Identification of Prognostic Markers in Cholangiocarcinoma Using Altered DNA Methylation and Gene Expression Profiles

Integration analysis of miRNA-mRNA expression exploring their potential roles in intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is the second common primary hepatic malignancy tumor. In this study, an integrative analysis of differentially expressed genes (DEGs) and miRNAs from the ICC onset and adjacent normal tissues were performed to explore the regulatory roles of miRNA-mRNA interaction. A total of 1018 DEGs and 39 miRNAs were likely involved in ICC pathogenesis, suggesting the changes in cell metabolism in ICC development. The built network indicated that 30 DEGs were regulated by 16 differentially expressed miRNA. The screened DEGs and miRNA together were probably considered the biomarkers of ICC, and their important roles in ICC pathogenesis remain to be elucidated. This study could provide a good basis to uncover the regulatory mechanism of miRNA and mRNAs in ICC pathogenesis.

Low Expression of BRCA1 as a Potential Relapse Predictor in B-Cell Acute Lymphoblastic Leukemia

B-cell acute lymphoblastic leukemia (B-ALL) is the most common childhood hematological malignancy worldwide. Treatment outcomes have improved dramatically in recent years; despite this, relapse is still a problem, and the potential molecular explanation for this remains an important field of study. We performed microarray and single-cell RNA-Seq data mining, and we selected significant data with a P -value<0.05. We validated BRCA1 gene expression by means of quantitative (reverse transcription-polymerase chain reaction.) We performed statistical analysis and considered a P -value<0.05 significant. We identified the overexpression of breast cancer 1, early onset (BRCA1; P -value=2.52 -134 ), by means of microarray analysis. Moreover, the normal distribution of BRCA1 expression in healthy bone marrow. In addition, we confirmed the increases in BRCA1 expression using real-time (reverse transcription-polymerase chain reaction and determined that it was significantly reduced in patients with relapse ( P -values=0.026). Finally, we identified that the expression of the BRCA1 gene could predict early relapse ( P -values=0.01). We determined that low expression of BRCA1 was associated with B-cell acute lymphoblastic leukemia relapse and could be a potential molecular prognostic marker.

DEPDC1 and KIF4A synergistically inhibit the malignant biological behavior of osteosarcoma cells through Hippo signaling pathway

The treatment of osteosarcoma (OS) is still mainly surgery combined with systematic chemotherapy, and gene therapy is expected to improve the survival rate of patients. This study aimed to explore the effect of DEP domain 1 protein (DEPDC1) and kinesin super-family protein 4A (KIF4A) in OS and understand its mechanism. Th expression of DEPDC1 and KIF4A in OS cells was detected by RT-PCR and western blot. The viability, proliferation, invasion and migration of OS cells and tube formation of human umbilical vein endothelial cells (HUVECs) after indicated treatment were in turn detected by CCK-8 assay, EdU staining, wound healing assay, transwell assay and tube formation assay. The interaction between DEPDC1 and KIF4A was predicted by STRING and confirmed by co-immunoprecipitation. The expression of epithelial-mesenchymal transition (EMT)-related proteins, tube formation-related proteins and Hippo signaling pathway proteins was detected by western blot. As a result, the expression of DEPDC1 and KIF4A was all increased in U2OS cells. Down-regulation of DEPDC1 suppressed the viability, proliferation, invasion and migration of U2OS cells and tube formation of HUVECs, accompanied by the increased expression of E-cadherin and decreased expression of N-cadherin, Vimentin and VEGF. DEPDC1 was confirmed to be interacted with KIF4A. Upregulation of KIF4A partially reversed the effect of DEPDC1 interference on the above biological behaviors of U2OS cells. Down-regulation of DEPDC1 promoted the expression of p-LATS1 and p-YAP in Hippo signaling pathway, which was reversed by upregulation of KIF4A. In conclusion, down-regulation of DEPDC1 inhibited the malignant biological behavior of OS cells through the activation of Hippo signaling pathway, which could be reversed by upregulation of KIF4A.

