Transcriptomic analysis and identification of prognostic biomarkers in cholangiocarcinoma

Protein regulator of cytokinesis 1 accentuates cholangiocarcinoma progression via mTORC1-mediated glycolysis

This study aimed to investigate the expression of protein regulator of cytokinesis 1 (PRC1) in cholangiocarcinoma (CHOL) and elucidate its potential impact as well as the underlying mechanisms governing the progression of CHOL. In this study, we used CHOL cells (HUCCT1, RBE, and CCLP1) and conducted a series of experiments, including qRT-PCR, cell counting kit-8 assays, EdU assays, flow cytometry, wound healing assays, Transwell assays, western blotting, double luciferase assays, and ELISA. Subsequently, a mouse model was established using cancer cell injections. Haematoxylin-eosin staining, along with Ki67 and TUNEL assays, were employed to assess tissue histopathology, cell proliferation, and apoptosis. Our findings revealed significantly elevated PRC1 expression in CHOL. According to bioinformatics analysis, it was found that the increased PRC1 level is correlated with the high tumour grades, metastases, and unfavourable prognoses. Notably, PRC1 knockdown inhibited cell viability, proliferation, migration, and invasion while promoting apoptosis in CHOL cells. Analysing TCGA-CHOL data and utilising transcription factor prediction tools (hTFtarget and HumanTFDB), we identified that genes positively correlated with PRC1 in TCGA-CHOL intersect with predicted transcription factors, revealing the activation of PRC1 by forkhead box protein M1 (FOXM1). Moreover, PRC1 was found to exert regulatory control over glycolysis and the mammalian target of rapamycin complex 1 (mTORC1) pathway in the context of CHOL based on KEGG and GSEA analysis. Collectively, these results underscore the pivotal role of PRC1 in CHOL progression, wherein it modulates glycolysis and the mTORC1 pathway under the regulatory influence of FOXM1.

Integrin β5 is an independent prognostic marker for intrahepatic cholangiocarcinoma in a Chinese population

Intrahepatic cholangiocarcinoma (ICC) is the second most common primary liver tumor and a major cause of cancer mortality worldwide. Integrin β5 (ITGB5) is considered to be involved in the intercellular signal transduction and regulation of tumorigenesis and development. The present study investigated the association between ITGB5 expression levels and the prognosis of ICC, as well as the effects of ITGB5 on the proliferation and invasion of ICC cells. RNA-sequencing transcriptomic profiling data of ICC samples were retrieved from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Tissue specimens from patients with ICC treated at Taizhou People's Hospital were collected and the ITGB5 expression levels were evaluated using immunohistochemical staining. The biological function of ITGB5 in ICC was investigated using Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA) and in vitro experiments using HuCCT1 cells. After knocking down ITGB5 expression, cell proliferation was detected using Cell Counting Kit-8 assay, while cell invasion was assessed using Transwell assays. According to TCGA dataset, ITGB5 was highly expressed in ICC; however, there was no significant difference in prognosis between patients with high and low ITGB5 expression levels. High expression of ITGB5 was present in the tissues of patients with ICC from the GEO database, which was associated with poor prognosis. Survival analyses of the clinical data obtained in the present study revealed that high expression levels of ITGB5 in patients with ICC were associated with a reduced overall survival. GO and GSEA indicated that genes associated with ITGB5 were enriched in the extracellular matrix-receptor interaction and focal adhesion signaling pathways. Silencing ITGB5 inhibited the proliferation and invasion of ICC cells. In conclusion, ITGB5 may act as an essential regulator of ICC development and progression by influencing the proliferation and invasion of ICC cells. However, future studies with larger sample sizes are required to validate the role of ITGB5 in the prognosis of patients with ICC.

MMP14 is a diagnostic gene of intrahepatic cholangiocarcinoma associated with immune cell infiltration

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor of the hepatobiliary system with concealed onset, strong invasiveness and poor prognosis.

AIM

To explore the disease characteristic genes that may be helpful in the diagnosis of ICC and affect immune cell infiltration.

