Overexpression of Topoisomerase 2-Alpha Confers a Poor Prognosis in Pancreatic Adenocarcinoma Identified by Co-Expression Analysis

Predicting severity of inpatient acute cholangitis: combined neutrophil-to-lymphocyte ratio and prognostic nutritional index

The indicators for rapid assessment of the severity of acute cholangitis remain highly debated. Therefore, this study aimed to evaluate the efficacy of various inflammatory and immune-nutritional markers in predicting the severity of acute cholangitis. The prognostic roles of the following markers were investigated: Systemic Immune-Inflammatory Index (SII), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Albumin (Alb), and Prognostic Nutritional Index (PNI). A total of 139 patients with acute cholangitis were included in the study. The inflammatory and immune-nutritional markers with better predictive efficacy were selected to construct a combined predictive score. According to the survival ROC curve analysis, the combined NLR and PNI score, termed PNS, demonstrated the best prognostic performance with an AUC of 0.853. Multivariable survival analysis identified the following independent prognostic factors: PNS (p = 0.010) and Prothrombin Time (PT) (p = 0.003). The results indicate that PNS = 2 is associated with a higher incidence of severe cholangitis.

Recent developments of topoisomerase inhibitors: Clinical trials, emerging indications, novel molecules and global sales

The nucleic acid topoisomerases (TOP) are an evolutionary conserved mechanism to solve topological problems within DNA and RNA that have been historically well-established as a chemotherapeutic target. During investigation of trends within clinical trials, we have identified a very high number of clinical trials involving TOP inhibitors, prompting us to further evaluate the current status of this class of therapeutic agents. In total, we have identified 233 unique molecules with TOP-inhibiting activity. In this review, we provide an overview of the clinical drug development highlighting advances in current clinical uses and discussing novel drugs and indications under development. A wide range of bacterial infections, along with solid and hematologic neoplasms, represent the bulk of clinically approved indications. Negative ADR profile and drug resistance among the antibacterial TOP inhibitors and anthracycline-mediated cardiotoxicity in the antineoplastic TOP inhibitors are major points of concern, subject to continuous research efforts. Ongoing development continues to focus on bacterial infections and cancer; however, there is a degree of diversification in terms of novel drug classes and previously uncovered indications, such as glioblastoma multiforme or Clostridium difficile infections. Preclinical studies show potential in viral, protozoal, parasitic and fungal infections as well and suggest the emergence of a novel target, TOP IIIβ. We predict further growth and diversification of the field thanks to the large number of experimental TOP inhibitors emerging.

Integrated bioinformatics reveals genetic links between visceral obesity and uterine tumors

Visceral obesity (VO), characterized by excess fat around internal organs, is a recognized risk factor for gynecological tumors, including benign uterine leiomyoma (ULM) and malignant uterine leiomyosarcoma (ULS). Despite this association, the shared molecular mechanisms remain underexplored. This study utilizes an integrated bioinformatics approach to elucidate common molecular pathways and identify potential therapeutic targets linking VO, ULM, and ULS. We analyzed gene expression datasets from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) in each condition. We found 101, 145, and 18 DEGs in VO, ULM, and ULS, respectively, with 37 genes overlapping across all three conditions. Functional enrichment analysis revealed that these overlapping DEGs were significantly enriched in pathways related to cell proliferation, immune response, and transcriptional regulation, suggesting shared biological processes. Protein-protein interaction network analysis identified 14 hub genes, of which TOP2A, APOE, and TYMS showed significant differential expression across all three conditions. Drug-gene interaction analysis identified 26 FDA-approved drugs targeting these hub genes, highlighting potential therapeutic opportunities. In conclusion, this study uncovers shared molecular pathways and actionable drug targets across VO, ULM, and ULS. These findings deepen our understanding of disease etiology and offer promising avenues for drug repurposing. Experimental validation is needed to translate these insights into clinical applications and innovative treatments.

The Prognostic Hub Gene POLE2 Promotes BLCA Cell Growth via the PI3K/AKT Signaling Pathway

BACKGROUND

BLCA is a common urothelial malignancy characterized by a high recurrence rate. Despite its prevalence, the molecular mechanisms underlying its development remain unclear.

AIMS

This study aimed to explore new prognostic biomarkers and investigate the underlying mechanism of bladder cancer (BLCA).

OBJECTIVE

The objective of this study is to identify key prognostic biomarkers for BLCA and to elucidate their roles in the disease.

METHODS

We first collected the overlapping DEGs from GSE42089 and TCGA-BLCA samples for the subsequent weighted gene co-expression network analysis (WGCNA) to find a key module. Then, key module genes were analyzed by the MCODE algorithm, prognostic risk model, expression and immunohistochemical staining to identify the prognostic hub gene. Finally, the hub gene was subjected to clinical feature analysis, as well as cellular function assays.

RESULTS

In WGCNA on 1037 overlapping genes, the blue module was the key module. After a series of bioinformatics analyses, POLE2 was identified as a prognostic hub gene in BLCA from potential genes (TROAP, POLE2, ANLN, and E2F8). POLE2 level was increased in BLCA and related to different clinical features of BLCA patients. Cellular assays showed that si-POLE2 inhibited BLCA proliferation, and si-POLE2+ 740Y-P in BLCA cells up-regulated the PI3K and AKT protein levels.

CONCLUSION

In conclusion, POLE2 was identified to be a promising prognostic biomarker as an oncogene in BLCA. It was also found that POLE2 exerts a promoting function by the PI3K/AKT signaling pathway in BLCA.