Advances in prognostic and therapeutic targets for hepatocellular carcinoma and intrahepatic cholangiocarcinoma: The hippo signaling pathway

Primary liver cancer is the sixth most frequently diagnosed cancer worldwide and the third leading cause of cancer-related death. The majority of the primary liver cancer cases are hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Worldwide, there is an increasing incidence of primary liver cancer cases due to multiple risk factors ranging from parasites and viruses to metabolic diseases and lifestyles. Often, patients are diagnosed at advanced stages, depriving them of surgical curability benefits. Moreover, the efficacy of the available chemotherapeutics is limited in advanced stages. Furthermore, tumor metastases and recurrence make primary liver cancer management exceptionally challenging. Thus, exploring the molecular mechanisms for the development and progression of primary liver cancer is critical in improving diagnostic, treatment, prognostication, and surveillance modalities. These mechanisms facilitate the discovery of specific targets that are critical for novel and more efficient treatments. Consequently, the Hippo signaling pathway executing a pivotal role in organogenesis, hemostasis, and regeneration of tissues, regulates liver cells proliferation, and apoptosis. Cell polarity or adhesion molecules and cellular metabolic status are some of the biological activators of the pathway. Thus, understanding the mechanisms exhibited by the Hippo pathway is critical to the development of novel targeted therapies. This study reviews the advances in identifying therapeutic targets and prognostic markers of the Hippo pathway for primary liver cancer in the past six years.

PIWIL4 and SUPT5H combine to predict prognosis and immune landscape in intrahepatic cholangiocarcinoma

Background

Intrahepatic cholangiocarcinoma (ICC) is a fatal primary liver cancer, and its long-term survival rate remains poor. RNA-binding proteins (RBPs) play an important role in critical cellular processes, failure of any one or more processes can lead to the development of multiple cancers. This study aimed to explore pivotal biomarkers and corresponding mechanisms to predict the prognosis of patients with ICC.

Methods

The transcriptomic and clinical information of patients were collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Bioinformatic methods were used to identify survival-related and differentially-expressed biomarkers. Quantitative real-time PCR (qRT-PCR) and immunohistochemistry were used to detect the expression levels of key biomarkers in independent real-world cohorts. Subsequently, a prognostic signature was constructed that effectively distinguished patients in the high- and low-risk groups. Independent prognosis analysis was used to verify the signature’s independent predictive capabilities, and two nomograms were developed to predict survival.

Results

PIWIL4 and SUPT5H were identified and considered as pivotal biomarkers, and the same expression trends of upregulation in ICC were also validated via qRT-PCR and immunohistochemistry in the separate real-world sample cohorts. The prognostic signature showed good predictive capabilities according to the area under the curve. The correlation of the biomarkers with the tumour microenvironment suggested that the high riskScore was positively related to the enrichment of resting natural killer cells and activated memory CD4 + T cells.

Conclusion

In the present study, we demonstrated that PIWIL4 and SUPT5H could be used as novel prognostic biomarkers to develop a prognostic signature. This study provides potential biomarkers of prognostic value for patients with intrahepatic cholangiocarcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02310-2.

The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis

INTRODUCTION

Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver malignancies and is currently the fourth most common cause of cancer-related death worldwide. Due to varying underlying etiologies, the prognosis of HCC differs greatly among patients. It is important to develop ways to help stratify patients upon initial diagnosis to provide optimal treatment modalities and follow-up plans. The current study uses Artificial Neural Network (ANN) and Classification Tree Analysis (CTA) to create a gene signature score that can help predict survival in patients with HCC.

METHODS

The Cancer Genome Atlas (TCGA-LIHC) was analyzed for differentially expressed genes. Clinicopathological data were obtained from cBioPortal. ANN analysis of the 75 most significant genes predicting disease-free survival (DFS) was performed. Next, CTA results were used for creation of the scoring system. Cox regression was performed to identify the prognostic value of the scoring system.

