Genome-scale analysis to identify prognostic markers in patients with early-stage pancreatic ductal adenocarcinoma after pancreaticoduodenectomy

Identification of candidate biomarkers for GBM based on WGCNA

Glioblastoma multiforme (GBM), the most aggressive form of primary brain tumor, poses a considerable challenge in neuro-oncology. Despite advancements in therapeutic approaches, the prognosis for GBM patients remains bleak, primarily attributed to its inherent resistance to conventional treatments and a high recurrence rate. The primary goal of this study was to acquire molecular insights into GBM by constructing a gene co-expression network, aiming to identify and predict key genes and signaling pathways associated with this challenging condition. To investigate differentially expressed genes between various grades of Glioblastoma (GBM), we employed Weighted Gene Co-expression Network Analysis (WGCNA) methodology. Through this approach, we were able to identify modules with specific expression patterns in GBM. Next, genes from these modules were performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using ClusterProfiler package. Our findings revealed a negative correlation between biological processes associated with neuronal development and functioning and GBM. Conversely, the processes related to the cell cycle, glomerular development, and ECM-receptor interaction exhibited a positive correlation with GBM. Subsequently, hub genes, including SYP, TYROBP, and ANXA5, were identified. This study offers a comprehensive overview of the existing research landscape on GBM, underscoring the challenges encountered by clinicians and researchers in devising effective therapeutic strategies.

Single-cell RNA sequencing highlights epithelial and microenvironmental heterogeneity in malignant progression of pancreatic ductal adenocarcinoma

Intraductal papillary mucinous neoplasms (IPMNs) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). Single-cell transcriptomics provides a unique perspective for dissecting the epithelial and microenvironmental heterogeneity that accompanies progression from benign IPMNs to invasive PDAC. Single-cell RNA sequencing was performed through droplet-based sequencing on 35 693 cells from three high-grade IPMNs and two IPMN-derived PDACs (all surgically resected). Analysis of single-cell transcriptomes revealed heterogeneous alterations within the epithelium and the tumor microenvironment during the progression of noninvasive dysplasia to invasive cancer. For epithelial cells, we identified acinar-ductal cells and isthmus-pit cells enriched in IPMN lesions and profiled three types of PDAC-unique ductal cells. Notably, a proinflammatory immune component was distinctly observed in IPMNs, comprising CD4+ T cells, CD8+ T cells, and B cells, whereas M2 macrophages were significantly accumulated in PDAC. Through the analysis of cellular communication, the osteopontin gene (SPP1)-CD44 pathway between macrophages and epithelial cells were particularly strengthened in the PDAC group. Further prognostic analysis revealed that SPP1 is a biomarker of IPMN carcinogenesis for surveillance. This study demonstrates the ability to perform high-resolution profiling of single cellular transcriptomes during the progression of high-grade IPMNs to cancer. Notably, single-cell analysis provides an unparalleled insight into both epithelial and microenvironmental heterogeneity associated with early cancer pathogenesis and provides practical markers for surveillance and targets for cancer interception.

SLA2 is a prognostic marker in HNSCC and correlates with immune cell infiltration in the tumor microenvironment

PURPOSE

To investigate Src-like adaptor 2 gene (SLA2) expression in head and neck squamous cell carcinoma (HNSCC), its potential prognostic value, and its effect on immune cell infiltration.

METHODS

Through a variety of bioinformatics analyses, we extracted and analyzed data sets from the Cancer Genome Atlas (TCGA), Tumor Immune Estimation Resource (TIMER), and Gene Expression Profile Interaction Analysis (GEPIA) to analyze the correlation between SLA2 and the prognosis, immune checkpoint, tumor microenvironment (TME) and immune cell infiltration of HNSCC, and to explore its potential oncogenic mechanism. To further explore the potential role of SLA2 in HNSCC by Gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.

RESULTS

SLA2 messenger ribonucleic acid (mRNA) levels were increased in HNSCC tumor tissues compared with normal tissues. In addition, we found that SLA2 may be an independent prognostic factor for HNSCC, and high SLA2 expression is associated with favorable prognosis in HNSCC. SLA2 expression was positively correlated with B cells, cluster of differentiation 8-positive T cells (CD8 + T cells), cluster of differentiation 4-positive T cells (CD4 + T cells), macrophages, neutrophil and dendritic cells infiltration. SLA2 has also been shown to co-express immune-related genes and immune checkpoints. Significant GO term analysis by Gene Set Enrichment Analysis (GSEA) indicated that genes correlated with SLA2 were located mainly in the side of membrane, receptor complex, secretory granule membrane, endocytic vesicle, membrane region, and endosome membrane, where they were involved in leukocyte cell-cell adhesion, response to interferon-gamma, and regulation of immune effector process. These related genes also served as antigen binding, cytokine receptor activity, phosphatidylinositol 3-kinase activity, peptide receptor activity, Src homology domain 3 (SH3) domain binding, and cytokine receptor binding. KEGG pathway analysis demonstrated that these genes related to SLA2 were mainly enriched in signal pathways, such as hematopoietic cell lineage, cell adhesion molecules (CAMs), natural killer cell mediated cytotoxicity, measles, and chemokine signaling pathway.

CONCLUSIONS

SLA2 is increased in HNSCC, and high SLA2 expression is associated with favorable prognosis. SLA2 may affect tumor development by regulating tumor infiltrating cells in TME. SLA2 may be a potential target for immunotherapy.

The cellular trajectories of tumor-associated macrophages decipher the heterogeneity of pancreatic cancer

Emerging evidence indicates that the interactions and dynamic changes among tumor-associated macrophages (TAMs) are pivotal in molding the tumor microenvironment (TME), thereby influencing diverse clinical outcomes. However, the potential clinical ramifications of these evolutionary shifts in tumor-associated macrophages within pancreatic adenocarcinoma (PAAD) remain largely unexamined. Single-cell RNA sequencing (scRNA-seq) data were retrieved from the Tumor Immune Single-cell Hub. The Seurat and Monocle algorithms were employed to elucidate the progression of TAMs, using non-negative matrix factorization (NMF) to determine molecular classifications. Subsequently, the prognosis, biological characteristics, genomic modifications, and immune landscape across various clusters were interpreted. Furthermore, the sensitivity of potential therapeutic drugs between subtypes was predicted. Cellular experiments were conducted to explore the function of the NR1H3 gene in pancreatic cancer. These experiments encompassed gene knockdown, proliferation assessment, clone formation evaluation, transwell examination, and apoptosis analysis. Trajectory gene expression analysis of tumor-associated macrophages identified three disparate clusters, each associated with different clinical outcomes Compared to clusters C1 and C2, cluster C3 is seemingly at a less advanced pathological stage and associates with a relatively favorable prognosis. Further investigation revealed pronounced genetic instability in cluster C2, whereas cluster C3 demonstrated notable genetic stability. Cluster C1, characterized as "immune-hot," exhibits an abundance of immune cells and elevated immune checkpoint expression, suggesting its suitability for immunotherapy. Furthermore, several potential therapeutic agents have been pinpointed, potentially facilitating the clinical application of these insights. Cell assays indicated that NR1H3 knockdown markedly induced apoptosis and suppressed clonogenesis, migration, and proliferation of pancreatic cancer cells in the PTAU-8988 and PANC-1 cell lines. Overall, our study discerned three clusters with unique characteristics, defined by the evolution of TAMs. We propose customized therapeutic strategies for patients within these specific clusters to improve clinical outcomes and optimize clinical management.

Construction of a 3-mRNA hypoxia prognostic model to evaluate immune microenvironment in hepatocellular carcinoma

BACKGROUND

Hypoxia is a key factor in the development of hepatocellular carcinoma (HCC), which is the most common primary liver cancer with poor prognosis. The current study aimed to identify the potential prognostic biomarkers of the hypoxia-associated gene signature in patients with HCC, and to further explore the relationship between hypoxia and immune infiltration.

METHODS

After the determination of differentially expressed genes (DEGs) using the HCC transcriptome data of The Cancer Genome Atlas database and hypoxia-related gene set, the prognosis-associated genes were identified using univariate Cox regression analysis. Then, the hypoxia prognosis model was established via multivariate Cox regression analysis, with functional annotation conducted using Gene Set Enrichment Analysis. CIBERSORT was utilized to analyze the degree of tumor immune invasion, and an International Cancer Genome Consortium cohort to verify the reliability of the prognosis model. Expression levels of hypoxia-associated genes were detected by real-time quantitative polymerase chain reaction in HCC samples.

RESULTS

3 genes (ENO1, SAP30, and STC2) constructed the hypoxia prognosis model. The patients were subdivided into 2 groups based on median risk score, with a high hypoxic score indicating poor prognosis of HCC. The hypoxia signature could be employed as an independent prognostic factor in HCC. In addition, the proportion of macrophages was higher in the high-risk group.

CONCLUSION

The hypoxia-associated signature could be a potential prognostic marker of HCC and provides a different perspective for immunotherapy of HCC.

Prognostic Signature Development on the Basis of Macrophage Phagocytosis-Mediated Oxidative Phosphorylation in Bladder Cancer

Background

Macrophages are correlated with the occurrence and progression of bladder cancer (BCa). However, few research has focused on the predictive relevance of macrophage phagocytosis-mediated oxidative phosphorylation (MPOP) with BCa overall survival. Herein, we aimed to propose the targeted macrophage control based on MPOP as a treatment method for BCa immunotherapy.

Methods

The mRNA expression data sets and clinical data of bladder cancer originated from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data set. A systematic study of several GEO data sets found differentially expressed macrophage phagocytosis regulators (DE-MPR) between BCa and normal tissues. To discover overall survival-associated DE-MPR and develop prognostic gene signature with performance validated based on receiver operating curves and Kaplan-Meier curves, researchers used univariate and Lasso Cox regression analysis (ROC). External validation was done with GSE13057 and GSE69795. To clarify its molecular mechanism and immune relevance, GO/KEGG enrichment analysis and tumor immune analysis were used. To find independent bladder cancer prognostic variables, researchers employed multivariate Cox regression analysis. Finally, using TCGA data set, a predictive nomogram was built.