METHODS

We downloaded two ICC-related human gene expression profiles from GEO database as the training group (GSE26566 and GSE32958 datasets) for difference analysis, and performed enrichment analysis on differential genes. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF), three machine learning algorithms, were used to screen the characteristic genes. Double verification was carried out on GSE107943 and The Cancer Genome Atlas, two verification groups. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the diagnostic efficacy of genes for ICC. CIBERSORT and ssGSEA algorithms were used to evaluate the effect of characteristic genes on immune infiltration pattern. Human Protein Atlas (HPA) was used to analyze the protein expression level of the target gene.

RESULTS

A total of 1091 differential genes were obtained in the training group. Enrichment analysis showed that the above genes were mainly enriched in small molecular catabolism, complement and coagulation cascade, bile secretion and other functions and pathways. Twenty-five characteristic genes were screened by LASSO regression, 19 by SVM-RFE algorithm, and 30 by RF algorithm. Three algorithms were used in combination to determine the characteristic gene of ICC: MMP14. The verification group confirmed that the genes had a high diagnostic accuracy (AUC values of the training group and the verification group were 0.960, 0.999, and 0.977, respectively). Comprehensive analysis of immune infiltration showed that MMP14 could affect the infiltration of monocytes, activated memory CD4 T cells, resting memory CD4 T cells, and other immune cells, and was closely related to the expression of CD200, cytotoxic T-lymphocyte-associated antigen 4, CD14, CD44, and other immune checkpoints. The results of immunohistochemistry in HPA database showed was indeed overexpressed in ICC.

CONCLUSION

MMP14 can be used as a disease characteristic gene of ICC, and may regulate the distribution of immune-infiltrating cells in the ICC tumor microenvironment, which provides a new method for the determination of ICC diagnostic markers and screening of therapeutic targets.

Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangiocarcinoma

Background

Distal cholangiocarcinoma (dCCA), originating from the common bile duct, is greatly associated with a dismal prognosis. A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. In this study, we explored and compared several novel machine learning models that might lead to an improvement in prediction accuracy and treatment options for patients with dCCA.

Methods

In this study, 169 patients with dCCA were recruited and randomly divided into the training cohort (n = 118) and the validation cohort (n = 51), and their medical records were reviewed, including survival outcomes, laboratory values, treatment strategies, pathological results, and demographic information. Variables identified as independently associated with the primary outcome by least absolute shrinkage and selection operator (LASSO) regression, the random survival forest (RSF) algorithm, and univariate and multivariate Cox regression analyses were introduced to establish the following different machine learning models and canonical regression model: support vector machine (SVM), SurvivalTree, Coxboost, RSF, DeepSurv, and Cox proportional hazards (CoxPH). We measured and compared the performance of models using the receiver operating characteristic (ROC) curve, integrated Brier score (IBS), and concordance index (C-index) following cross-validation. The machine learning model with the best performance was screened out and compared with the TNM Classification using ROC, IBS, and C-index. Finally, patients were stratified based on the model with the best performance to assess whether they benefited from postoperative chemotherapy through the log-rank test.

Results

Among medical features, five variables, including tumor differentiation, T-stage, lymph node metastasis (LNM), albumin-to-fibrinogen ratio (AFR), and carbohydrate antigen 19-9 (CA19-9), were used to develop machine learning models. In the training cohort and the validation cohort, C-index achieved 0.763 vs. 0.686 (SVM), 0.749 vs. 0.692 (SurvivalTree), 0.747 vs. 0.690 (Coxboost), 0.745 vs. 0.690 (RSF), 0.746 vs. 0.711 (DeepSurv), and 0.724 vs. 0.701 (CoxPH), respectively. The DeepSurv model (0.823 vs. 0.754) had the highest mean area under the ROC curve (AUC) than other models, including SVM (0.819 vs. 0.736), SurvivalTree (0.814 vs. 0.737), Coxboost (0.816 vs. 0.734), RSF (0.813 vs. 0.730), and CoxPH (0.788 vs. 0.753). The IBS of the DeepSurv model (0.132 vs. 0.147) was lower than that of SurvivalTree (0.135 vs. 0.236), Coxboost (0.141 vs. 0.207), RSF (0.140 vs. 0.225), and CoxPH (0.145 vs. 0.196). Results of the calibration chart and decision curve analysis (DCA) also demonstrated that DeepSurv had a satisfactory predictive performance. In addition, the performance of the DeepSurv model was better than that of the TNM Classification in C-index, mean AUC, and IBS (0.746 vs. 0.598, 0.823 vs. 0.613, and 0.132 vs. 0.186, respectively) in the training cohort. Patients were stratified and divided into high- and low-risk groups based on the DeepSurv model. In the training cohort, patients in the high-risk group would not benefit from postoperative chemotherapy (p = 0.519). In the low-risk group, patients receiving postoperative chemotherapy might have a better prognosis (p = 0.035).