Discovery of BAR502, as potent steroidal antagonist of leukemia inhibitory factor receptor for the treatment of pancreatic adenocarcinoma

Introduction

The leukemia inhibitory factor (LIF), is a cytokine belonging to IL-6 family, whose overexpression correlate with poor prognosis in cancer patients, including pancreatic ductal adenocarcinoma (PDAC). LIF signaling is mediate by its binding to the heterodimeric LIF receptor (LIFR) complex formed by the LIFR receptor and Gp130, leading to JAK1/STAT3 activation. Bile acids are steroid that modulates the expression/activity of membrane and nuclear receptors, including the Farnesoid-X-Receptor (FXR) and G Protein Bile Acid Activated Receptor (GPBAR1).

Methods

Herein we have investigated whether ligands to FXR and GPBAR1 modulate LIF/LIFR pathway in PDAC cells and whether these receptors are expressed in human neoplastic tissues.

Results

The transcriptome analysis of a cohort of PDCA patients revealed that expression of LIF and LIFR is increased in the neoplastic tissue in comparison to paired non-neoplastic tissues. By in vitro assay we found that both primary and secondary bile acids exert a weak antagonistic effect on LIF/LIFR signaling. In contrast, BAR502 a non-bile acid steroidal dual FXR and GPBAR1 ligand, potently inhibits binding of LIF to LIFR with an IC50 of 3.8 µM.

Discussion

BAR502 reverses the pattern LIF-induced in a FXR and GPBAR1 independent manner, suggesting a potential role for BAR502 in the treatment of LIFR overexpressing-PDAC.

Recognition of Key Genes in Human Anaplastic Thyroid Cancer via the Weighing Gene Coexpression Network

Methods

For determining pathways and key genes that have relation with development of ATC, differentially expressed genes (DEGs) from GSE33630 as well as GSE65144 expression microarray were screened. Furthermore, we also worked on carrying out the task of constructing a protein-protein interaction (PPI) network and the work of weighing gene coexpression network (WGCNA). DAVID was utilized for the performance of the Gene Ontology (GO) as well as KEGG pathway enrichment analyses for DEGs. We used TCGA THCA data and GSE53072 to further verify the hub gene and hub pathway.

Results

We came to the conclusion of the recognition of a total of 1063 genes as DEGs. Analysis regarding functional and pathway enrichment showed that there existed a notable enrichment of upregulated DEGs in the organization of extracellular structure and matrix organization, as well as in organelle fission and nuclear division. The downregulated DEG was markedly gathered in the thyroid hormone metabolic process and generation, as well as in the metabolic process of cellular modified amino acid. We identified 10 hub genes (CXCL8, CDH1, AURKA, CCNA2, FN1, CDK1, ITGAM, CDC20, MMP9, and KIF11) through the PPI network, which might be strongly linked to the carcinogenesis and the development of ATC. In the coexpression network, 6 modules that were relevant to ATC were recognized. The modules were related to the interaction of signaling pathway of p53, Hippo, PI3K/Akt, and ECM-receptor. This hub genes and hub pathway were further successfully validated as a potential biomarker for carcinogenesis and prediction in another database GSE53072.

Conclusion

To summarize, this research displayed an illustration of hub genes and pathways that had relation with ATC development, which suggested that DEGs and hub genes, recognized on the basis of bioinformatics analyses, were valuable in the diagnosis for patients with ATC.

Implications of prognosis-associated genes in pancreatic tumor metastasis: lessons from global studies in bioinformatics

Pancreatic cancer (PC) is a highly lethal malignancy with a 5-year survival rate of 10%. The occurrence of metastasis, among other hallmarks, is the main contributor to its poor prognosis. Consequently, the elucidation of metastatic genes involved in the aggressive nature of the disease and its poor prognosis will result in the development of new treatment modalities for improved management of PC. There is a deep interest in understanding underlying disease pathology, identifying key prognostic genes, and genes associated with metastasis. Computational approaches, which have become increasingly relevant over the last decade, are commonly used to explore such interests. This review aims to address global studies that have employed global approaches to identify prognostic and metastatic genes, while highlighting their methods and limitations. A panel of 48 prognostic genes were identified across these studies, but only five, including ANLN, ARNTL2, PLAU, TOP2A, and VCAN, were validated in multiple studies and associated with metastasis. Their association with metastasis has been further explored here, and the implications of these genes in the metastatic cascade have been interpreted.

Screening Prognosis-Related lncRNAs Based on WGCNA to Establish a New Risk Score for Predicting Prognosis in Patients with Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) remains an important cause of cancer death. The molecular mechanism of hepatocarcinogenesis and prognostic factors of HCC have not been completely uncovered.

METHODS

In this study, we screened out differentially expressed lncRNAs (DE lncRNAs), miRNAs (DE miRNAs), and mRNAs (DE mRNAs) by comparing the gene expression of HCC and normal tissue in The Cancer Genome Atlas (TCGA) database. DE mRNAs were used to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the miRNA and lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a competitive endogenous RNA (ceRNA) network by weighted gene coexpression network analysis (WGCNA). Moreover, univariable Cox regression and Kaplan-Meier curve analyses of DE lncRNAs and DE mRNAs were conducted. Finally, the lasso-penalized Cox regression analysis and nomogram model were used to establish a new risk scoring system and predict the prognosis of patients with liver cancer. The expression of survival-related DE lncRNAs was verified by qRT-PCR.