Results

In BCa, a four-gene signature of oxidative phosphorylation composed of PTPN6, IKZF3, HDLBP, and EMC1 was found to predict overall survival. With the MPOP feature, the ROC curve showed that TCGA data set and the external validation data set performed better in predicting overall survival than the traditional AJCC stage. The four-gene signature can identify cancers from normal tissue and separate patients into the high-risk and low-risk groups with different overall survival rates. The four MPOP-gene signature was an independent predictive factor for BCa. In predicting overall survival, a nomogram integrating genetic and clinical prognostic variables outperformed AJCC staging. Multiple oncological features and invasion-associated pathways were identified in the high-risk group, which were also correlated with significantly lower levels of immune cell infiltration.

Conclusion

This paper found the MPOP-feature gene and developed a predictive nomogram capable of accurately predicting bladder cancer overall survival. The above discoveries can contribute to the development of personalized treatments and medical decisions.

Bioinformatics Analysis for Constructing a Six-Immune-Related Long Noncoding RNA Signature as a Prognostic Model of Hepatocellular Carcinoma

The present study was aimed at identifying the potential prognostic biomarkers of the immune-related long noncoding RNA (IRL) signature for patients with hepatocellular carcinoma (HCC). RNA-sequencing data and clinical information about HCC were obtained from The Cancer Genome Atlas. The IRLs were determined with regard to the coexpression of immune-related genes and differentially expressed lncRNAs. The survival IRLs were obtained using the univariate Cox analysis. Subsequently, the prognosis model was constructed via the multivariate Cox analysis. Subsequently, functional annotation was conducted using Gene Set Enrichment Analysis (GSEA) and principal component analysis (PCA). In total, 341 IRLs were identified, and 6 IRLs were found to have a highly significant association with the prognosis of patients with HCC. The immune prognosis model was constructed with these 6 IRLs (AC099850.4, negative regulator of antiviral response, AL031985.3, PRRT3-antisense RNA1, AL365203.2, and long intergenic nonprotein coding RNA 1224) using the multivariate Cox regression analysis. In addition, immune-related prognosis signatures were confirmed as an independent prognostic factor. The association between prognostic signatures and immune infiltration indicated that the 6 lncRNAs could reflect the immune status of the tumor. Collectively, the present study demonstrates that six-lncRNA signatures may be potential biomarkers to predict the prognosis of patients with HCC.

Type 1 T Helper Cell-Based Molecular Subtypes and Signature Are Associated with Clinical Outcome in Pancreatic Ductal Adenocarcinoma

Lymph node metastasis in pancreatic ductal adenocarcinoma (PDAC) is shown to be related with poor prognosis. To construct an immune-related gene prognostic risk model for PDAC and clarify the molecular and immune characteristics and the benefit of immune checkpoint inhibitor (ICI) therapy in prognostic risk model-defined subgroups of PDAC, we analyze the association between the density of immune cell infiltration and lymph node metastatic status and further study the potential role of immune cells, immune cell–related genes, and immunotherapy outcomes in PDAC patients using bioinformatics models and machine learning methods. Based on The Cancer Genome Atlas (TCGA), PACA-AU and PACA-CA data sets, 62 immune-related hub genes were identified by weighted gene co-expression network analysis (WGCNA). Four genes were selected to construct a molecular subtype identification based on the type 1 T helper cell–related hub genes by using the Cox regression method. We found that lower type 1 T helper cell abundance was correlated with prolonged survival in PDAC patients. Further, prognostic risk score model constructed with the type 1 T helper cell-related signature showed high accuracy in predicting overall survival and response to immunotherapy. This study might improve the understanding of the role of type 1 T helper cells in PDAC patients and aid in the development of immunotherapy and personalized treatments for these patients.

Clinical significance of mitogen-activated protein kinase kinase kinases in hepatitis B virus -related hepatocellular carcinoma and underlying mechanism exploration

The purpose of this research was to explore the diagnostic/prognostic significance and prospective molecular mechanisms of mitogen-activated protein kinase kinase kinases (MAP3Ks) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). Diagnostic/prognostic significance of MAP3Ks was screened in the GSE1450 data set and validated in the Guangxi cohort. Various bioinformatics tools were used to explore the biological functions of prognosis-related genes. Subsequently, molecular biology assays were used to verify the biological functions and molecular mechanisms of specific gene. MAP3K9 was observed to be differentially expressed in HCC and adjacent tissues with satisfactory diagnostic value. It was discovered in survival analysis that MAP3K13 and MAP3K15 were associated with overall survival (OS) of patients with HBV-related HCC in the GSE1450 data set and the Guangxi cohort. Nomograms were established based on prognosis-related genes and clinical factors for individualized risk assessment. The assays on HCC cells demonstrated that MAP3K13 regulated the death and proliferation of HCC cells by activating the JNK pathway and inducing the expression of apoptosis-related factors. In conclusion, our results suggested that MAP3K9 might serve as a diagnostic biomarker in HBV-related HCC and MAP3K13 and MAP3K15 might serve as useful prognostic biomarkers. Besides, cytological assays prompted that MAP3K13 might impact the prognosis of HCC by regulating the JNK pathway and inducing apoptosis.

Analysis of Genetic Variants and the ceRNA Network of miR-9 in Non-Small Cell Lung Cancer

Objective: To explore the role of single-nucleotide polymorphisms (SNPs) in hsa-miR-9 in non-small cell lung cancer (NSCLC). Methods: Log-rank and Cox regression analyses were conducted to assess the association of four functional SNPs of miR-9 with overall survival (OS) of Chinese patients with NSCLC. A reporter luciferase assay was performed to examine the relationship between the SNPs and transcriptional activity of miR-9. The expression of miR-9 in cells was detected by quantitative real-time PCR assay. Xenograft model was established in nude mice, which were treated with Lv-MiR-9-mimics or Lv-miR-9-inhibitor. A long noncoding RNA (lncRNA)-miR-9-messenger RNA (mRNA) competing endogenous RNA (ceRNA) network was established based on bioinformatics analyses. Results: We found that rs1501672 was associated with the prognosis of 1001 Chinese NSCLC patients (A>G, additive model: adjusted hazard ratio = 0.89, 95% confidence interval = 0.79-1.00, p = 0.056). Luciferase reporter assay showed higher luciferase activity with wild A allele than that with mutant G allele in 293T, SPC-A1, and A549 cell lines. The miR-9 level was significantly higher in lung cancer cells than normal lung cells. miR-9 was also over expressed in lung cancer tissue according to The Cancer Genome Atlas and gene expression omnibus databases. Xenograft models based on H1299 cells showed that lv-miR-9-inhibitor significantly decreased tumor growth compared with the lv-miR-9-NC group (p < 0.001). Bioinformatics analysis showed that one target gene leukemia inhibitory factor receptor and two lncRNAs (KIAA0087 and GVINP1) were associated with OS of NSCLC patients. Conclusion: The rs1501672 of miR-9 was associated with the prognosis of NSCLC patients in the Chinese population. The lncRNA-miR-9-mRNA ceRNA network revealed potential molecular biological regulation pathways and prognostic biomarkers for NSCLC.

Identification of solute carrier family genes related to the prognosis and tumor-infiltrating immune cells of pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) has persisted as one of the worst prognostic tumors with a 5-year survival rate of lower than 6%. Although many studies have investigated PDAC, new biomarkers are required to ensure early diagnosis and predict the prognosis of PDAC.

Methods

In this study, we used bioinformatics methods to evaluate differences in the expression of solute carrier (SLC) family genes in tumors and non-tumors. A Kaplan-Meier analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox proportional hazards regression analysis were used to evaluate the relationship between SLC genes and prognosis using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. The prognostic signature was constructed depending on the risk score to assess the impact of multiple genes on the prognosis, receiver operating characteristic (ROC) curves and forest plot was constructed to assess the ability to predict the prognosis and effects of clinical variables in both high- and low-risk groups. Tumor-infiltrating immune cells were evaluated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) in both high- and low-risk groups.

Results

In 32 SLC genes, 9 were significantly associated with the OS after LASSO analysis. SLC19A3 (P=0.007), SLC25A39 (P=0.027), SLC39A11 (P=0.043) were significantly associated with prognosis and included into the prognostic model. CIBERSORT demonstrated that memory B cells (P=0.004), naive B cells (P=0.007), CD8 T cells (P=0.003), activated memory CD4 T cells (P=0.004), and activated NK cells (P=0.019) were significantly higher in the low-risk group. Gene set enrichment analysis (GSEA) showed that potential molecular mechanisms enriched in MYC and p53 signaling pathways.

Conclusions

SLC19A3, SLC25A35, and SLC39A11 were significantly relative to the prognosis of PDAC and changed the tumor microenvironment, as well as the MYC and p53 signaling pathways. The SLC19A3 gene may represent a new tumor suppressor in PDAC.

Identification of prognostic metabolic genes in adrenocortical carcinoma and establishment of a prognostic nomogram: A bioinformatic study

ABSTRACT

Adrenocortical carcinoma is an invasive malignancy with poor prognosis, high recurrence rate and limited therapeutic options. Therefore, it is necessary to establish an effective method to diagnose and evaluate the prognosis of patients, so as to realize individualized treatment and improve their survival rate.This study investigated metabolic genes that may be potential therapeutic targets for Adrenocortical carcinoma (ACC). Level 3 gene expression data from the ACC cohort and the relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. To verify, other ACC datasets (GSE76021, GSE19750) were downloaded from the Gene Expression Omnibus (GEO) database. The ACC datasets from TCGA and GEO were used to screen metabolic genes through the Molecular Signatures Database using gene set enrichment analysis. Then, the overlapping metabolic genes of the 2 datasets were identified.A signature of five metabolic genes (CYP11B1, GSTM2, IRF9, RPL31, and UBE2C) was identified in patients with ACC. The signature could be used to divide the patients with ACC into high- and low-risk groups based on their median risk score. Multivariate Cox regression analysis was performed to determine the independent prognostic factors of ACC. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to assess the prediction accuracy of the prognostic signature. Last, a nomogram was established to assess the individualized prognosis prediction model.The results indicated that the signature of 5 metabolic genes had excellent predictive value for ACC. These findings might help improve personalized treatment and medical decisions.