Conclusions

In this study, the DeepSurv model was good at predicting prognosis and risk stratification to guide treatment options. AFR level might be a potential prognostic factor for dCCA. For the low-risk group in the DeepSurv model, patients might benefit from postoperative chemotherapy.

Novel biomarkers and the future of targeted therapies in cholangiocarcinoma: a narrative review

Background and Objectives

Cholangiocarcinoma is a highly aggressive and heterogenous group of biliary malignancies arising from any site in the biliary tree, comprising 15% of all primary liver cancers. The nature of the disease and nonspecific presentation leads to late diagnosis and ultimately poor outcomes for patients. Combination gemcitabine and cisplatin has been the standard of care for cholangiocarcinoma (CCA) since 2010, with a median overall survival of 11.7 months. The five-year survival for CCA remains 5-10%, revealing a clear need for improved treatment options.

Methods

This targeted review highlights the role of next generation sequencing in CCA and the clinically relevant tumor biomarkers that have become the focus of therapeutic development.

Key Content and Findings

These tumor biomarkers or actionable mutations hold the potential to enable earlier diagnosis, provide prognostic information, and guide treatment decisions for patients with CCA. Specifically, the FGFR2 fusion and IDH1 mutation have shown considerable promise in development of targeted therapies. Clinical trials with inhibitors targeting FGFR2 fusion and IDH1 mutation have created expectations that these drugs will soon enter clinical practice. Other biomarkers including KRAS and B-raf protooncogenes, Her2/neu genes, and BRCA1 and 2 tumor-suppressor genes have also been touted as potential targets for future therapies, with early data showing promise for new drug development.

Conclusion

The discovery of these actionable mutations and identification of targeted therapies have challenged the notion of a "one-size fits all" for treatment of CCA, and generated optimism that these novel treatments will soon be available for patients with CCA.

Biomarkers and Genetic Markers of Hepatocellular Carcinoma and Cholangiocarcinoma-What Do We Already Know

Simple Summary

Hepatocellular carcinoma and cholangiocarcinoma continue to remain a serious threat. In this review, we describe the most common biomarkers and genetic markers currently used in the diagnosis of hepatocellular carcinoma and cholangiocarcinoma. It can be observed that biomarkers and genetic markers might be applied in various parts of diagnosis including screening tests in a high-risk group, non-invasive detection, control of therapy, treatment selection, and control of recurrence. Also, it can be seen that nowadays there is a need for more specific markers that would improve the detection in early or very early stages of both types of cancers and further research should be focused on it.

Abstract

Hepatocellular carcinoma (HCC) is the most common primary liver cancer with an increasing worldwide mortality rate. Cholangiocarcinoma (CCA) is the second most common primary liver cancer. In both types of cancers, early detection is very important. Biomarkers are a relevant part of diagnosis, enabling non-invasive detection and control of cancer recurrence, as well as in the application of screening tests in high-risk groups. Furthermore, some of these biomarkers are useful in controlling therapy and treatment selection. Detection of some markers presents higher sensitivity and specificity in combination with other markers when compared with a single detection. Some gene aberrations are also prognostic markers in the two types of cancers. In the following review, we discuss the most common biomarkers and genetic markers currently being used in the diagnosis of hepatocellular carcinoma and cholangiocarcinoma.