RESULTS

A total of 1896 DEmRNAs, 330 DElncRNAs, and 76 DEmiRNAs were identified in HCC and normal tissue samples. Then, the turquoise miRNA and turquoise lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a ceRNA network by WGCNA. In this ceRNA network, there were 566 lncRNA-miRNA-mRNA regulatory pairs, including 30 upregulated lncRNAs, 16 downregulated miRNAs, and 75 upregulated mRNAs. Moreover, we screened out 19 lncRNAs and 14 hub mRNAs related to prognosis from this ceRNA network by univariable Cox regression and Kaplan-Meier curve analyses. Finally, a new risk scoring system was established by selecting the optimal risk lncRNAs from the 19 prognosis-related lncRNAs through lasso-penalized Cox regression analysis. In addition, we established a nomogram model consisting of independent prognostic factors to predict the survival rate of HCC patients. Finally, the correlation between the risk score and immune cell infiltration and gene set enrichment analysis were determined.

CONCLUSIONS

In conclusion, the results may provide potential biomarkers or therapeutic targets for HCC and the establishment of the new risk scoring system and nomogram model provides the new perspective for predicting the prognosis of HCC.

Identification of Biomarkers for Predicting Allograft Rejection following Kidney Transplantation Based on the Weighted Gene Coexpression Network Analysis

Kidney transplantation is the promising treatment of choice for chronic kidney disease and end-stage kidney disease and can effectively improve the quality of life and survival rates of patients. However, the allograft rejection following kidney transplantation has a negative impact on transplant success. Therefore, the present study is aimed at screening novel biomarkers for the diagnosis and treatment of allograft rejection following kidney transplantation for improving long-term transplant outcome. In the study, a total of 8 modules and 3065 genes were identified by WGCNA based on the GSE46474 and GSE15296 dataset from the Gene Expression Omnibus (GEO) database. Moreover, the results of Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that these genes were mainly involved in the immune-related biological processes and pathways. Thus, 317 immune-related genes were selected for further analysis. Finally, 5 genes (including CD200R1, VAV2, FASLG, SH2D1B, and RAP2B) were identified as the candidate biomarkers based on the ROC and difference analysis. Furthermore, we also found that in the 5 biomarkers an interaction might exist among each other in the protein and transcription level. Taken together, our study identified CD200R1, VAV2, FASLG, SH2D1B, and RAP2B as the candidate diagnostic biomarkers, which might contribute to the prevention and treatment of allograft rejection following kidney transplantation.

Prognostic Value and Significant Pathway Exploration Associated with TOP2A Involved in Papillary Thyroid Cancer

Background

Topoisomerase 2-alpha (TOP2A) has been identified as a hub gene that played an important role in the initiation and progression of thyroid carcinoma (THCA). However, the exact function of TOP2A in papillary thyroid cancer (PTC) remained elusive. The current study aimed to evaluate the TOP2A expression, prognosis significance and key signaling pathways involved in PTC.

Methods

We firstly evaluated the expression of TOP2A in PTC via UALCAN, cBioportal, HPA and LinkdedOmics databases. Genetic alteration of TOP2A in PTC was then explored in cBioportal. Prognostic impacts of TOP2A expression on disease-free survival (DFS) of PTC patients were subsequently evaluated using Kaplan–Meier plotter and Gepia databases. Taking gender, age, cancer stage, T, N and M stages into consideration, we compared survival difference between TOP2A high and low expression groups. KEGG pathway analysis in WebGestalt and GSEA analysis were further performed to reveal the potential TOP2A-associated signaling pathways involved in PTC. Finally, the upstream microRNAs of TOP2A were assessed using DIANA, TargetScan, miRDB and miRWALK database, followed by mechanism exploration of upstream microRNAs.

Results

1) The mRNA and protein of TOP2A were highly expressed in PTC tissue compared with normal thyroid tissue. TOP2A expression was associated with patient’s age, N stage and cancer stage (all P<0.05). TOP2A protein was mainly localized to nucleoplasm. 2) Most of samples occurred the missense substitution, and mutation site was located at K1199E. Nucleotide mutations were mainly presented as G>A (35.29%). 3) TOP2A high expression significantly influenced the DFS of PTC patients (P=0.015). Restricted survival analysis showed that TOP2A high expression caused poorer DFS of female patients (P=0.003) and those with age <60 years old (P=0.002), early clinical stage (P=0.012), N0 stage (P=0.002) or M0 stage (P=0.040). 4) Pathway analysis suggested that TOP2A positively participated in the cell cycle, oocyte meiosis and p53 signaling pathways (all P<0.05) involved in thyroid cancer.

Conclusion

The expression of TOP2A was higher in PTC tissue, which resulted in a worse DFS of patients with PTC. TOP2A might act as an effective therapeutic target for PTC treatment.

miR-340 Promotes Retinoblastoma Cell Proliferation, Migration and Invasion Through Targeting WIF1

Background

MicroRNAs (miRNAs) function as important regulators of gene expression involved in tumor pathogenesis, including retinoblastoma. However, the expression profiles and potential roles in retinoblastoma are still largely unclear.

Material and Methods

Differentially expressed miRNAs (DEmiRs) and genes (DEGs) in retinoblastoma were extracted from Gene Expression Omnibus (GEO) repository. Expression levels of miR-340 and WIF1 were detected in retinoblastoma tissues and cell lines by qRT-PCR. Both gain-of-function and loss-of-function experiments were performed to explore the effects of miR-340 on cell proliferation, migration and invasion. Bioinformatics analysis and luciferase reporter assay were used to explore the interaction between miR-340 and WIF1.

Results

A total of 11 DEmiRs were identified in retinoblastoma tissue and blood samples. Among them, we validated that miR-340 was the most highly expressed miRNA and correlated with tumor size, ICRB stage and optic nerve invasion. miR-340 was observed to enhance the proliferation, migration and invasion capacity of retinoblastoma cells. We then identified 26 DEGs from 3 retinoblastoma GEO datasets and subsequently constructed a miRNA–mRNA regulatory network. Further analysis revealed that WIF1 was a direct target of miR-340. Moreover, overexpression of WIF1 could repress retinoblastoma progression induced by miR-340 in vitro and in vivo.

