancer-related deaths worldwide and is usually accompanied with metastasis which is the primary cause of those death and correlated with poor prognosis. However, the mechanism of lung cancer metastasis is still lack of definition.

METHODS

We compared the primary lung adenocarcinoma (AD) and its metastasis tissues induced by overexpression of Kras^G12D and inactivation of P53 in mouse lungs by analyzing GSE40222 about the differentially expressed genes (DEGs), pathways and hub genes. And human lung AD databases are used to verify the conversed changes of identified key gene and then followed functional studies are performed to explore the functions of key gene.

RESULTS

We identified 165 genes differentially expressed in lung AD metastasis compared to primary AD. Pathway analysis identified 649 GO biological processes and 8 KEGG pathways, such as ECM-receptor interaction. Biological network interaction identified the hub genes during lung adenocarcinoma metastasis, such as the up-regulated COL5A1, a novel gene in AD metastasis. We found it's also increased in human AD and advanced stage. Knockdown of COL5A1 in human AD metastatic cells inhibited cell growth and invasion, and induced cell apoptosis. Notably, higher expression of COL5A1 was observed in the lung AD patients with recurrence and short survive.

CONCLUSION

By analyzing mouse lung AD and its metastases, we identified the potential key genes and pathways regulating lung AD metastasis, such as COL5A1. The following analysis of COL5A1 in human AD database and cells explores its functions, holding the implications of target therapy in AD metastasis and also providing more clues for future studies.