Signature of prognostic epithelial-mesenchymal transition related long noncoding RNAs (ERLs) in hepatocellular carcinoma

Reliable biomarkers are of great significance for the treatment and diagnosis of hepatocellular carcinoma (HCC). This study identified potential prognostic epithelial-mesenchymal transition related lncRNAs (ERLs) by the cancer genome atlas (TCGA) database and bioinformatics.The differential expression of long noncoding RNA (lncRNA) was obtained by analyzing the lncRNA data of 370 HCC samples in TCGA. Then, Pearson correlation analysis was carried out with EMT related genes (ERGs) from molecular signatures database. Combined with the univariate Cox expression analysis of the total survival rate of hepatocellular carcinoma (HCC) patients, the prognostic ERLs were obtained. Then use "step" function to select the optimal combination of constructing multivariate Cox expression model. The expression levels of ERLs in HCC samples were verified by real-time quantitative polymerase chain reaction.Finally, we identified 5 prognostic ERLs (AC023157.3, AC099850.3, AL031985.3, AL365203.2, CYTOR). The model showed that these prognostic markers were reliable independent predictors of risk factors (P value <.0001, hazard ratio [HR] = 2.400, 95% confidence interval [CI] = 1.667-3.454 for OS). In the time-dependent receiver operating characteristic analysis, this prognostic marker is a good predictor of HCC survival (area under the curve of 1 year, 2 years, 3 years, and 5 years are 0.754, 0.720, 0.704, and 0.662 respectively). We analyzed the correlation of clinical characteristics of these prognostic markers, and the results show that this prognostic marker is an independent factor that can predict the prognosis of HCC more accurately. In addition, by matching with the Molecular Signatures Database, we obtained 18 ERLs, and then constructed the HCC prognosis model and clinical feature correlation analysis using 5 prognostic ERLs. The results show that these prognostic markers have reliable independent predictive value. Bioinformatics analysis showed that these prognostic markers were involved in the regulation of EMT and related functions of tumor occurrence and migration.Five prognostic types of ERLs identified in this study can be used as potential biomarkers to predict the prognosis of HCC.

Integrative Analysis of a Novel Eleven-Small Nucleolar RNA Prognostic Signature in Patients With Lower Grade Glioma

Objective

The present study used the RNA sequencing (RNA-seq) dataset to identify prognostic snoRNAs and construct a prognostic signature of The Cancer Genome Atla (TCGA) lower grade glioma (LGG) cohort, and comprehensive analysis of this signature.

Methods

RNA-seq dataset of 488 patients from TCGA LGG cohort were included in this study. Comprehensive analysis including function enrichment, gene set enrichment analysis (GSEA), immune infiltration, cancer immune microenvironment, and connectivity map (CMap) were used to evaluate the snoRNAs prognostic signature.

Results

We identified 21 LGG prognostic snoRNAs and constructed a novel eleven-snoRNA prognostic signature for LGG patients. Survival analysis suggests that this signature is an independent prognostic risk factor for LGG, and the prognosis of LGG patients with a high-risk phenotype is poor (adjusted P = 0.003, adjusted hazard ratio = 2.076, 95% confidence interval = 1.290–3.340). GSEA and functional enrichment analysis suggest that this signature may be involved in the following biological processes and signaling pathways: such as cell cycle, Wnt, mitogen-activated protein kinase, janus kinase/signal transducer and activator of tran-ions, T cell receptor, nuclear factor-kappa B signaling pathway. CMap analysis screened out ten targeted therapy drugs for this signature: 15-delta prostaglandin J2, MG-262, vorinostat, 5155877, puromycin, anisomycin, withaferin A, ciclopirox, chloropyrazine and megestrol. We also found that high- and low-risk score phenotypes of LGG patients have significant differences in immune infiltration and cancer immune microenvironment.

Conclusions

The present study identified a novel eleven-snoRNA prognostic signature of LGG and performed a integrative analysis of its molecular mechanisms and relationship with tumor immunity.

Seven-gene signature on tumor microenvironment for predicting the prognosis of patients with pancreatic cancer

Background

The aim of the present study was to construct a novel gene signature on the tumor microenvironment (TME) to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC).

Methods

We downloaded gene expression profiles and clinical information of PDAC from The Cancer Genome Atlas (TCGA) datasets, as well as Gene Expression Omnibus (GEO) datasets (GSE78229, GSE62452, and GSE28735). Differentially expressed genes were generated by comparing high versus low score groups of immune/stromal subgroups based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Subsequently, a prognostic risk score model was constructed and validated through univariate and multivariate Cox regression analyses. Finally, functional enrichment analysis and protein-protein interactions were performed to predict the functional implication of the prognostic model.

Results

We picked out 1,797 upregulated genes in immune groups and stromal groups. Through further analysis, we constructed a 7-gene signature on the TME. The risk score from the model effectively differentiated patients into high-risk and low-risk groups with different overall survival and was validated by GEO datasets. A functional analysis suggested that 7 selected genes and their co-expressed genes were mainly enriched in immune response, extracellular structure organization, and cell adhesion molecule binding.

Conclusions

Our results showed that the 7-gene model on the TME can be used to assess the prognosis of patients with PDAC.

Single-cell differential splicing analysis reveals high heterogeneity of liver tumor-infiltrating T cells

Recent advances in single-cell RNA sequencing (scRNA-seq) have improved our understanding of the association between tumor-infiltrating lymphocyte (TILs) heterogeneity and cancer initiation and progression. However, studies investigating alternative splicing (AS) as an important regulatory factor of heterogeneity remain limited. Here, we developed a new computational tool, DESJ-detection, which accurately detects differentially expressed splicing junctions (DESJs) between cell groups at the single-cell level. We analyzed 5063 T cells of hepatocellular carcinoma (HCC) and identified 1176 DESJs across 11 T cell subtypes. Interestingly, DESJs were enriched in UTRs, and have putative effects on heterogeneity. Cell subtypes with a similar function closely clustered together at the AS level. Meanwhile, we identified a novel cell state, pre-activation with the isoform markers ARHGAP15-205. In summary, we present a comprehensive investigation of alternative splicing differences, which provided novel insights into T cell heterogeneity and can be applied to other full-length scRNA-seq datasets.

UXT antisense RNA 1 sever as a novel prognostic long non-coding RNA in early stage pancreatic ductal adenocarcinoma patients after receiving pancreaticoduodenectomy

Objective: The principal objective of this project was to investigate the prognostic value of UXT antisense RNA 1 (UXT-AS1) in pancreatic ductal adenocarcinoma (PDAC), as well as its biological function mechanisms and the screening of targeted drugs using The Cancer Genome Atlas (TCGA) PDAC genome-wide RNA sequencing (RNA-seq) dataset.

Methods: We used TCGA 112 early stage PDAC patients to screen the prognostic value of UXT-AS1. Biological functions and mechanisms of UXT-AS1 were investigated by co-expression analysis, differentially expressed genes (DEGs) and gene set enrichment analysis, while targeted drug screening was investigated by connectivity Map (CMap).

Results: By analyzing the dataset from TCGA cohort, we found that UXT-AS1 was significantly up-regulated in pancreatic cancer tissues. Multivariate survival analysis demonstrated that PDAC patients with high UXT-AS1 expression had an unfavourable prognosis (adjusted P=0.033, HR=1.830, 95%CI=1.051-3.188). Genome-wide co-expression analysis and gene set enrichment analysis suggested that UXT-AS1 may act as a pivotal part in PDAC by participating in nuclear factor kappa beta, regulation of tumor necrosis factor, cell adhesion, T cell receptor signaling pathway, and numerous immune-related biological processes and signaling pathways. Functional enrichment analysis of DEGs between high- and low-UXT-AS1 expression groups suggested that these DEGs were significant enriched in B cell receptor complex, response to drug chemical carcinogenesis and drug metabolism-cytochrome P450. CMap analysis revealed that quipazine and terazosin may be targeted drugs for UXT-AS1 in PDAC.

Conclusion: Our current study has identified UXT-AS1 as a novel biomarker for the prognosis of early stage PDAC. We also clarified its biological functional mechanisms and identified two targeted drugs of UXT-AS1 in PDAC.

Comprehensive investigation of the clinical significance of long non-coding RNA HOXA-AS2 in acute myeloid leukemia using genome-wide RNA sequencing dataset

Objective: The present study aimed to determine the prognostic value of HOXA cluster antisense RNA2 (HOXA-AS2) in acute myeloid leukemia (AML), and to explore its potential molecular mechanisms. We also screening of potential drugs targeting HOXA-AS2 in AML.

Methods: The level 3 raw genome-wide RNA sequencing dataset of AML was download from The Cancer Genome Atlas (TCGA) Data Portal, and the potential molecular mechanisms and drugs prediction of HOXA-AS2 in AML were explored using multiple bioinformatics analysis approaches.

Results: TCGA AML cohort dataset indicated that HOXA-AS2 was significantly up-regulated in AML bone marrow tissues, and high HOXA-AS2 expression was related to poor overall survival (log-rank P=0.0284, hazard ratio 1.640, 95% confidence interval 1.046-2.573). Functional enrichment of differentially expressed genes (DEGs) suggested that the difference in prognosis between AML patients with high- and low-HOXA-AS2 expression may be due to differences in biological processes and pathways, including cell adhesion, angiogenesis, mitogen-activated protein kinase, cell differentiation, and other biological processes, and phosphatidylinositol 3 kinase-protein kinase B and Wnt signaling pathways. We also screened out three potential HOXA-AS2-targeted therapeutic drugs for AML, megestrol, carmustine, and cefoxitin, based on these DEGs. Functional enrichment analysis of HOXA-AS2-co-expressed genes revealed that HOXA-AS2 may act a part in AML by regulating nuclear factor-κB transcription factor activity, DNA methylation, angiogenesis, apoptosis, cell migration, Toll-like receptor 4, and Wnt signaling pathways.

Conclusion: Our findings suggest that HOXA-AS2 is up-regulated in the bone marrow in patients with AML, and may serve as a novel prognostic biomarker for AML.