Conclusion

Collectively, miR-340 functioned as an oncomiRNA to promote retinoblastoma cell proliferation, migration and invasion via regulating WIF1. Our data also provided multiple miRNAs and genes that may contribute to a better understanding of retinoblastoma pathogenesis.

The Value of Immune-Related Genes Signature in Osteosarcoma Based on Weighted Gene Co-expression Network Analysis

Background. Osteosarcoma (OS) is a serious malignant tumor that is more common in adolescents or children under 20 years of age. This study is aimed at obtaining immune-related genes (IRGs) associated with the progression and prognosis of OS. Method. Expression profiling data and clinical data for OS were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. ESTIMATE calculates immune scores and stromal scores of samples and performs the prognostic analysis. Weighted gene coexpression network analysis (WGCNA) was used to find modules correlated with immune and stromal scores. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to explore IRGs associated with OS prognosis and construct and validate a hazard score model. Finally, we verified the expression and function of EVI2B in OS. Results. WGCNA selected twenty-eight IRGs, 10 of which were associated with OS prognosis, and LASSO further obtained three key prognostic genes. A prognostic model of EVI2B was constructed, and according to the risk score model, patients in the high-risk group had a worse prognosis than those in the low-risk group, and the prognosis was statistically significant in the high- and low-risk groups. Receiver operating characteristic (ROC) curves were used to assess the prognostic model’s accuracy and externally validate the independent GSE21257 cohort. The results of immunohistochemical staining and qPCR showed that EVI2B was a tumor suppressor gene. The differential genes in the high- and low-risk groups were analyzed by enrichment analysis of GO and KEGG, indicating that the EVI2B model is associated with immune response. Conclusion. In this study, IRG EVI2B is closely related to OS’s prognosis and can be used as a potential biomarker for prognosis and treatment of OS.

Integrated analysis identifies oxidative stress genes associated with progression and prognosis in gastric cancer

Oxidative stress (OS) reactions are reported to be associated with oncogenesis and tumor progression. However, little is known about the potential diagnostic value of OS in gastric cancer (GC). This study identified hub OS genes associated with the prognosis and progression of GC and illustrated the underlying mechanisms. The transcriptome data and corresponding GC clinical information were collected from The Cancer Genome Atlas (TCGA) database. Aberrantly expressed OS genes between tumors and adjacent normal tissues were screened, and 11 prognosis-associated genes were identified with a series of bioinformatic analyses and used to construct a prognostic model. These genes were validated in the Gene Expression Omnibus (GEO) database. Furthermore, weighted gene co-expression network analysis (WGCNA) was subsequently conducted to identify the most significant hub genes for the prediction of GC progression. Analysis revealed that a good prognostic model was constructed with a better diagnostic accuracy than other clinicopathological characteristics in both TCGA and GEO cohorts. The model was also significantly associated with the overall survival of patients with GC. Meanwhile, a nomogram based on the risk score was established, which displayed a favorable discriminating ability for GC. In the WGCNA analysis, 13 progression-associated hub OS genes were identified that were also significantly associated with the progression of GC. Furthermore, functional and gene ontology (GO) analyses were performed to reveal potential pathways enriched with these genes. These results provide novel insights into the potential applications of OS-associated genes in patients with GC.

Identification of key protein-coding genes in lung adenocarcinomas based on bioinformatic analysis

Background

Lung cancer is one of the most common cancers and the primary cause of cancer-related deaths in the world. The 5-year survival of lung cancer patients is lower than 15%. As a common subtype of lung cancer, lung adenocarcinoma still has a high morbidity and mortality, although many strategies have been made, such as surgical operation, chemotherapy, targeted therapy. The use of gene expression microarray has provided a feasible and effective approach for the study on lung cancer. However, the biomarkers and potential therapeutic targets of lung adenocarcinomas are still not completely identified. Our study is aimed to find biomarkers and therapeutic targets of lung adenocarcinomas by identifying the key protein-coding gene in lung adenocarcinomas by bioinformatical approaches.

Methods

We selected and obtained messenger RNA microarray datasets from Gene Expression Omnibus database to identify differentially expressed genes between lung adenocarcinomas and normal lung tissue. The differentially expressed genes were clarified by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, the protein-protein interaction (PPI) network and statistical analyses. Subsequently, quantitative real-time PCR was used to verify the results of bioinformatic analysis.

Results

We obtained 1,264, 896 and 408 differentially expressed genes from GSE32863, GSE43458 and GSE63459, respectively. The 242 common differentially expressed genes in three datasets were related to cell adhesion molecules, ECM-receptor interaction, leukocyte transendothelial migration according to KEGG analysis. GO analysis showed that these common differentially expressed genes were enriched in tumor-related functions. ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T and KIAA0101 have the strongest protein-protein interaction relationships based on protein-protein interaction networks. Survival analysis showed that these nine genes were closely related to the survival of lung adenocarcinomas. The further qRT-PCR assays indicated that seven key genes (ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T) display differential profile between clinical lung adenocarcinoma specimens and their matched normal tissues.

Conclusions

ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T may be key protein coding genes in lung adenocarcinoma, and deserve further study to verify their feasibility and effectiveness as biomarkers and therapeutic targets for lung adenocarcinomas.

Integrated analysis of co-expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer

Long non‐coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co‐expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan‐Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co‐expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2‐AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.