Identification and Validation of Three Autophagy-Related Long Noncoding RNAs as Prognostic Signature in Cholangiocarcinoma

Cholangiocarcinoma (CCA) is featured by common occurrence and poor prognosis. Autophagy is a biological process that has been extensively involved in the progression of tumors. Long noncoding RNAs (lncRNAs) have been discovered to be critical in diagnosing and predicting various tumors. It may be valuable to elaborate autophagy-related lncRNAs (ARlncRNAs) in CCA, and indeed, there are still few studies concerning the role of ARlncRNAs in CCA. Here, a prognostic ARlncRNA signature was constructed to predict the survival outcome of CCA patients. Through identification, three differentially expressed ARlncRNAs (DEARlncRNAs), including CHRM3.AS2, MIR205HG, and LINC00661, were screened and were considered predictive signatures. Furthermore, the overall survival (OS) of patients with high-risk scores was significantly lower than that of patients with low scores. Interestingly, the risk score was an independent factor for the OS of patients with CCA. Moreover, receiver operating characteristic (ROC) curve analysis showed that the screened and constructed prognosis signature for 1 year (AUC = 0.884), 3 years (AUC =0.759), and 5 years (AUC = 0.788) presented a high score of accuracy in predicting OS of CCA patients. Gene set enrichment analysis (GSEA) revealed that the three DEARlncRNAs were significantly enriched in CCA-related signaling pathways, including “pathways of basal cell carcinoma”, “glycerolipid metabolism”, etc. Quantitative real-time PCR (qRT-PCR) showed that expressions of CHRM3.AS2, MIR205HG, and LINC00661 were higher in CCA tissues than those in normal tissues, similar to the trends detected in the CCA dataset. Furthermore, Pearson’s analysis reported an intimate correlation of the risk score with immune cell infiltration, indicating a predictive value of the signature for the efficacy of immunotherapy. In addition, the screened lncRNAs were found to have the ability to modulate the expression of mRNAs by interacting with miRNAs based on the established lncRNA-miRNA-mRNA network. In conclusion, our study develops a novel nomogram with good reliability and accuracy to predict the OS of CCA patients, providing a significant guiding value for developing tailored therapy for CCA patients.

Integrative Analysis for Elucidating Transcriptomics Landscapes of Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a complex and heterogeneous autoimmune disease that the immune system attacks healthy cells and tissues. SLE is difficult to get a correct and timely diagnosis, which makes its morbidity and mortality rate very high. The pathogenesis of SLE remains to be elucidated. To clarify the potential pathogenic mechanism of SLE, we performed an integrated analysis of two RNA-seq datasets of SLE. Differential expression analysis revealed that there were 4,713 and 2,473 differentially expressed genes, respectively, most of which were up-regulated. After integrating differentially expressed genes, we identified 790 common differentially expressed genes (DEGs). Gene functional enrichment analysis was performed and found that common differentially expressed genes were significantly enriched in some important immune-related biological processes and pathways. Our analysis provides new insights into a better understanding of the pathogenic mechanisms and potential candidate markers for systemic lupus erythematosus.

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.

Analysis of transcriptome in the relationship between expression of PRC1 protein and prognosis of patients with cholangiocarcinoma

OBJECTIVE

To investigate whether protein regulator of cytokinesis 1 (PRC1), which is involved in the regulation of human carcinogenesis, contributes to poor prognosis in patients with cholangiocarcinoma (CCA).

METHODS

Data and tissues from patients with CCA were retrospectively studied. Immunohistochemical staining and western blotting were used to evaluate and contrast the PRC1 expression profile at the protein level in CCA tumour and pericarcinomatous tissues from the same study population. Relationships between clinical characteristics and patient survival were observed using univariate and multivariate analyses. Correlations between PRC1 expression and clinical characteristics were analysed by logistic regression.

RESULTS

A total of 45 patients were included. PRC1 expression was found to be upregulated in CCA cancer tissues versus pericarcinomatous tissues. Overexpression of PRC1 was shown to be related to tumour differentiation, tumour node metastasis staging and lymph node metastasis, and was also revealed to be an independent marker of poor CCA prognosis.

CONCLUSIONS

The present results suggest that PRC1 may be a prognostic and therapeutic biomarker for patients with CCA.