Prognostic Value and Molecular Mechanisms of Proteasome 26S Subunit, Non-ATPase Family Genes for Pancreatic Ductal Adenocarcinoma Patients after Pancreaticoduodenectomy

Objective: Pancreatic cancer (PC) is an extremely malignant tumor with similar morbidity and mortality and lack of an effective treatment. This study explored the prognostic value and molecular mechanisms of proteasome 26S subunit, non-ATPase (PSMD) family genes in pancreatic ductal adenocarcinoma (PDAC).Methods: Survival analyses were performed to elucidate the relationship between prognosis and the level of PSMD expression. ROC curves and nomograms were constructed to predict the prognosis. A bioinformatics analysis was used to explore the co-expression and complex interaction networks of PSMDs. The potential mechanisms were further explored via gene set enrichment analysis (GSEA).Results: We find high levels of PSMD6, PSMD9, PSMD11, and PSMD14 expression were significantly associated with a poorer OS. High PSMD6 and PSMD11 expression was associated with a poorer relapse-free survival (RFS). A risk score model was constructed based on prognosis-related genes. The area under ROC curves (AUC) was 53.3%, 59.3%, and 62.9% for 1-, 2-, 3 years, respectively.Conclusion: GSEA revealed that PSMD6 and PSMD11 play a role in PDAC through various biological processes and signaling pathways, including TP53, CDKN2A, MYC pathway, DNA repair, KRAS, cell cycle checkpoint, NIK, NF-κB signaling pathway, and proteasomes. This study demonstrated that PSMD6 and PSMD11 could serve as a potential prognostic and diagnostic biomarkers for patients with early-stage PDAC after pancreaticoduodenectomy.

Prognostic value of Glypican family genes in early-stage pancreatic ductal adenocarcinoma after pancreaticoduodenectomy and possible mechanisms

Background

This study explored the prognostic significance of Glypican (GPC) family genes in patients with pancreatic ductal adenocarcinoma (PDAC) after pancreaticoduodenectomy using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO).

Methods

A total of 112 PDAC patients from TCGA and 48 patients from GEO were included in the analysis. The relationship between overall survival and the expression of GPC family genes as well as basic clinical characteristics was analyzed using the Kaplan-Meier method with the log-rank test. Joint effects survival analysis was performed to further examine the relationship between GPC genes and prognosis. A prognosis nomogram was established based on clinical characteristics and prognosis-related genes. Prognosis-related genes were investigated by genome-wide co-expression analysis and gene set enrichment analysis (GSEA) was carried out to identify potential mechanisms of these genes affecting prognosis.

Results

In TCGA database, high expression of GPC2, GPC3, and GPC5 was significantly associated with favorable survival (log-rank P = 0.031, 0.021, and 0.028, respectively; adjusted P value = 0.005, 0.022, and 0.020, respectively), and joint effects analysis of these genes was effective for prognosis prediction. The prognosis nomogram was applied to predict the survival probability using the total scores calculated. Genome-wide co-expression and GSEA analysis suggested that the GPC2 may affect prognosis through sequence-specific DNA binding, protein transport, cell differentiation and oncogenic signatures (KRAS, RAF, STK33, and VEGFA). GPC3 may be related to cell adhesion, angiogenesis, inflammatory response, signaling pathways like Ras, Rap1, PI3K-Akt, chemokine, GPCR, and signatures like cyclin D1, p53, PTEN. GPC5 may be involved in transcription factor complex, TFRC1, oncogenic signatures (HOXA9 and BMI1), gene methylation, phospholipid metabolic process, glycerophospholipid metabolism, cell cycle, and EGFR pathway.

Conclusion

GPC2, GPC3, and GPC5 expression may serve as prognostic indicators in PDAC, and combination of these genes showed a higher efficiency for prognosis prediction.

Functional gene networks reveal distinct mechanisms segregating in migraine families

Migraine is the most common neurological disorder worldwide and it has been shown to have complex polygenic origins with a heritability of estimated 40-70%. Both common and rare genetic variants are believed to underlie the pathophysiology of the prevalent types of migraine, migraine with typical aura and migraine without aura. However, only common variants have been identified so far. Here we identify for the first time a gene module with rare mutations through a systems genetics approach integrating RNA sequencing data from brain and vascular tissues likely to be involved in migraine pathology in combination with whole genome sequencing of 117 migraine families. We found a gene module in the visual cortex, based on single nuclei RNA sequencing data, that had increased rare mutations in the migraine families and replicated this in a second independent cohort of 1930 patients. This module was mainly expressed by interneurons, pyramidal CA1, and pyramidal SS cells, and pathway analysis showed association with hormonal signalling (thyrotropin-releasing hormone receptor and oxytocin receptor signalling pathways), Alzheimer's disease pathway, serotonin receptor pathway and general heterotrimeric G-protein signalling pathways. Our results demonstrate that rare functional gene variants are strongly implicated in the pathophysiology of migraine. Furthermore, we anticipate that the results can be used to explain the critical mechanisms behind migraine and potentially improving the treatment regime for migraine patients.

A Prognostic Prediction Model Developed Based on Four CpG Sites and Weighted Correlation Network Analysis Identified DNAJB1 as a Novel Biomarker for Pancreatic Cancer

Background

The prognosis of pancreatic cancer, which is among the solid tumors associated with high mortality, is poor. There is a need to improve the overall survival rate of patients with pancreatic cancer.

Materials and Methods

The Cancer Genome Atlas (TCGA) dataset with 153 samples and the International Cancer Genome Consortium (ICGC) dataset with 235 samples were used as the discovery and validation cohorts, respectively. The least absolute shrinkage and selection operator regression was used to construct the prognostic prediction model based on the DNA methylation markers. The predictive efficiency of the model was evaluated based on the calibration curve, concordance index, receiver operating characteristic curve, area under the curve, and decision curve. The xenograft model and cellular functional experiments were used to investigate the potential role of DNAJB1 in pancreatic cancer.

Results

A prognostic prediction model based on four CpG sites (cg00609645, cg13512069, cg23811464, and cg03502002) was developed using TCGA dataset. The model effectively predicted the overall survival rate of patients with pancreatic cancer, which was verified in the ICGC dataset. Next, a nomogram model based on the independent prognostic factors was constructed to predict the overall survival rate of patients with pancreatic cancer. The nomogram model had a higher predictive value than TCGA or ICGC datasets. The low-risk group with improved prognosis exhibited less mutational frequency and high immune infiltration. The brown module with 247 genes derived from the WGCNA analysis was significantly correlated with the prognostic prediction model, tumor grade, clinical stage, and T stage. The bioinformatic analysis indicated that DNAJB1 can serve as a novel biomarker for pancreatic cancer. DNAJB1 knockdown significantly inhibited the proliferation, migration, and invasion of pancreatic cancer cells in vivo and in vitro.

Conclusion

The prognostic prediction model based on four CpG sites is a new method for predicting the prognosis of patients with pancreatic cancer. The molecular characteristic analyses, including Gene Ontology, Gene Set Enrichment Analysis, mutation spectrum, and immune infiltration of the subgroups, stratified by the model provided novel insights into the initiation and development of pancreatic cancer. DNAJB1 may serve as diagnostic and prognostic biomarkers for pancreatic cancer.

Development and validation of a cancer stem cell-related signature for prognostic prediction in pancreatic ductal adenocarcinoma

Background

Cancer stem cells (CSCs) are crucial to the malignant behaviour and poor prognosis of pancreatic ductal adenocarcinoma (PDAC). In recent years, CSC biology has been widely studied, but practical prognostic signatures based on CSC-related genes have not been established or reported in PDAC.

Methods

A signature was developed and validated in seven independent PDAC datasets. The MTAB-6134 cohort was used as the training set, while one local Chinese cohort and five other public cohorts were used for external validation. CSC-related genes with credible prognostic roles were selected to form the signature, and their predictive performance was evaluated by Kaplan–Meier survival, receiver operating characteristic (ROC), and calibration curves. Correlation analysis was employed to clarify the potential biological characteristics of the gene signature.

Results

A robust signature comprising DCBLD2, GSDMD, PMAIP1, and PLOD2 was developed. It classified patients into high-risk and low-risk groups. High-risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) than low-risk patients. Calibration curves and Cox regression analysis demonstrated powerful predictive performance. ROC curves showed the better survival prediction by this model than other models. Functional analysis revealed a positive association between risk score and CSC markers. These results had cross-dataset compatibility.

Impact

This signature could help further improve the current TNM staging system and provide data for the development of novel personalized therapeutic strategies in the future.

Development and validation of a metastasis-related Gene Signature for predicting the Overall Survival in patients with Pancreatic Ductal Adenocarcinoma

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly fatal, aggressive cancer characterized by invasiveness and metastasis. In this study, we aimed to propose a gene prediction model based on metastasis-related genes (MTGs) to more accurately predict PDAC prognosis.

Methods: Differentially expressed MTGs (DE-MTGs) were identified via integrated analysis of gene expression omnibus (GEO) datasets and Human Cancer Metastasis Database (HCMDB). Overall survival (OS) related DE-MTGs were then identified and a prognostic gene signature was established using Lasso-Cox regression with TCGA-PAAD datasets. Tumor immunity was analyzed using ESTIMATE and CIBERSORT algorithms. Finally, a nomogram predicting 1-year, 2-year, and 3-year OS of PDAC patients was established based on the prognostic gene signature and relevant clinical parameters using a stepwise Cox regression model.

Results: A total of 36 DE-MTGs related to OS were identified in PDAC. Consequently, an MTG-based gene signature comprising of RACGAP1, RARRES3, TPX2, MMP28, GPR87, KIF14, and TSPAN7 was established to predict the OS of PDAC. The MTG-based gene signature was able to distinguish high-risk patients with significantly poorer prognosis and accurately predict OS of PDAC in both the training and external validation datasets. Cox regression analysis indicated that the MTG-based gene signature was an independent prognostic factor in PDAC. The gene set enrichment analysis (GSEA) showed that molecular alterations in the high-risk group were associated with multiple oncological pathways. Moreover, analysis of tumor immunity revealed significantly higher levels of follicular helper T cells and M0 macrophage infiltration, and lower levels of infiltrating naïve B cells, CD8 T cells, monocytes, and resting dendritic cells in the high-risk group. Immune cell infiltration levels were significantly associated with the expression of the seven DE-MTGs. Finally, a nomogram was established by incorporating the prognostic gene signature and clinical parameters, which was superior to the AJCC staging system in predicting the OS of PDAC patients.

Conclusions: The DE-MTGs we identified were closely associated with the progress and prognosis of PDAC and are potential therapeutic targets. The MTG-based gene signature and nomogram may serve to improve the individualized prediction of survival, assisting in clinical decision-making.

Genome-wide RNA-sequencing dataset reveals the prognostic value and potential molecular mechanisms of lncRNA in non-homologous end joining pathway 1 in early stage Pancreatic Ductal Adenocarcinoma

Objective: Our current study is to explore the prognostic value and molecular mechanisms underlying the role of lncRNA in non-homologous end joining pathway 1 (LINP1) in early stage pancreatic ductal adenocarcinoma (PDAC).