A robust 6-mRNA signature for prognosis prediction of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies worldwide. PDAC prognostic and diagnostic biomarkers are still being explored. The aim of this study is to establish a robust molecular signature that can improve the ability to predict PDAC prognosis. 155 overlapping differentially expressed genes between tumor and non-tumor tissues from three Gene Expression Omnibus (GEO) datasets were explored. A least absolute shrinkage and selection operator method (LASSO) Cox regression model was employed for selecting prognostic genes. We developed a 6-mRNA signature that can distinguish high PDAC risk patients from low risk patients with significant differences in overall survival (OS). We further validated this signature prognostic value in three independent cohorts (GEO batch, P < 0.0001; ICGC, P = 0.0036; Fudan, P = 0.029). Furthermore, we found that our signature remained significant in patients with different histologic grade, TNM stage, locations of tumor entity, age and gender. Multivariate cox regression analysis showed that 6-mRNA signature can be an independent prognostic marker in each of the cohorts. Receiver operating characteristic curve (ROC) analysis also showed that our signature possessed a better predictive role of PDAC prognosis. Moreover, the gene set enrichment analysis (GSEA) analysis showed that several tumorigenesis and metastasis related pathways were indeed associated with higher scores of risk. In conclusion, identifying the 6-mRNA signature could provide a valuable classification method to evaluate clinical prognosis and facilitate personalized treatment for PDAC patients. New therapeutic targets may be developed upon the functional analysis of the classifier genes and their related pathways.

Identification of Key Genes and Pathways in Pancreatic Cancer Gene Expression Profile by Integrative Analysis

Background: Pancreatic cancer is one of the malignant tumors that threaten human health. Methods: The gene expression profiles of GSE15471, GSE19650, GSE32676 and GSE71989 were downloaded from the gene expression omnibus database including pancreatic cancer and normal samples. The differentially expressed genes between the two types of samples were identified with the Limma package using R language. The gene ontology functional and pathway enrichment analyses of differentially-expressed genes were performed by the DAVID software followed by the construction of a protein–protein interaction network. Hub gene identification was performed by the plug-in cytoHubba in cytoscape software, and the reliability and survival analysis of hub genes was carried out in The Cancer Genome Atlas gene expression data. Results: The 138 differentially expressed genes were significantly enriched in biological processes including cell migration, cell adhesion and several pathways, mainly associated with extracellular matrix-receptor interaction and focal adhesion pathway in pancreatic cancer. The top hub genes, namely thrombospondin 1, DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were identified from the protein–protein interaction network. The expression levels of hub genes were consistent with data obtained in The Cancer Genome Atlas. DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were significantly linked with poor survival in pancreatic adenocarcinoma. Conclusions: These hub genes may be used as potential targets for pancreatic cancer diagnosis and treatment.

nm23, TOP2A and VEGF expression: Potential prognostic biologic factors in peripheral T-cell lymphoma, not otherwise specified

Peripheral T-cell lymphoma not otherwise specified (PTCL-NOS) is an aggressive lymphoma associated with a poor outcome. To date, the factor consistently associated with prognosis is the International Prognostic Index (IPI) score; however, it is considered that the IPI score cannot be beneficial for guiding potential targeted therapies. New scoring systems have recently been developed. The aim of the present study was to observe the expression of NME/NM23 nucleoside diphosphate kinase 1 (nm23), nuclear DNA topoisomerase 2-α (TOP2A), multiple myeloma oncogene-1 (MUM-1) and vascular endothelial growth factor (VEGF), and evaluate their prognostic value in PTCL-NOS. A retrospective analysis of 124 cases of PTCL-NOS showed that 70/122 (57.4%) cases were positive for nm23, 71/122 (58.2%) for TOP2A, 30/119 (25.2%) for MUM-1 and 64/122 (52.5%) for VEGF. Of note, 50/122 cases concurrently expressed nm23, TOP2A and VEGF. The univariate analysis results revealed that the nm23 (P=0.012), TOP2A (P=0.002) and VEGF (P=0.008) expression had a negative prognostic effect in patients with PTCL-NOS, while the MUM-1 expression did not have a significant prognostic value (P=0.918). In addition, the concurrent expression of nm23, TOP2A and VEGF was significantly associated with a worse prognosis (P=0.002). However, in multivariate Cox regression analysis, the concurrent expression of nm23, TOP2A and VEGF tended to predict a worse prognosis, however the P-value was borderline (hazard ratio, 1.495; 95% confidence interval, 0.993-2.250; P=0.054). It is speculated that there may be an association among the expression of nm23, TOP2A and VEGF, and that their expression may serve as a promising prognostic factor for PTCL-NOS.

Extracting predictors for lung adenocarcinoma based on Granger causality test and stepwise character selection

Background

Lung adenocarcinoma is the most common type of lung cancer, with high mortality worldwide. Its occurrence and development were thoroughly studied by high-throughput expression microarray, which produced abundant data on gene expression, DNA methylation, and miRNA quantification. However, the hub genes, which can be served as bio-markers for discriminating cancer and healthy individuals, are not well screened.

Result

Here we present a new method for extracting gene predictors, aiming to obtain the least predictors without losing the efficiency. We firstly analyzed three different expression microarrays and constructed multi-interaction network, since the individual expression dataset is not enough for describing biological behaviors dynamically and systematically. Then, we transformed the undirected interaction network to directed network by employing Granger causality test, followed by the predictors screened with the use of the stepwise character selection algorithm. Six predictors, including TOP2A, GRK5, SIRT7, MCM7, EGFR, and COL1A2, were ultimately identified. All the predictors are the cancer-related, and the number is very small fascinating diagnosis. Finally, the validation of this approach was verified by robustness analyses applied to six independent datasets; the precision is up to 95.3% ∼ 100%.