RESULTS

363 patients diagnosed with HCC were analyzed in this study. ANN provided 15 genes with normalized importance >50%. CTA resulted in a set of three genes (NRM, STAG3, and SNHG20). Patients were then divided in to 4 groups based on the CTA tree cutoff values. The Kaplan-Meier analysis showed significantly reduced DFS in groups 1, 2, and 3 (median DFS: 29.7 months, 16.1 months, and 11.7 months, p < 0.01) compared to group 0 (median not reached). Similar results were observed when overall survival (OS) was analyzed. On multivariate Cox regression, higher scores were associated with significantly shorter DFS (1 point: HR 2.57 (1.38-4.80), 2 points: 3.91 (2.11-7.24), and 3 points: 5.09 (2.70-9.58), p < 0.01).

CONCLUSION

Long-term outcomes of patients with HCC can be predicted using a simplified scoring system based on tumor mRNA gene expression levels. This tool could assist clinicians and researchers in identifying patients at increased risks for recurrence to tailor specific treatment and follow-up strategies for individual patients.

The Role of microRNAs in Cholangiocarcinoma

Cholangiocarcinoma (CCA), an aggressive malignancy, is typically diagnosed at an advanced stage. It is associated with dismal 5-year postoperative survival rates, generating an urgent need for prognostic and diagnostic biomarkers. MicroRNAs (miRNAs) are a class of non-coding RNAs that are associated with cancer regulation, including modulation of cell cycle progression, apoptosis, metastasis, angiogenesis, autophagy, therapy resistance, and epithelial–mesenchymal transition. Several miRNAs have been found to be dysregulated in CCA and are associated with CCA-related risk factors. Accumulating studies have indicated that the expression of altered miRNAs could act as oncogenic or suppressor miRNAs in the development and progression of CCA and contribute to clinical diagnosis and prognosis prediction as potential biomarkers. Furthermore, miRNAs and their target genes also contribute to targeted therapy development and aid in the determination of drug resistance mechanisms. This review aims to summarize the roles of miRNAs in the pathogenesis of CCA, their potential use as biomarkers of diagnosis and prognosis, and their utilization as novel therapeutic targets in CCA.

Current Advances in Basic and Translational Research of Cholangiocarcinoma

Simple Summary

Cholangiocarcinoma (CCA) is highly malignant biliary tract cancer, which is characterized by limited treatment options and poor prognosis. Basic science studies to seek therapies for CCA are also limited due to lack of gold-standard experimental models and heterogeneity of CCA resulting in various genetic alterations and origins of tumor cells. Recent studies have developed new experimental models and techniques that may facilitate CCA studies leading to the development of novel treatments. This review summarizes the update in current basic studies of CCA.

Abstract

Cholangiocarcinoma (CCA) is a type of biliary tract cancer emerging from the biliary tree. CCA is the second most common primary liver cancer after hepatocellular carcinoma and is highly aggressive resulting in poor prognosis and patient survival. Treatment options for CCA patients are limited since early diagnosis is challenging, and the efficacy of chemotherapy or radiotherapy is also limited because CCA is a heterogeneous malignancy. Basic research is important for CCA to establish novel diagnostic testing and more effective therapies. Previous studies have introduced new techniques and methodologies for animal models, in vitro models, and biomarkers. Recent experimental strategies include patient-derived xenograft, syngeneic mouse models, and CCA organoids to mimic heterogeneous CCA characteristics of each patient or three-dimensional cellular architecture in vitro. Recent studies have identified various novel CCA biomarkers, especially non-coding RNAs that were associated with poor prognosis or metastases in CCA patients. This review summarizes current advances and limitations in basic and translational studies of CCA.

Artificial neural networks for multi-omics classifications of hepato-pancreato-biliary cancers: towards the clinical application of genetic data

PURPOSE

Several multi-omics classifications have been proposed for hepato-pancreato-biliary (HPB) cancers, but these classifications have not proven their role in the clinical practice and been validated in external cohorts.

PATIENTS AND METHODS

Data from whole-exome sequencing (WES) of The Cancer Genome Atlas (TCGA) patients were used as an input for the artificial neural network (ANN) to predict the anatomical site, iClusters (cell-of-origin patterns) and molecular subtype classifications. The Ohio State University (OSU) and the International Cancer Genome Consortium (ICGC) patients with HPB cancer were included in external validation cohorts. TCGA, OSU and ICGC data were merged, and survival analyses were performed using both the 'classic' survival analysis and a machine learning algorithm (random survival forest).