Methods: Genome-wide RNA-seq datasets of 112 early stage PDAC patients were got from The Cancer Genome Atlas and analyzed using multiple online tools.

Results: Overall survival in high LINP1 expression patients was shorter than those with low expression (high-LINP1 vs. low-LINP1=481 vs. 592 days, log-rank P=0.0432). The multivariate Cox proportional hazard regression model suggested that high-LINP1 patients had a markedly higher risk of death than low-LINP1 patients (adjusted P=0.004, hazard ratio=2.214, 95% confidence interval=1.283-3.820). Analysis of genome-wide co-expressed genes, screening of differentially expressed genes, and gene set enrichment analysis indicated that LINP1 may be involved in the regulation of cell proliferation-, cell adhesion- and cell cycle-related biological processes in PDAC. Six small-molecule compounds including STOCK1N-35874, fenofibrate, exisulind, NU-1025, vinburnine, and doxylamine were identified as potential LINP1-targeted drugs for the treatment of PDAC.

Conclusions: Our study indicated that LINP1 may serve as a prognostic biomarker of early stage PDAC. Analysis of genome-wide datasets led to the elucidation of the underlying mechanisms and identified six potential targeted drugs for the treatment of early PDAC.

Investigation of the association between C-X-C motif chemokine receptor subunits and tumor infiltration levels and prognosis in patients with early-stage pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the malignancies with the highest morality rate due to postoperative local invasion and distant metastasis. Although C-X-C motif chemokine receptor (CXCR) subunits have been reported as prognostic indicators in gastric cancer, the prognostic value of CXCR subunits in PDAC remains poorly understood. In the present study, the expression levels and biological functions of CXCR subunits were investigated using multiple publicly accessible bioinformatic platforms and databases. Survival analysis was used to evaluate the prognostic value of CXCR subunits in 112 early-stage PDAC cases by setting the median expression levels as the cut-off values. A nomogram was constructed to combine CXCR subunit expression levels and clinical data for prognosis prediction. Moreover, the association between CXCR subunit expression levels and tumor infiltration levels were detected in PDAC. The expression levels of CXCR subunits were elevated in PDAC tumor tissues. In the multivariate Cox proportional risk regression model, high CXCR2, CXCR4 and CXCR6 expression levels in early-stage PDAC were associated with a more favorable prognosis. Further, it was demonstrated that the differential expression levels of CXCR subunits in PDAC for combined survival analysis could contribute to risk stratification. The nomogram model demonstrated the contribution of CXCR subunits and clinical features in the prognosis of PDAC. Gene Set Enrichment Analysis suggested that CXCR subunits serve a role in immunomodulatory functions. The expression levels and somatic copy number alterations of CXCR subunits were associated with tumor infiltration levels in PDAC. CXCR subunits were associated with prognosis in patients with early-stage PDAC and may be potential drug targets for the treatment of pancreatic cancer.

A Genome-Wide Screen in Mice To Identify Cell-Extrinsic Regulators of Pulmonary Metastatic Colonisation

Metastatic colonization, whereby a disseminated tumor cell is able to survive and proliferate at a secondary site, involves both tumor cell-intrinsic and -extrinsic factors. To identify tumor cell-extrinsic (microenvironmental) factors that regulate the ability of metastatic tumor cells to effectively colonize a tissue, we performed a genome-wide screen utilizing the experimental metastasis assay on mutant mice. Mutant and wildtype (control) mice were tail vein-dosed with murine metastatic melanoma B16-F10 cells and 10 days later the number of pulmonary metastatic colonies were counted. Of the 1,300 genes/genetic locations (1,344 alleles) assessed in the screen 34 genes were determined to significantly regulate pulmonary metastatic colonization (15 increased and 19 decreased; P < 0.005 and genotype effect <-55 or >+55). While several of these genes have known roles in immune system regulation (Bach2, Cyba, Cybb, Cybc1, Id2, Igh-6, Irf1, Irf7, Ncf1, Ncf2, Ncf4 and Pik3cg) most are involved in a disparate range of biological processes, ranging from ubiquitination (Herc1) to diphthamide synthesis (Dph6) to Rho GTPase-activation (Arhgap30 and Fgd4), with no previous reports of a role in the regulation of metastasis. Thus, we have identified numerous novel regulators of pulmonary metastatic colonization, which may represent potential therapeutic targets.

Three hypomethylated genes were associated with poor overall survival in pancreatic cancer patients

Pancreatic cancer (PC) is a highly malignant cancer with poor prognosis and high mortality. Aberrant DNA methylation plays a critical role in the occurrence, progression and prognosis of malignant tumors. In this study, we employed multiple datasets from APGI, TCGA and GEO to perform Multi-Omics analysis, including DNA methylation and expression profiling analysis. Three differentially expressed genes (SULT1E1, IGF2BP3, MAP4K4) with altered status of DNA methylation were identified and then enrolled into prognostic risk score model using LASSO regression. Univariate cox regression analysis indicated that high risk score was significantly associated with poor prognosis. Multivariate cox regression analysis proved the risk score was an independent prognostic factor for PC. In addition, time-dependent ROC curves indicated good performance of our model in predicting the 1-, 3- and 5-year survival of PC patients. Besides, stratified survival analysis revealed that the risk score model had greater prognostic value for patients of late stage with T3/T4 and N+. Pathway enrichment analysis suggested that these three genes might promote tumor progression by affecting signaling by Rho GTPases and chromosome segregation. In summary, three hypomethylated gene signature were significantly associated with patients' overall survival, which might serve as potential prognostic biomarkers for PC patients.

Identification of potential prognostic long non-coding RNA for predicting survival in intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is an aggressive biliary epithelial tumor with poor prognosis. There are increasing evidences that long non-coding RNAs (lncRNAs) are dysregulated in multifarious tumors, revealing potential significant role of lncRNAs in tumorigenesis.We used the ICC dataset retrieved from The Cancer Genome Atlas and the Gene Expression Omnibus database to obtain the lncRNAs expression profiles and identify potential prognostic lncRNAs for predicting the prognosis in ICC. Univariate and multivariate Cox regression analyses were performed to construct a prognostic index (PI). Furthermore, coexpression analysis and functional assessment were performed to initially investigate the function of these prognostic lncRNAs.A total of 255 differentially expressed lncRNAs (DElncRNAs) were identified among two RNA sequencing dataset of a total 63 ICC patients with 98 samples using R platform. Thirteen of 255 DElncRNAs were identified as prognostic lncRNAs and used for a PI. Patients with high PI were associated with poor prognostic (P = .0064), and the Cox regression showed consistent result (P = .042). The time-dependent receiver operating characteristic analysis showed the PI performed well in ICC survival prediction with an area under curve of 0.921, 0.801, and 0.717 for 1-, 3-, and 5-year survival, respectively.In conclusion, we included 13 identified prognostic DElncRNAs and constructed a prognostic signature/PI. ICC patient with higher PI was associated with poorer prognosis. However, the clinical role as well as biological functions of constructed PI and these prognostic DElncRNAs need to be verified in future study.

Identify potential clinical significance of long noncoding RNA forkhead box P4 antisense RNA 1 in patients with early stage pancreatic ductal adenocarcinoma

Previous studies have shown that forkhead box P4 antisense RNA 1 (FOXP4‐AS1) is dysregulated in tumor tissues and can serve as a prognostic indicator for multiple cancers. However, the clinical significance of FOXP4‐AS1 in pancreatic ductal adenocarcinoma (PDAC) remains unclear. The goal of this study is to recognize the possible clinical significance of long noncoding RNA FOXP4‐AS1 in patients with early stage PDAC. A total of 112 patients from The Cancer Genome Atlas (TCGA) PDAC cohort, receiving RNA sequencing, were involved in the study. Survival analysis, functional mechanism, and potential small molecule drugs of target therapy of FOXP4‐AS1 were performed in this study. Survival analysis in TCGA PDAC cohort suggested that patients with high FOXP4‐AS1 expression had significantly augmented possibility of death than in PDAC patients with lower FOXP4‐AS1 expression (adjusted P = .008; adjusted HR = 2.143, 95% CI = 1.221‐3.760). In this study, a genome‐wide RNA sequencing dataset was used to identify 927 genes co‐expressing with FOXP4‐AS1 in PDAC tumor tissues. A total of 676 differentially expressed genes were identified between different FOXP4‐AS1 expression groups. Functional enrichment analysis of these genes and gene set enrichment analysis for PDAC genome‐wide RNA sequencing dataset was done. We have found that FOXP4‐AS1 may function in PDAC by participating in biological processes and pathways including oxidative phosphorylation, tricarboxylic acid cycle, classical tumor‐related pathways such as NF‐kappaB as well as Janus kinase/signal transducers in addition to activators of transcription, cell proliferation, and adhesion. In addition, we also screened two potential targeted therapeutic small molecule drugs (dimenhydrinate and metanephrine) for FOXP4‐AS1 in PDAC. In conclusion, our present study demonstrated that higher expression of FOXP4‐AS1 in PDAC tumor tissues were related with an inferior medical outcome. Through multiple genome‐wide approaches, we identified the potential molecular mechanisms of FOXP4‐AS1 in PDAC and two targeted therapeutic drugs for it.

Prognostic value of Kinesin-4 family genes mRNA expression in early-stage pancreatic ductal adenocarcinoma patients after pancreaticoduodenectomy

BACKGROUND

The aim of this study was to investigate the potential prognostic value of Kinesin-4 family genes mRNA expression in early-stage pancreatic ductal adenocarcinoma (PDAC) patients after pancreaticoduodenectomy.

METHODS

Kaplan-Meier survival analysis method with log-rank test and Cox proportional hazards regression analysis were performed to figure out the association between Kinesin-4 family genes expression and PDAC patients overall survival time. Joint-effect survival analysis and stratified survival analysis were carried out to assess the prognosis prediction value of prognosis-related gene. Nomogram was constructed for the individualized prognosis prediction. In addition, we had used the gene set enrichment analysis and genome-wide co-expression analysis to further explore the potential mechanism.