Conclusion

Although there are complicated differences between cancer and normal cells in gene functions, cancer cells could be differentiated in case that a group of special genes expresses abnormally. Here we presented a new, robust, and effective method for extracting gene predictors. We identified as low as 6 genes which can be taken as predictors for diagnosing lung adenocarcinoma.

ITLNI identified by comprehensive bioinformatic analysis as a hub candidate biological target in human epithelial ovarian cancer

Background

Epithelial ovarian cancer (EOC) is a female malignant tumor. Bioinformatics has been widely utilized to analyze genes related to cancer progression. Targeted therapy for specific biological factors has become more valuable.

Materials and methods

Gene expression profiles of GSE18520 and GSE27651 were downloaded from Gene Expression Omnibus. We used the “limma” package to screen differentially expressed genes (DEGs) between EOC and normal ovarian tissue samples and then used Clusterprofiler to do functional and pathway enrichment analyses. We utilized Search Tool for the Retrieval of Interacting Genes Database to assess protein–protein interaction (PPI) information and the plug-in Molecular Complex Detection to screen hub modules of PPI network in Cytoscape, and then performed functional analysis on the genes in the hub module. Next, we utilized the Weighted Gene Expression Network Analysis package to establish a co-expression network. Validation of the key genes in databases and Gene Expression Profiling Interactive Analysis (GEPIA) were completed. Finally, we used quantitative real-time PCR to validate hub gene expression in clinical tissue samples.

Results

We analyzed the DEGs (96 samples of EOC tissue and 16 samples of normal ovarian tissue) for functional analysis, which showed that upregulated DEGs were strikingly enriched in phosphate ion binding and the downregulated DEGs were significantly enriched in glycosaminoglycan binding. In the PPI network, CDK1 was screened as the most relevant protein. In the co-expression network, one EOC-related module was identified. For survival analysis, database and clinical sample validation of genes in the turquoise module, we found that ITLN1 was positively correlated with EOC prognosis and had lower level in EOC than in normal tissues, which was consistent with the results predicted in GEPIA.

Conclusion

In this study, we exhibited the key genes and pathways involved in EOC and speculated that ITLN1 was a tumor suppressor which could be used as a potential biomarker for treating EOC, Gene Expression Omnibus, prognosis.

Analysis of gene co‑expression network reveals prognostic significance of CNFN in patients with head and neck cancer

In patients with head and neck cancer (HNC), lymph node (N) metastases are associated with cancer aggressiveness and poor prognosis. Identifying meaningful gene modules and representative biomarkers relevant to the N stage helps predict prognosis and reveal mechanisms underlying tumor progression. The present study used a step-wise approach for weighted gene co-expression network analysis (WGCNA). Dataset GSE65858 was subjected to WGCNA. RNA sequencing data of HNC downloaded from the Cancer Genome Atlas (TCGA) and dataset GSE39366 were utilized to validate the results. Following data preprocessing, 4,295 genes were screened, and blue and black modules associated with the N stage of HNC were identified. A total of 16 genes [keratinocyte differentiation associated protein, suprabasin, cornifelin (CNFN), small proline rich protein 1B, desmoglein 1 (DSG1), chromosome 10 open reading frame 99, keratin 16 pseudogene 3, gap junction protein β2, dermokine, LY6/PLAUR domain containing 3, transmembrane protein 79, phospholipase A2 group IVE, transglutaminase 5, potassium two pore domain channel subfamily K member 6, involucrin, kallikrein related peptidase 8] that had a negative association with the N-stage in the blue module, and two genes (structural maintenance of chromosomes 4 and mutS homolog 6) that had a positive association in the black module, were identified to be candidate hub genes. Following further validation in TCGA and dataset GSE65858, it was identified that CNFN and DSG1 were associated with the clinical stage of HNC. Survival analysis of CNFN and DSG1 was subsequently performed. Patients with increased expression of CNFN displayed better survival probability in dataset GSE65858 and TCGA. Therefore, CNFN was selected as the hub gene for further verification in the Gene Expression Profiling Interactive Analysis database. Finally, functional enrichment and gene set enrichment analyses were performed using datasets GSE65858 and GSE39366. Three gene sets, namely ‘P53 pathway’, ‘estrogen response early’ and ‘estrogen response late’, were enriched in the two datasets. In conclusion, CNFN, identified via the WGCNA algorithm, may contribute to the prediction of lymph node metastases and prognosis, probably by regulating the pathways associated with P53, and the early and late estrogen response.

Down-regulation of MiR-539 Indicates Poor Prognosis in Patients with Pancreatic Cancer

It has been demonstrated that miR-539 plays an important role in the development and progression of tumors. The purpose of this study was to analyze the correlation between the expression level of miR-539 and the clinicopathological features and prognosis of patients with pancreatic cancer. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to analyze the expression level of miR-539 in 60 patients with pancreatic cancer. It was found that miR-539 gene expression was down-regulated in pancreatic cancer compared with that in paracancerous tissues. In addition, the expression level of miR-539 was inversely correlated with tumor differentiation (poorly to moderately differentiated vs. well differentiated, P=0.006), lymph node metastasis (positive vs. negative, P=0.006), clinical stage (III-IV vs. I-II, P=0.002), CA199 (≥200 vs. <200, P=0.019) and distant metastasis (positive vs. negative, P=0.035). The survival time of pancreatic cancer patients with low expression of miR-539 was significantly shorter than that of patients with high expression of miR-539. Multivariate analysis suggested that miR-539 expression level was an independent prognostic indicator for patients with pancreatic cancer (P=0.025). Down-regulation of miR-539 may be a potentially unfavorable prognostic factor for patients with pancreatic cancer, and further studies are needed to confirm our conclusion in the future.

