Liver-Metastasis-Related Genes are Potential Biomarkers for Predicting the Clinical Outcomes of Patients with Pancreatic Adenocarcinoma

DNA methylation biomarker panels for differentiating various liver adenocarcinomas, including hepatocellular carcinoma, cholangiocarcinoma, colorectal liver metastases and pancreatic adenocarcinoma liver metastases

BACKGROUND

DNA methylation biomarkers are one of the most promising tools for the diagnosis and differentiation of adenocarcinomas of the liver, which are among the most common malignancies worldwide. Their differentiation is important because of the different prognoses and treatment options. This study aimed to validate previously identified DNA methylation biomarkers that successfully differentiate between liver adenocarcinomas, including the two most common primary liver cancers, hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), as well as two common metastatic liver cancers, colorectal liver metastases (CRLM) and pancreatic ductal adenocarcinoma liver metastases (PCLM), and translate them to the methylation-sensitive high-resolution melting (MS-HRM) and digital PCR (dPCR) platforms.

METHODS

Our study included a cohort of 149 formalin-fixed, paraffin-embedded tissue samples, including 19 CRLMs, 10 PCLMs, 15 HCCs, 15 CCAs, 15 colorectal adenocarcinomas (CRCs), 15 pancreatic ductal adenocarcinomas (PDACs) and their paired normal tissue samples. The methylation status of the samples was experimentally determined by MS-HRM and methylation-specific dPCR. Previously determined methylation threshold were adjusted according to dPCR data and applied to the same DNA methylation array datasets (provided by The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO)) used to originally identify the biomarkers for the included cancer types and additional CRLM projects. The sensitivities, specificities and diagnostic accuracies of the panels for individual cancer types were calculated.

RESULTS

In the dPCR experiment, the DNA methylation panels identified HCC, CCA, CRC, PDAC, CRLM and PCLM with sensitivities of 100%, 66.7%, 100%, 86.7%, 94.7% and 80%, respectively. The panels differentiate between HCC, CCA, CRLM, PCLM and healthy liver tissue with specificities of 100%, 100%, 97.1% and 94.9% and with diagnostic accuracies of 100%, 94%, 97% and 93%, respectively. Reevaluation of the same bioinformatic data with new additional CRLM projects demonstrated that the lower dPCR methylation threshold still effectively differentiates between the included cancer types. The bioinformatic data achieved sensitivities for HCC, CCA, CRC, PDAC, CRLM and PCLM of 88%, 64%, 97.4%, 75.5%, 80% and 84.6%, respectively. Specificities between HCC, CCA, CRLM, PCLM and healthy liver tissue were 98%, 93%, 86.6% and 98.2% and the diagnostic accuracies were 94%, 91%, 86% and 98%, respectively. Moreover, we confirmed that the methylation of the investigated promoters is preserved from primary CRC and PDAC to their liver metastases.

CONCLUSIONS

The cancer-specific methylation biomarker panels exhibit high sensitivities, specificities and diagnostic accuracies and enable differentiation between primary and metastatic adenocarcinomas of the liver using methylation-specific dPCR. High concordance was achieved between MS-HRM, dPCR and bioinformatic data, demonstrating the successful translation of bioinformatically identified methylation biomarkers from the Illumina Infinium HumanMethylation450 BeadChip (HM450) and lllumina MethylationEPIC BeadChip (EPIC) platforms to the simpler MS-HRM and dPCR platforms.

Chloride Intracellular Channel Protein 1 Expression and Angiogenic Profile of Liver Metastasis of Digestive Origin

Chloride intracellular channel 1 (CLIC1) is involved in cell migration and metastasis. The histological growth patterns of liver metastasis are as follows: desmoplastic (d-HGP), replacement (r-HGP), pushing (p-HGP), and mixed. The aim of this study was to evaluate the relation between HGP, angiogenesis, and CLIC1 expression. Materials and Methods: A total of 40 cases of primary tumors and their LM: d-HGP (12 cases), r-HGP (13 cases), and p-HGP (15 cases), were evaluated through simple and double immunostaining. CLIC1 assessment was conducted as follows: scores of 0 (less than 10% of positive cells), 1 (10-30%), 2 (30-50%), or 3 (more than 50%) were assigned. Heterogeneous CLIC1 expression was found. CLIC1 in primary tumors correlated with grade G for all cases of LM with a p-HGP (p = 0.004). The CLIC1 score for LMs with an r-HGP correlated with grade G of the corresponding primary tumor (p = 0.027). CLIC1 and CD34+/Ki67+ vessels (p = 0.006) correlated in primary tumors. CLIC1 in primary tumors correlated with CD34+/Ki67+ vessels of LMs with a d HGP (p = 0.024). Conclusions: The CLIC1 score may have prognostic value, mainly for LMs with a p-HGP and r-HGP, and therapeutic value for LMs with a d-HGP.

Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer

BACKGROUND

Lung cancer is a high-incidence cancer, and it is also the most common cause of cancer death worldwide. 80-85% of lung cancer cases can be classified as non-small cell lung cancer (NSCLC).

METHODS

NSCLC transcriptome data and clinical information were downloaded from the TCGA database and GEO database. Firstly, we analyzed and identified the differentially expressed genes (DEGs) between non-metastasis group and metastasis group of NSCLC in the TCGA database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were consulted to explore the functions of the DEGs. Thereafter, univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic metastasis-related signature, followed by the construction of the risk score model and nomogram for predicting the survival of NSCLC patients. GSEA analyzed that differentially expressed gene-related signaling pathways in the high-risk group and the low-risk group. The survival of NSCLC patients was analyzed by the Kaplan-Meier method. ROC curve was plotted to evaluate the accuracy of the model. Finally, the GEO database was further applied to verify the metastasis‑related prognostic signature.