RESULTS

Although the ANN predicting the anatomical site of the tumour (i.e. cholangiocarcinoma, hepatocellular carcinoma of the liver, pancreatic ductal adenocarcinoma) demonstrated a low accuracy in TCGA test cohort, the ANNs predicting the iClusters (cell-of-origin patterns) and molecular subtype classifications demonstrated a good accuracy of 75% and 82% in TCGA test cohort, respectively. The random survival forest analysis and Cox' multivariable survival models demonstrated that models for HPB cancers that integrated clinical data with molecular classifications (iClusters, molecular subtypes) had an increased prognostic accuracy compared with standard staging systems.

CONCLUSION

The analyses of genetic status (i.e. WES, gene panels) of patients with HPB cancers might predict the classifications proposed by TCGA project and help to select patients suitable to targeted therapies. The molecular classifications of HPB cancers when integrated with clinical information could improve the ability to predict the prognosis of patients with HPB cancer.

Aberrantly DNA Methylated-Differentially Expressed Genes in Pancreatic Cancer Through an Integrated Bioinformatics Approach

Pancreatic cancer remains one of the chief contributors to cancer related deaths on a global scale, with its diagnosis often associated with poor prognosis and high mortality. Accumulating literature continues to highlight the role of aberrant DNA methylation in relation to pancreatic cancer progression. Integrated bioinformatics approaches in the characterization of methylated-differentially expressed genes (MeDEGs) in pancreatic cancer were employed to enhance our understanding of the potential underlying molecular mechanisms of this cancer. We initially identified differentially expressed genes (DEGs) between 178 pancreatic cancer samples and 4 normal samples and differentially methylated genes (DMGs) based on 185 pancreatic cancer samples as well as 10 normal samples by analyzing RNA sequencing data in the TCGA database. Eventually, 31 MeDEGs including 5 hypomethylated/upregulated genes and 26 hypermethylated/downregulated genes were identified. Univariate Cox model and Kaplan–Meier method revealed that, among 31 MeDEGs, 5 hypermethylated/downregulated genes (ZNF804A, ZFP82, TRIM58, SOX17, and C12orf42) were correlated with poor survival of patients with pancreatic cancer. KEGG pathway enrichment analysis by GSEA 3.0 and the protein–protein interaction (PPI) network revealed that these 5 MeDEGs were enriched in numerous cancer-related pathways in addition to interacting with each other, highlighting a significant role in the development of pancreatic cancer. Taken together, the key findings of the current study demonstrate that ZNF804A, ZFP82, TRIM58, SOX17, and C12orf42 are hypermethylated/downregulated genes in pancreatic cancer and may be associated, through their modulation of specific pathways, with unfavorable pancreatic cancer prognosis.

Pan-Cancer Analysis of Human Kinome Gene Expression and Promoter DNA Methylation Identifies Dark Kinase Biomarkers in Multiple Cancers

Kinases are a group of intracellular signaling molecules that play critical roles in various biological processes. Even though kinases comprise one of the most well-known therapeutic targets, many have been understudied and therefore warrant further investigation. DNA methylation is one of the key epigenetic regulators that modulate gene expression. In this study, the human kinome's DNA methylation and gene expression patterns were analyzed using the level-3 TCGA data for 32 cancers. Unsupervised clustering based on kinome data revealed the grouping of cancers based on their organ level and tissue type. We further observed significant differences in overall kinase methylation levels (hyper- and hypomethylation) between the tumor and adjacent normal samples from the same tissue. Methylation expression quantitative trait loci (meQTL) analysis using kinase gene expression with the corresponding methylated probes revealed a highly significant and mostly negative association (~92%) within 1.5 kb from the transcription start site (TSS). Several understudied (dark) kinases (PKMYT1, PNCK, BRSK2, ERN2, STK31, STK32A, and MAPK4) were also identified with a significant role in patient survival. This study leverages results from multi-omics data to identify potential kinase markers of prognostic and diagnostic importance and further our understanding of kinases in cancer.