A six-mRNA prognostic model to predict survival in head and neck squamous cell carcinoma

BACKGROUND

Transcriptional dysregulation is one of the most important features of cancer genesis and progression. Applying gene expression dysregulation information to predict the development of cancers is useful for cancer diagnosis. However, previous studies mainly focused on the relationship between a single gene and cancer. Prognostic prediction using combined gene models remains limited.

MATERIALS AND METHODS

Gene expression profiles were downloaded from The Cancer Genome Atlas and the data sets were randomly divided into training data sets and test data sets. A six-gene signature associated with head and neck squamous cell carcinoma (HNSCC) and overall survival (OS) was identified according to a training cohort by using weighted gene correlation network analysis and least absolute shrinkage and selection operator Cox regression. The test data set and gene expression omnibus (GEO) data set were used to validate this signature.

RESULTS

We identified six candidate genes, namely, FOXL2NB, PCOLCE2, SPINK6, ULBP2, KCNJ18, and RFPL1, and, using a six-gene model, predicted the risk of death of head and neck squamous cell carcinoma in The Cancer Genome Atlas. At a selected cutoff, patients were clustered into low- and high-risk groups. The OS curves of the two groups of patients had significant differences, and the time-dependent receiver operating characteristics of OS, disease-specific survival (DSS), and progression-free survival (PFS) were as high as 0.766, 0.731, and 0.623, respectively. Then, the test data set and the GEO data set were used to evaluate our model, and we found that the OS time in the high-risk group was significantly shorter than in the low-risk group in both data sets, and the receiver operating characteristics of test data set were 0.669, 0.675, and 0.614, respectively. Furthermore, univariate and multivariate Cox regression analyses showed that the risk score was independent of clinicopathological features.

CONCLUSION

The six-gene model could predict the OS of HNSCC patients and improve therapeutic decision-making.

Several genes involved in the JAK-STAT pathway may act as prognostic markers in pancreatic cancer identified by microarray data analysis

PURPOSE

This study aimed to identify the underlying mechanisms in pancreatic cancer (PC) carcinogenesis and those as potential prognostic biomarkers, which can also be served as new therapeutic targets of PC.

METHODS

Differentially expressed genes (DEGs) were identified between PC tumor tissues and adjacent normal tissue samples from a public GSE62452 dataset, followed by functional and pathway enrichment analysis. Then, protein-protein interaction (PPI) network was constructed and prognosis-related genes were screened based on genes in the PPI network, before which prognostic gene-related miRNA regulatory network was constructed. Functions of the prognostic gene in the network were enriched before which Kaplan-Meier plots were calculated for significant genes. Moreover, we predicted related drug molecules based on target genes in the miRNA regulatory network. Furthermore, another independent GSE60979 dataset was downloaded to validate the potentially significant genes.

RESULTS

In the GSE62452 dataset, 1017 significant DEGs were identified. Twenty-six important prognostic-related genes were found using multivariate Cox regression analysis. Through pathway enrichment analysis and miRNA regulatory analysis, we found that the 5 genes, such as Interleukin 22 Receptor Subunit Alpha 1 (IL22RA1), BCL2 Like 1 (BCL2L1), STAT1, MYC Proto-Oncogene (MYC), and Signal Transducer And Activator Of Transcription 2 (STAT2), involved in the Jak-STAT signaling pathway were significantly associated with prognosis. Moreover, the expression change of these 5 genes was further validated using another microarray dataset. Additionally, we identified camptothecin as an effective drug for PC.

CONCLUSION

IL22RA1, BCL2L1, STAT1, MYC, and STAT2 involved in the Jak-STAT signaling pathway may be significantly associated with prognosis of PC.

Identification of core genes in ovarian cancer by an integrative meta-analysis

BACKGROUND

Epithelial ovarian cancer is one of the most severe public health threats in women. Since it is still challenging to screen in early stages, identification of core genes that play an essential role in epithelial ovarian cancer initiation and progression is of vital importance.

RESULTS

Seven gene expression datasets (GSE6008, GSE18520, GSE26712, GSE27651, GSE29450, GSE36668, and GSE52037) containing 396 ovarian cancer samples and 54 healthy control samples were analyzed to identify the significant differentially expressed genes (DEGs). We identified 563 DEGs, including 245 upregulated and 318 downregulated genes. Enrichment analysis based on the gene ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways showed that the upregulated genes were significantly enriched in cell division, cell cycle, tight junction, and oocyte meiosis, while the downregulated genes were associated with response to endogenous stimuli, complement and coagulation cascades, the cGMP-PKG signaling pathway, and serotonergic synapse. Two significant modules were identified from a protein-protein interaction network by using the Molecular Complex Detection (MCODE) software. Moreover, 12 hub genes with degree centrality more than 29 were selected from the protein-protein interaction network, and module analysis illustrated that these 12 hub genes belong to module 1. Furthermore, Kaplan-Meier analysis for overall survival indicated that 9 of these hub genes was correlated with poor prognosis of epithelial ovarian cancer patients.

CONCLUSION

The present study systematically validates the results of previous studies and fills the gap regarding a large-scale meta-analysis in the field of epithelial ovarian cancer. Furthermore, hub genes that could be used as a novel biomarkers to facilitate early diagnosis and therapeutic approaches are evaluated, providing compelling evidence for future genomic-based individualized treatment of epithelial ovarian cancer.