RESULTS

KIF21A expression level was significantly associated with PDAC patient clinical prognosis outcome and patient with a high expression of KIF21A would have a shorter overall survival time. The prognosis prediction significance of KIF21A was well validated by the joint-effect survival analysis, stratified survival analysis, and nomogram. Meanwhile, the gene set enrichment analysis and genome-wide co-expression analysis revealed that KIF21A might involve in DNA damage and repair, transcription and translation process, post-translation protein modification, cell cycle, carcinogensis genes and pathways.

CONCLUSIONS

Our current research demonstrated that KIF21A could serve as a potential prognostic biomarker for patient with early-stage PDAC after pancreaticoduodenectomy.

Genome-wide analysis to identify a novel microRNA signature that predicts survival in patients with stomach adenocarcinoma

Objective: Using genome-wide screening, this study was aimed at identifying prognostic microRNA (miRNA) in those patients suffering from stomach adenocarcinoma (STAD).

Methods: A genome-wide miRNA sequencing dataset and relevant STAD clinical information was obtained via The Cancer Genome Atlas (TCGA). Prognostic miRNA selection was carried out through a whole genome multivariate Cox regression model in order to establish a prognostic STAD signature.

Results: Eleven miRNAs (hsa-mir-509-2, hsa-mir-3917, hsa-mir-495, hsa-mir-653, hsa-mir-3605, hsa-mir-2115, hsa-mir-1292, hsa-mir-137, hsa-mir-6511b-1, hsa-mir-145, and hsa-mir-138-2) were recognized as prognostic and used for the construction of a STAD prognostic signature. This signature exhibited good performance in predicting prognosis (adjusted P<0.0001, adjusted hazard ratio= 3.047, and 95% confidence interval=2.148-4.323). The time-dependent receiver operating characteristic examination exhibited area under curve values of 0.711, 0.697, 0.716, 0.733, 0.805, and 0.805, for 1-, 2-, 3-, 4-, 5-, and 10-year overall survival (OS) estimation, respectively. Comprehensive survival analysis suggests that the 11-miRNA prognostic signature acts as an independent feature of STAD prognosis and exhibits superior performance in OS prediction when compared to traditional clinical parameters. Furthermore, fourteen miRNA target genes were linked to STAD OS. These included SERPINE1, MLEC, ANGPT2, C5orf38, FZD7, MARCKS, PDGFD, DUSP6, IRS1, PSAT1, TENM3, TMEM127, BLMH, and TIRAP. Functional and gene set enrichment analysis suggested that target genes and the 11-miRNA prognostic signature were both participate in various biological processes and pathways, including the growth factor beta, Wnt, and Notch signaling pathways.

Conclusions: By means of a genome-wide analysis, an 11-miRNA expression signature that may serve as an underlying prognostic indicator for those patients suffering from STAD has been identified and described here.

Identification of a Nine-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival of Pancreatic Cancer

Background: Pancreatic cancer is highly lethal and aggressive with increasing trend of mortality in both genders. An effective prediction model is needed to assess prognosis of patients for optimization of treatment. Materials and Methods: Seven datasets of mRNA expression and clinical data were obtained from gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas pancreatic ductal adenocarcinoma (TCGA-PAAD) dataset. Differentially expressed genes (DEGs) between pancreatic tumor and normal tissue were identified by integrated analysis of multiple GEO datasets. Univariate and Lasso Cox regression analyses were applied to identify overall survival-related DEGs and establish a prognostic gene signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index (C-index) and calibration curve. GSE62452 and GSE57495 were used for external validation. Gene set enrichment analysis (GSEA) and tumor immunity analysis were applied to elucidate the molecular mechanisms and immune relevance. Multivariate Cox regression analysis was used to identify independent prognostic factors in pancreatic cancer. Finally, a prognostic nomogram was established based on the TCGA PAAD dataset. Results: A nine-gene signature comprising MET, KLK10, COL17A1, CEP55, ANKRD22, ITGB6, ARNTL2, MCOLN3, and SLC25A45 was established to predict overall survival of pancreatic cancer. The ROC curve and C-index indicated good performance of the nine-gene signature at predicting overall survival in the TCGA dataset and external validation datasets relative to classic AJCC staging. The nine-gene signature could classify patients into high- and low-risk groups with distinct overall survival and differentiate tumor from normal tissue. Univariate Cox regression revealed that the nine-gene signature was an independent prognostic factor in pancreatic cancer. The nomogram incorporating the gene signature and clinical prognostic factors was superior to AJCC staging in predicting overall survival. The high-risk group was enriched with multiple oncological signatures and aggressiveness-related pathways and associated with significantly lower levels of CD4⁺ T cell infiltration. Conclusion: Our study identified a nine-gene signature and established a prognostic nomogram that reliably predict overall survival in pancreatic cancer. The findings may be beneficial to therapeutic customization and medical decision-making.

Comprehensive investigation of key biomarkers and pathways in hepatitis B virus-related hepatocellular carcinoma

Objective: Our study is aim to explore potential key biomarkers and pathways in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) using genome-wide expression profile dataset and methods.

Methods: Dataset from the GSE14520 is used as the training cohort and The Cancer Genome Atlas dataset as the validation cohort. Differentially expressed genes (DEGs) screening were performed by the limma package. Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), gene ontology, the Kyoto Encyclopedia of Genes and Genomes, and risk score model were used for pathway and genes identification.

Results: GSEA revealed that several pathways and biological processes are associated with hepatocarcinogenesis, such as the cell cycle, DNA repair, and p53 pathway. A total of 160 DEGs were identified. The enriched functions and pathways of the DEGs included toxic substance decomposition and metabolism processes, and the P450 and p53 pathways. Eleven of the DEGs were identified as hub DEGs in the WGCNA. In survival analysis of hub DEGs, high expression of PRC1 and TOP2A were significantly associated with poor clinical outcome of HBV-related HCC, and shown a good performance in HBV-related HCC diagnosis. The prognostic signature consisting of PRC1 and TOP2A also doing well in the prediction of HBV-related HCC prognosis. The diagnostic and prognostic values of PRC1 and TOP2A was confirmed in TCGA HCC patients.

Conclusions: Key biomarkers and pathways identified in the present study may enhance the comprehend of the molecular mechanisms underlying hepatocarcinogenesis. Additionally, mRNA expression of PRC1 and TOP2A may serve as potential diagnostic and prognostic biomarkers for HBV-related HCC.

Comprehensive investigation of the clinical significance and molecular mechanisms of plasmacytoma variant translocation 1 in sarcoma using genome-wide RNA sequencing data

Objective: The present study aims to identify the potential clinical application and molecular mechanism of plasmacytoma variant translocation 1 (PVT1) in patients with sarcomas by mining an RNA sequencing dataset from The Cancer Genome Atlas (TCGA) through multiple genome-wide analysis approaches.

Methods: A genome-wide RNA sequencing dataset was downloaded from TCGA, survival analysis was used to evaluate the prognostic value of PVT1 in sarcoma. The potential mechanism was investigated by multiple tools: Database for Annotation, Visualization, and Integrated Discovery v6.8, gene set enrichment analysis (GSEA), and Connectivity Map (CMap).

Results: Comprehensive survival analysis indicated that overexpression of PVT1 was significantly associated with poor prognosis in patients with sarcoma, and nomogram demonstrated that PVT1 contributed more than other traditional clinical parameters in sarcoma survival prediction. Weighted gene co-expression network analysis identified ten hub differentially expressed genes (DEGs) between sarcoma tissues with low and overexpression of PVT1, and substantiated that these DEGs have a complex co-expression network relationship. CMap analysis has identified that antipyrine, ondansetron, and econazole may be candidate targeted drugs for sarcoma patients with PVT1 overexpression. GSEA revealed that overexpression of PVT1 may be involved in the posttranscriptional regulation of gene expression, tumor invasiveness and metastasis, osteoblast differentiation and development, apoptosis, nuclear factor kappa B, Wnt, and apoptotic related signaling pathways.

Conclusions: Our findings indicate that PVT1 may serve as a prognostic indicator in patients with sarcoma. Its underlying mechanism is revealed by GSEA, and CMap offers three candidate drugs for the individualized targeted therapy of sarcoma patients with overexpression of PVT1.

Clinical significance and prospective molecular mechanism of C‑C motif chemokine receptors in patients with early‑stage pancreatic ductal adenocarcinoma after pancreaticoduodenectomy

The present study aimed to determine the clinical significance and potential molecular mechanisms of C‑C motif chemokine receptor (CCR) genes in patients with early‑stage pancreatic ductal adenocarcinoma (PDAC). The transcriptomic, survival and clinical data of 112 patients with early‑stage PDAC who underwent pancreaticoduodenectomy were obtained from The Cancer Genome Atlas. The prognostic values of the CCR genes involved in early‑stage PDAC were evaluated using Kaplan‑Meier analysis and the multivariate Cox proportional risk regression model, and the potential molecular mechanisms were determined using bioinformatics tools. The identified CCRs closely interacted with each other at both the gene and protein levels. High expression levels of CCR5 [adjusted P=0.012; adjusted hazard ration (HR)=0.478, 95% confidence interval (CI)=0.269‑0.852], CCR6 (adjusted P=0.026; adjusted HR=0.527, 95% CI=0.299‑0.927) and CCR9 (adjusted P=0.001; adjusted HR=0.374, 95% CI=0.209‑0.670) were significantly associated with longer overall survival times in patients with early‑stage PDAC. The contribution of CCR5, CCR6 and CCR9 to the outcome of early‑stage PDAC was also demonstrated. Combined survival analysis of CCR5, CCR6 and CCR9 suggested that patients with high expression levels of these CCRs exhibited the most favorable outcomes. A prognostic signature was constructed in terms of the expression level of CC5, CCR6 and CCR9, and time‑dependent receiver operating characteristic curves indicated that this signature was able to effectively predict the outcome of patients with early‑stage PDAC. The potential molecular mechanisms of CCR5, CC6 and CCR9 in PDAC include its intersection of the P53, nuclear factor (NF)‑κB, generic transcription, mitogen‑activated protein kinase and STAT signaling pathways. Collectively, this highlights that CCR5, CCR6 and CCR9 are potential prognostic biomarkers for early‑stage PDAC.