RESULTS

In total, 2058 DEGs were identified. GO functions and KEGG pathways analysis results showed that the DEGs mainly concentrated in epidermis development, skin development, and the pathway of Neuro active ligand -receptor interaction in cancer. A six-gene metastasis-related risk signature including C1QL2, FLNC, LUZP2, PRSS3, SPIC, and GRAMD1B was constructed to predict the overall survival of NSCLC patients. The reliability of the gene signature was verified in GSE13213. The NSCLC patients were grouped into low-risk and high-risk groups based on the median value of risk scores. And low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for NSCLC.

CONCLUSION

Our study identified 6 metastasis biomarkers in the NSCLC. The biomarkers may contribute to individual risk estimation, survival prognosis.

N7-Methylguanosine Genes Related Prognostic Biomarker in Hepatocellular Carcinoma

Background: About 90% of liver cancer-related deaths are caused by hepatocellular carcinoma (HCC). N7-methylguanosine (m7G) modification is associated with the biological process and regulation of various diseases. To the best of our knowledge, its role in the pathogenesis and prognosis of HCC has not been thoroughly investigated.

Aim: To identify N7-methylguanosine (m7G) related prognostic biomarkers in HCC. Furthermore, we also studied the association of m7G–related prognostic gene signature with immune infiltration in HCC.

Methods: The TCGA datasets were used as a training and GEO dataset “GSE76427” for validation of the results. Statistical analyses were performed using the R statistical software version 4.1.2.

Results: Functional enrichment analysis identified some pathogenesis related to HCC. We identified 3 m7G-related genes (CDK1, ANO1, and PDGFRA) as prognostic biomarkers for HCC. A risk score was calculated from these 3 prognostic m7G-related genes which showed the high-risk group had a significantly poorer prognosis than the low-risk group in both training and validation datasets. The 3- and 5-years overall survival was predicted better with the risk score than the ideal model in the entire cohort in the predictive nomogram. Furthermore, immune checkpoint genes like CTLA4, HAVCR2, LAG3, and TIGT were expressed significantly higher in the high-risk group and the chemotherapy sensitivity analysis showed that the high-risk groups were responsive to sorafenib treatment.

Conclusion: These 3 m7G genes related signature model can be used as prognostic biomarkers in HCC and a guide for immunotherapy and chemotherapy response. Future clinical study on this biomarker model is required to verify its clinical implications.

Systematic Analysis of an Invasion-Related 3-Gene Signature and Its Validation as a Prognostic Model for Pancreatic Cancer

Background

Pancreatic adenocarcinoma (PAAD) is a malignant tumor of the digestive system that is associated with a poor prognosis in patients owing to its rapid progression and high invasiveness.

Methods

Ninety-seven invasive-related genes obtained from the CancerSEA database were clustered to obtain the molecular subtype of pancreatic cancer based on the RNA-sequencing (RNA-seq) data of The Cancer Genome Atlas (TCGA). The differentially expressed genes (DEGs) between subtypes were obtained using the limma package in R, and the multi-gene risk model based on DEGs was constructed by Lasso regression analysis. Independent datasets GSE57495 and GSE62452 were used to validate the prognostic value of the risk model. To further explore the expression of the hub genes, immunohistochemistry was performed on PAAD tissues obtained from a large cohort.

Results

The TCGA-PAAD samples were divided into two subtypes based on the expression of the invasion-related genes: C1 and C2. Most genes were overexpressed in the C1 subtype. The C1 subtype was mainly enriched in tumor-related signaling pathways, and the prognosis of patients with the C1 subtype was significantly worse than those with the C2 subtype. A 3-gene signature consisting of LY6D, BCAT1, and ITGB6 based on 538 DEGs between both subtypes serves as a stable prognostic marker in patients with pancreatic cancer across multiple cohorts. LY6D, BCAT1, and ITGB6 were over-expressed in 120 PAAD samples compared to normal samples.

Conclusions

The constructed 3-gene signature can be used as a molecular marker to assess the prognostic risk in patients with PAAD.

Common and differential features of liver and pancreatic cancers: molecular mechanism approach

AIM

The aim of this study was to introduce biomarkers commonly involved in pancreatic cancer metastasis to the liver.

BACKGROUND

The liver is affected by metastatic disease in pancreatic cancer.

METHODS

Two cancer biomarkers were distinguished through a STRING database protein query. The dysregulated proteins of the two cancers were included in 2 networks drawn by Cytoscape software v 3.2.7. 20 top nodes and achieved by the Network analyzer application of Cytoscape based on degree value. The common hub nodes were determined, and action maps were analyzed.

RESULTS

Among 20 hubs of each studied cancer, 18 common hub nodes (90% of hubs) were identified and screened by action maps. Four proteins, AKT1, CDKN2A, ERBB2, and IL6, were identified as common central proteins related to the two studied diseases.

CONCLUSION

AKT1, CDKN2A, ERBB2, and IL6 are common protein core of liver and pancreatic cancers, while STAT3, CASP3, NOTCH1, and CTNNB1 are possible differential proteins to discriminate these cancers.