Behavioral and Gene Expression Analysis of Stxbp6-Knockout Mice

Since the first report that Stxbp6, a brain-enriched protein, regulates the assembly of soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complexes, little has been discovered about its functions over the past two decades. To determine the effects of Stxbp6 loss on nervous-system-associated phenotypes and underlying mechanisms, we constructed a global Stxbp6-knockout mouse. We found that Stxbp6-null mice survive normally, with normal behavior, but gained less weight relative to age- and sex-matched wildtype mice. RNA-seq analysis of the cerebral cortex of Stxbp6-null mice relative to wildtype controls identified 126 differentially expressed genes. Of these, 57 were upregulated and 69 were downregulated. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the most significant enriched KEGG term was “complement and coagulation cascades”. Our results suggest some potential regulatory pathways of Stxbp6 in the central nervous system, providing a remarkable new resource for understanding Stxbp6 function at the organism level.

Profiles of alternative splicing events in the diagnosis and prognosis of Gastric Cancer

Background: Gastric cancer (GC) is a heterogeneous disease, and alternative splicing (AS) is a powerful universal transcriptional regulatory mechanism that contributes to the occurrence and development of cancer. However, the systematic analysis of AS events in GC is lacking; therefore, further studies are needed. Methods: Genome-wide analysis of AS events was performed using RNA-Seq data to evaluate the difference between GC and adjacent tissues at the AS level. Prognostic signatures based on differentially expressed alternative splicing (DEAS) events and a correlation network between DEAS and genes were built. Results: We identified 48,141 AS events, of which 2325 showed differential expression patterns. The parental genes before DEAS events play an essential role in regulating GC-related processes such as ribosome (FDR < 0.0001) and thermogenesis (FDR = 0.0002). There were 76 survival-associated DEAS cases. Stratifying patients according to the percent spliced in index value of six types of splicing patterns formed significant Kaplan-Meier curves in the overall survival analysis. A prognostic feature based on DEAS performed well for stratification in patients with GC. Conclusion: The present study will enrich our understanding regarding the distinction of GC and provide a generous amount of biomarkers and potential targets for the treatment of GC.

Comprehensive circular RNA expression profiling constructs a ceRNA network and identifies hsa_circ_0000673 as a novel oncogene in distal cholangiocarcinoma

Circular RNAs (circRNAs) play an important role in cholangiocarcinoma (CCA) development; however, the expression and functions of circRNAs in distal CCA (dCCA) remain unknown. Herein, we explored the expression profile of circRNAs in six paired dCCA tumor and adjacent normal tissue samples using microarray. A total of 171 differentially expressed (DE) circRNAs were identified in dCCA tissues. Host genes of DE circRNAs were enriched in the cellular cytoskeleton and adheren junction. Bioinformatics analyses were used to establish a circRNA-microRNA-mRNA network for dCCA. Protein-protein interaction networks were constructed, and five hub genes were associated with the regulation of the cell cycle based on gene set enrichment analyses. Five DE circRNAs were validated with qRT-PCR in 40 pairs of dCCA tissues, and hsa_circ_0000673 showed promising diagnostic performance in distinguishing dCCA from normal tissues (AUC = 0.85, p < 0.01). Overexpression of hsa_circ_0000673 was associated with tumor invasion (p = 0.001), poor differentiation (p = 0.041), and residual tumor (p = 0.044). In vitro experiments indicated that inhibition of hsa_circ_0000673 suppressed the proliferation, migration, and invasion of CCA cells. This research provided a landscape of dysregulated circRNAs in dCCA and identified hsa_circ_0000673 as a potential biomarker and therapeutic target for dCCA.

Co-expression based cancer staging and application

A novel method is developed for predicting the stage of a cancer tissue based on the consistency level between the co-expression patterns in the given sample and samples in a specific stage. The basis for the prediction method is that cancer samples of the same stage share common functionalities as reflected by the co-expression patterns, which are distinct from samples in the other stages. Test results reveal that our prediction results are as good or potentially better than manually annotated stages by cancer pathologists. This new co-expression-based capability enables us to study how functionalities of cancer samples change as they evolve from early to the advanced stage. New and exciting results are discovered through such functional analyses, which offer new insights about what functions tend to be lost at what stage compared to the control tissues and similarly what new functions emerge as a cancer advances. To the best of our knowledge, this new capability represents the first computational method for accurately staging a cancer sample. The R source code used in this study is available at GitHub (https://github.com/yxchspring/CECS).