Exosomal markers (CD63 and CD9) expression and their prognostic significance using immunohistochemistry in patients with pancreatic ductal adenocarcinoma

Background

Exosomes are important mediators of intercellular communications and play pivotal roles in cancer progression, metastasis and chemoresistance. CD63 and CD9 are widely accepted exosomal markers. In patients with pancreatic ductal adenocarcinoma (PDAC), positive correlation between CD9 expression and overall survival (OS) was reported. CD63 expression was conserved in all patients with no reported prognostic significance. This study explored the prognostic significance of CD63 and CD9 expression using immunohistochemistry (IHC) in patients with PDAC of mixed racial background.

Methods

Between 2012 and 2016, 49 patients with PDAC had available tissues for CD63 and CD9 staining using IHC. Two pathologists independently scored the CD63 and CD9 expression. Staining intensity was graded from 1-3 and staining percentage was estimated in 10% increments. Mean Quick-score (Q-score) (Intensity X Percentage of staining) was calculated.

Results

The mean Q-score for CD63 and CD9 are higher in primary tumor from the pancreas compared to pancreatic tumor from metastatic sites (185 vs. 102, P=0.0002) and (48 vs. 20, P=0.0418) respectively. We fitted Cox proportion hazard regression models to investigate the impact of the covariates CD63 and CD9 on progression free survival (PFS) and OS. CD63 has significant impact on PFS (P=0.0135) and OS (P=0.003). The higher the CD63 Q-score, the longer the PFS and OS. CD9 doesn't have significant impact on PFS (P=0.5734) or OS (P=0.2682). The mean CD63 and CD9 Q-scores are slightly higher in African American (AA) compared to Caucasians (157 vs. 149, P=0.76) and (45 vs. 29, P=0.43) respectively.

Conclusions

CD63 and CD9 expression is higher in primary tumor from the pancreas compared to pancreatic tumor from metastatic sites. There is correlation between CD63 expression (but not CD9 in this cohort) and PFS and OS. To our knowledge, this is the first study to show prognostic significance of CD63 expression in patients with PDAC using IHC. A trend of higher expression of CD63 and CD9 among AA compared to Caucasians was also noticed.

Genome‑wide investigation of the clinical significance and prospective molecular mechanisms of kinesin family member genes in patients with lung adenocarcinoma

The current study aimed to identify the potential clinical significance and molecular mechanisms of kinesin (KIF) family member genes in lung adenocarcinoma (LUAD) using genome-wide RNA sequencing (RNA-seq) datasets derived from The Cancer Genome Atlas (TCGA) database. Clinical parameters and RNA-seq data of patients with LUAD from the TCGA database enabled the assessment of the clinical significance of KIF genes, while the potential mechanisms of their interactions in LUAD were investigated by gene set enrichment analysis (GSEA). A gene signature with potential prognostic value was constructed via a stepwise multivariable Cox analysis. In total, 23 KIF genes were identified to be differentially expressed genes (DEGs) between the LUAD tumor and adjacent non-cancerous tissues. Of these, 8 differentially expressed KIF genes were strongly found to be strongly associated with the overall survival of patients with LUAD. Three of these genes were found to be able to be grouped as a potential prognostic gene signature. Patients with higher risk scores calculated using this gene signature were found to have a markedly higher risk of mortality (adjusted P=0.003; adjusted HR, 1.576; 95% CI, 1.166–2.129). Time-dependent receiver operating characteristic analysis indicated that this prognostic signature was able to accurately predict patient prognosis with an area under curve of 0.636, 0.643,0.665, 0.670 and 0.593 for the 1-, 2-, 3-, 4- and 5-year survival, respectively. This prognostic gene signature was identified as an independent risk factor for LUAD and was able to more accurately predict prognosis in comparison to other known clinical parameters, as shown via comprehensive survival analysis. GSEA enrichment revealed that that KIF14, KIF18B and KIF20A mediated basic cell physiology through the regulation of the cell cycle, DNA replication, and DNA repair biological processes and pathways. On the whole, the findings of this study identified 23 KIF genes that were DEGs between LUAD tumor and adjacent non-cancerous tissues. In total, 8 of these genes had the potential to function as prognostic and diagnostic biomarkers in patients with LUAD.

Identification of key modules and hub genes for small-cell lung carcinoma and large-cell neuroendocrine lung carcinoma by weighted gene co-expression network analysis of clinical tissue-proteomes

Small-cell lung carcinoma (SCLC) and large-cell neuroendocrine lung carcinoma (LCNEC) are high-grade lung neuroendocrine tumors (NET). However, comparative protein expression within SCLC and LCNEC remains unclear. Here, protein expression profiles were obtained via mass spectrometry-based proteomic analysis. Weighted gene co-expression network analysis (WGCNA) identified co-expressed modules and hub genes. Of 34 identified modules, six were significant and selected for protein-protein interaction (PPI) network analysis and pathway enrichment. Within the six modules, the activation of cellular processes and complexes, such as alternative mRNA splicing, translation initiation, nucleosome remodeling and deacetylase (NuRD) complex, SWItch/Sucrose Non-Fermentable (SWI/SNF) superfamily-type complex, chromatin remodeling pathway, and mRNA metabolic processes, were significant to SCLC. Modules enriched in processes, including signal recognition particle (SRP)-dependent co-translational protein targeting to membrane, nuclear-transcribed mRNA catabolic process of nonsense-mediated decay (NMD), and cellular macromolecule catabolic process, were characteristically activated in LCNEC. Novel high-degree hub genes were identified for each module. Master and upstream regulators were predicted via causal network analysis. This study provides an understanding of the molecular differences in tumorigenesis and malignancy between SCLC and LCNEC and may help identify potential therapeutic targets.

Integrated analysis of competing endogenous RNA network revealing potential prognostic biomarkers of hepatocellular carcinoma

Objective: The goal of our study is to identify a competing endogenous RNA (ceRNA) network using dysregulated RNAs between HCC tumors and the adjacent normal liver tissues from The Cancer Genome Atlas (TCGA) datasets, and to investigate underlying prognostic indicators in hepatocellular carcinoma (HCC) patients.

Methods: All of the RNA- and miRNA-sequencing datasets of HCC were obtained from TCGA, and dysregulated RNAs between HCC tumors and the adjacent normal liver tissues were investigated by DESeq and edgeR algorithm. Survival analysis was used to confirm underlying prognostic indicators.

Results: In the present study, we constructed a ceRNA network based on 16 differentially expressed genes (DEGs), 7 differentially expressed microRNAs and 34 differentially expressed long non-coding RNAs (DELs). Among these dysregulated RNAs, three DELs (AP002478.1, HTR2A-AS1, and ERVMER61-1) and six DEGs (enhancer of zeste homolog 2 [EZH2], kinesin family member 23 [KIF23], chromobox 2 [CBX2], centrosomal protein 55 [CEP55], cell division cycle 25A [CDC25A], and claspin [CLSPN]) were used for construct a prognostic signature for HCC overall survival (OS), and performed well in HCC OS (adjusted P<0.0001, adjusted hazard ratio = 2.761, 95% confidence interval = 1.838-4.147). Comprehensive survival analysis demonstrated that this prognostic signature may be act as an independent prognostic indicator of HCC OS. Functional assessment of these dysregulated DEGs in the ceRNA network and gene set enrichment of this prognostic signature suggest that both were enriched in the biological processes and pathways of the cell cycle, cell division and cell proliferation.

Conclusions: Our current study constructed a ceRNA network for HCC, and developed a prognostic signature that may act as an independent indicator for HCC OS.

Identification of Temporal Characteristic Networks of Peripheral Blood Changes in Alzheimer's Disease Based on Weighted Gene Co-expression Network Analysis

Alzheimer’s disease (AD) is a progressive neurodegenerative disease. The study of blood-based biomarkers has lasted for a long time in AD, because it supports the concept that peripheral changes are involved in AD pathology. But it is still unclear how peripheral blood is involved in the temporal characteristic molecular mechanisms of AD from aging to mild cognitive impairment (MCI) and which cells are responsible for the molecular mechanisms. The main purpose of our study is to gain a systematic and comprehensive understanding of temporal characteristic networks of peripheral blood in AD using whole blood samples with 329 case-control samples, including 104 normal elderly control subjects (CTL), 80 MCI who are susceptible to AD, and 145 AD, by the weighted gene co-expression network analysis (WGCNA). The module-trait relationships were constructed and module preservation was validated with independent datasets GSE63061, GSE97760, GSE18309, GSE29378, GSE28146, and GSE29652. Our results indicate that the down-regulated protein modification and ubiquitin degradation systems, and the up-regulated insulin resistance both play a major role in MCI, while the up-regulated inflammatory cascade dominates in AD, which is mainly mediated by monocytes, macrophages. Although there is mixed activation of M1 and M2 macrophages in all stages of AD, the immune neutral state or M2 polarization may predominate in MCI, and M1 polarization may predominate in AD. Moreover, we found that TRPV2, NDUFV1, ATF4, HSPA8, STAT3 and LUC7L3 may mediate the pathological changes in MCI, while SIRPA, LAMP-2, NDUFB5, HSPA8, STAT3 and FPR2 may mediate the conversion from MCI-AD or the pathological changes in AD, which provide a basis for the treatment based on the peripheral blood system. In addition, we also found that the combined diagnosis based on a panel of genes from the red, blue, and brown modules have a moderate diagnostic effect on distinguishing MCI and AD from CTL, suggesting that those panels of genes may be used for detection of MCI and prediction of this conversion from MCI to AD. Our research emphasizes that pathological changes, based on temporal characteristics of peripheral blood, provide a theoretical basis for targeted peripheral treatment based on appropriate times and identified several diagnostic markers.

Cystatin F as a key family 2 cystatin subunit and prognostic biomarker for early‑stage pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies, and early diagnosis and assessment may enhance the quality of life and survival of patients. The prognostic value of key family 2 cystatins subunit in PDAC patients remains unknown. The potential molecular roles of family 2 cystatins and related pathways were investigated using bioinformatics analysis. The relationship of family 2 cystatin expression levels and clinical outcomes of 112 patients with early-stage PDAC were evaluated via univariate and combined survival analysis. A prognostic nomogram model was also constructed and gene set enrichment analysis was performed to investigate potential pathways in PDAC. The pathways, interaction networks, and Gene Ontology term analysis of the cystatin gene family were analyzed in the present study. Cystatin F (CST7) was identified as the key subunit of family 2 cystatins in survival analysis. PDAC patients who harbored a higher expression level of CST7 had a lower risk in overall survival (adjusted HR_OS=0.44, 95% CI=0.25-0.77, P=0.004) and a longer survival time in various subgroups. The prognostic nomogram indicated that the CST7 expression model effectively predicted the outcomes of patients with early-stage PDAC (predictive ability >0.75). In the gene set enrichment analysis, it was revealed that CST7 expression may be involved in immune regulation and be associated with cell adhesion. CST7 could be a useful biomarker for the prognostic prediction of early-stage PDAC after pancreaticoduodenectomy.