Identifying a Novel Biomarker TOP2A of Clear Cell Renal Cell Carcinoma (ccRCC) Associated with Smoking by Co-Expression Network Analysis

Although it is well known that smoking is one of pathogenesis of clear cell renal cell carcinoma (ccRCC), the underlying molecular mechanism is still unclear. In our study, the microarray dataset GSE46699 is analyzed by weighted gene co-expression network analysis (WGCNA). Then we identify 15 co-expressed gene modules in which the lightcyan module (R² = 0.30) is the most significant. Combined with the protein-protein interaction (PPI) network and WGCNA, two hub genes are identified. Meanwhile, linear regression analyses indicate that TOP2A has a higher connection with smoking in ccRCC, survival analysis proved that overexpression of TOP2A in ccRCC could lead to shorter survival time. Furthermore, bioinformatical analyses based on GSE46699 and GSE2109 as well as qRT-PCR experiment show similar results that TOP2A is significantly up-regulated in smoking ccRCC compared to non-smoking ccRCC samples. In addition, Functional analysis, pathway enrichment analysis and gene set enrichment analysis (GSEA) indicate that high expression of TOP2A is related to cell cycle and p53 signaling pathway in ccRCC samples. Moreover, in vitro experiments revealed that TOP2A induced cell cycle arrest at G2 phase and proliferation inhibition via p53 phosphorylation. Taken together, by using WGCNA, we have identified a novel biomarker named TOP2A, which could affect the development of smoking-related ccRCC by regulating cell cycle and p53 signaling pathway.

Co-expression network analysis identified CDH11 in association with progression and prognosis in gastric cancer

Background and aims

Gastric cancer (GC) is one of the most common cancers worldwide, and its pathogenesis is related to a complex network of gene interactions. The aims of our study were to find hub genes associated with the progression and prognosis of GC and illustrate the underlying mechanisms.

Methods

Weighted gene co-expression network analysis (WGCNA) was conducted using the microarray dataset and clinical data of GC patients from Gene Expression Omnibus (GEO) database to identify significant gene modules and hub genes associated with TNM stage in GC. Functional enrichment analysis and protein-protein interaction network analysis were performed using the significant module genes. We regarded the common hub genes in the co-expression network and protein-protein interaction (PPI) network as "real" hub genes for further analysis. Hub gene was validated in another independent dataset and The Cancer Genome Atlas (TCGA) dataset.

Results

In the significant purple module (R ²=0.35), a total of 12 network hub genes were identified, among which six were also hub nodes in the PPI network of the module genes. Functional annotation revealed that the genes in the purple module focused on the biological processes of system development, biological adhesion, extracellular structure organization and metabolic process. In terms of validation, CDH11 had a higher correlation with the TNM stage than other hub genes and was strongly correlated with biological adhesion based on GO functional enrichment analysis. Data obtained from the Gene Expression Profiling Interactive Analysis (GEPIA) showed that CDH11 expression had a strong positive correlation with GC stages (P<0.0001). In the testing set and Oncomine dataset, CDH11 was highly expressed in GC tissues (P<0.0001). Survival analysis indicated that samples with a high CDH11 expression showed a poor prognosis. Cox regression analysis demonstrated an independent predictor of CDH11 expression in GC prognosis (HR=1.482, 95% CI: 1.015-2.164). Furthermore, gene set enrichment analysis (GSEA) demonstrated that multiple tumor-related pathways, especially focal adhesion, were enriched in CDH11 highly expressed samples.

Conclusion

CDH11 was identified and validated in association with progression and prognosis in GC, probably by regulating biological adhesion and focal adhesion-related pathways.

Identification of key genes and pathways in human clear cell renal cell carcinoma (ccRCC) by co-expression analysis

Human clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidney, and its prognostic is influenced by the progression covering a complex network of gene interactions. In our study, we screened differential expressed genes, and constructed protein-protein interaction (PPI) network and a weighted gene co-expression network to identify key genes and pathways associated with the progression of ccRCC (n = 56). Functional and pathway enrichment analysis demonstrated that upregulated differentially expressed genes (DEGs) were significantly enriched in response to wounding, positive regulation of immune system process, leukocyte activation, immune response and cell activation. Downregulated DEGs were significantly enriched in oxidation reduction, monovalent inorganic cation transport, ion transport, excretion and anion transport. In the PPI network, top 10 hub genes were identified (TOP2A, MYC, ALB, CDK1, VEGFA, MMP9, PTPRC, CASR, EGFR and PTGS2). In co-expression network, 6 ccRCC-related modules were identified. They were associated with immune response, metabolic process, cell cycle regulation, angiogenesis and ion transport. In conclusion, our study illustrated the hub genes and pathways involved in the progress of ccRCC, and further molecular biological experiments are needed to confirm the function of the candidate biomarkers in human ccRCC.

Ten hub genes associated with progression and prognosis of pancreatic carcinoma identified by co-expression analysis

Since the five-year survival rate is less than 5%, pancreatic ductal adenocarcinoma (PDAC) remains the 4th cause of cancer-related death. Although PDAC has been repeatedly researched in recent years, it is still predicted to be the second leading cause of cancer death by year 2030. In our study, the differentially expressed genes in dataset GSE62452 were used to construct a co-expression network by WGCNA. The yellow module related to grade of PDAC was screened. Combined with co-expression network and PPI network, 36 candidates were screened. After survival and regression analysis by using GSE62452 and TCGA dataset, we identified 10 real hub genes (CCNA2, CCNB1, CENPF, DLGAP5, KIF14, KIF23, NEK2, RACGAP1, TPX2 and UBE2C) tightly related to progression of PDAC. According to Oncomine database and The Human Protein Atlas (HPA), we found that all real hub genes were overexpressed in pancreatic carcinoma compared with normal tissues on transcriptional and translational level. ROC curve was plotted and AUC was calculated to distinguish recurrent and non-recurrent PDAC and every AUC of the real hub gene was greater than 0.5. Finally, functional enrichment analysis and gene set enrichment (GSEA) was performed and both of them showed the cell cycle played a vital role in PDAC.