Development of somatic mutation signatures for risk stratification and prognosis in lung and colorectal adenocarcinomas

Background

Prognostic signatures are vital to precision medicine. However, development of somatic mutation prognostic signatures for cancers remains a challenge. In this study we developed a novel method for discovering somatic mutation based prognostic signatures.

Results

Somatic mutation and clinical data for lung adenocarcinoma (LUAD) and colorectal adenocarcinoma (COAD) from The Cancer Genome Atlas (TCGA) were randomly divided into training (n = 328 for LUAD and 286 for COAD) and validation (n = 167 for LUAD and 141 for COAD) datasets. A novel method of using the log2 ratio of the tumor mutation frequency to the paired normal mutation frequency is computed for each patient and missense mutation. The missense mutation ratios were mean aggregated into gene-level somatic mutation profiles. The somatic mutations were assessed using univariate Cox analysis on the LUAD and COAD training sets separately. Stepwise multivariate Cox analysis resulted in a final gene prognostic signature for LUAD and COAD. Performance was compared to gene prognostic signatures generated using the same pipeline but with different somatic mutation profile representations based on tumor mutation frequency, binary calls, and gene-gene network normalization. Signature high-risk LUAD and COAD cases had worse overall survival compared to the signature low-risk cases in the validation set (log-rank test p-value = 0.0101 for LUAD and 0.0314 for COAD) using mutation tumor frequency ratio (MFR) profiles, while all other methods, including gene-gene network normalization, have statistically insignificant stratification (log-rank test p-value ≥0.05). Most of the genes in the final gene signatures using MFR profiles are cancer-related based on network and literature analysis.

Conclusions

We demonstrated the robustness of MFR profiles and its potential to be a powerful prognostic tool in cancer. The results are robust according to validation testing and the selected genes are biologically relevant.

Integrated analysis of dysregulated long non-coding RNAs/microRNAs/mRNAs in metastasis of lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD), largely remains a primary cause of cancer-related death worldwide. The molecular mechanisms in LUAD metastasis have not been completely uncovered.

METHODS

In this study, we identified differentially expressed genes (DEGs), miRNAs (DEMs) and lncRNAs (DELs) underlying metastasis of LUAD from The Cancer Genome Atlas database. Intersection mRNAs were used to perform gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and co-expression network analysis. In addition, survival analyses of intersection mRNAs were conducted. Finally, intersection mRNAs, miRNAs and lncRNAs were subjected to construct miRNA-mRNA-lncRNA network.

RESULTS

A total of 1015 DEGs, 54 DEMs and 22 DELs were identified in LUAD metastasis and non-metastasis samples. GO and KEGG pathway analysis had proven that the functions of intersection mRNAs were closely related with many important processes in cancer pathogenesis. Among the co-expression interactions network, 22 genes in the co-expression network were over the degree 20. These genes imply that they have connections with many other gene nodes. In addition, 14 target genes (ARHGAP11A, ASPM, HELLS, PRC1, TMPO, ARHGAP30, CD52, IL16, IRF8, P2RY13, PRKCB, PTPRC, SASH3 and TRAF3IP3) were found to be associated with survival in patients with LUAD significantly (log-rank P < 0.05). Two lncRNAs (LOC96610 and ADAM6) acting as ceRNAs were identified based on the miRNA-mRNA-lncRNA network.

CONCLUSIONS

Taken together, the results may provide a novel perspective to develop a multiple gene diagnostic tool for LUAD prognosis, which might also provide potential biomarkers or therapeutic targets for LUAD.

Diagnostic and prognostic values of the mRNA expression of excision repair cross-complementation enzymes in hepatitis B virus-related hepatocellular carcinoma

Background

The current study aims at using the whole genome expression profile chips for systematically investigating the diagnostic and prognostic values of excision repair cross-complementation (ERCC) genes in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC).

Materials and methods

Whole genome expression profile chips were obtained from the GSE14520. The receiver-operating characteristic (ROC) curve, survival analysis, and nomogram were used to investigate the diagnostic and prognostic values of ERCC genes. Investigation of the potential function of ERCC8 was carried out by gene set enrichment analysis (GSEA) and genome-wide coexpression analysis.

Results

ROC analysis suggests that six ERCC genes (ERCC1, ERCC2, ERCC3, ERCC4, ERCC5, and ERCC8) were dysregulated and may have potential to distinguish between HBV-related HCC tumor and paracancerous tissues (area under the curve of ROC ranged from 0.623 to 0.744). Survival analysis demonstrated that high ERCC8 expression was associated with a significantly decreased risk of recurrence (adjusted P=0.021; HR=0.643; 95% CI=0.442–0.937) and death (adjusted P=0.049; HR=0.631; 95% CI=0.399–0.998) in HBV-related HCC. Then, we also developed two nomograms for the HBV-related HCC individualized prognosis predictions. GSEA suggests that the high expression of ERCC8 may have involvement in the energy metabolism biological processes. As the genome-wide coexpression analysis and functional assessment of ERCC8 suggest, those coexpressed genes were significantly enriched in multiple biological processes of DNA damage and repair.

Conclusion

The present study indicates that six ERCC genes (ERCC1, ERCC2, ERCC3, ERCC4, ERCC5, and ERCC8) were dysregulated between HBV-related HCC tumor and paracancerous tissues and that the mRNA expression of ERCC8 may serve as a potential biomarker for the HBV-related HCC prognosis.

Serum level of co-expressed hub miRNAs as diagnostic and prognostic biomarkers for pancreatic ductal adenocarcinoma

Background: Sensitive and specific non-invasive biomarkers are urgently needed in order to improve the survival of patients with pancreatic ductal adenocarcinoma (PDAC), which is the fourth leading cause of cancer-related death. We aim to identify serum hub miRNAs as potential diagnostic and prognostic biomarkers for PDAC.

Methods: A total of 2578 serum miRNA expression data from 88 PDAC patients and 19 healthy subjects were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was constructed and significant modules were extracted from the network by WGCNA R package. Network modules and hub miRNAs closely related to PDAC were identified. The prognostic value of hub miRNAs was assessed by Kaplan-Meier overall survival analysis.

Results: Two modules strongly associated with PDAC were identified by WGCNA, which were labeled as turquoise and brown respectively. Within each module, twenty hub miRNAs were found. At the functional level, turquoise module was mainly associated with tumorigenesis pathways such as P53 and WNT signaling pathway, while the brown module was mostly related to the pathways of cancer such as RNA transport and MAPK signaling pathway. Utilizing overall survival analyses, five “real” miRNAs were able to stratify PDAC patients into low-risk and high-risk groups.

Conclusions: The association of specific Hub miRNAs with the development of pancreatic cancer was established by WGCNA analysis. Five miRNAs (mir-16-2-3p, mir-890, mir-3201, mir-602, and mir-877) were identified as potential diagnostic and prognostic biomarkers for PDAC.

Prognostic value of minichromosome maintenance mRNA expression in early-stage pancreatic ductal adenocarcinoma patients after pancreaticoduodenectomy

Background

The aim of the current study was to investigate the potential prognostic value of minichromosome maintenance (MCM) genes in patients with early-stage pancreatic ductal adenocarcinoma (PDAC) after pancreaticoduodenectomy by using the RNA-sequencing dataset from The Cancer Genome Atlas (TCGA).

Methods

An RNA-sequencing dataset of 112 early-stage PDAC patients who received a pancreaticoduodenectomy was obtained from TCGA. Survival analysis was used to identify potential prognostic values of MCM genes in PDAC overall survival (OS).

Results

Through mining public databases, we observed that MCM genes (MCM2, MCM3, MCM4, MCM5, MCM6, and MCM7) were upregulated in pancreatic cancer tumor tissue and have a strong positive coexpression with each other. Multivariate survival analysis indicated that a high expression of MCM4 significantly increased the risk of death in patients with PDAC, and time-dependent receiver operating characteristic analysis showed an area under the curve of 0.655, 0.587, and 0.509 for a 1-, 2-, and 3-year PDAC OS prediction, respectively. Comprehensive survival analysis of MCM4 using stratified and joint effects survival analysis suggests that MCM4 may be an independent prognostic indicator for PDAC OS. Gene set enrichment analysis indicated that MCM4 may participate in multiple biologic processes and pathways, including DNA replication, cell cycle, tumor protein p53, and Notch signaling pathways, thereby affecting prognosis of PDAC patients.

Conclusions

Our study indicates that MCM2–7 were upregulated in pancreatic cancer tumor tissues, and mRNA expression of MCM4 may serve as an independent prognostic indicator for PDAC OS prediction after pancreaticoduodenectomy.

Hematopoietic lineage cell-specific protein 1 (HS1), a hidden player in migration, invasion, and tumor formation, is over-expressed in ovarian carcinoma cells

Hematopoietic lineage cell-specific protein 1 (HS1), which is the hematopoietic homolog of cortactin, is an actin-binding protein and Lyn substrate. It is upregulated in several cancers and its expression level is associated with increased cell migration, metastasis, and poor prognosis. Here we investigated the expression and roles of HS1 in ovarian carcinoma cells. We analyzed the expression of HS1 in 171 ovarian cancer specimens and determined the association between HS1 expression and clinicopathological characteristics, including patient outcomes. In patients with stage II-IV disease, positive HS1 expression was associated with significantly worse overall survival than negative expression (P < 0.05). HS1 was localized in invadopodia in some ovarian cancer cells and was required for invadopodia formation. Migration and invasion of ovarian cancer cells were suppressed by down-regulation of HS1, but increased in cells that over-expressed exogenous HS1. Furthermore, ovarian cancer cells that expressed HS1 shRNA exhibited reduced tumor formation in a mouse xenograft model. Finally, we found that tyrosine phosphorylation of HS1 was essential for cell migration and invasion. These findings show that HS1 is a useful biomarker for the prognosis of patients with ovarian carcinoma and is a critical regulator of cytoskeleton remodeling involved in cell migration and invasion.