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. 2022 Nov 3;22(1):334.
doi: 10.1186/s12935-022-02737-1.

DNA methylome in pancreatic cancer identified novel promoter hyper-methylation in NPY and FAIM2 genes associated with poor prognosis in Indian patient cohort

Ankita Chatterjee #  1 , Akash Bararia #  2 , Debopriyo Ganguly  1 , Pronoy Kanti Mondal  2 , Paromita Roy  3 , Sudeep Banerjee  3 , Shibajyoti Ghosh  4 , Sumit Gulati  5 , Supriyo Ghatak  5 , Bitan Kumar Chattopadhay  6 , Priyadarshi Basu  1 , Aniruddha Chatterjee  7 , Nilabja Sikdar  8
Affiliations

DNA methylome in pancreatic cancer identified novel promoter hyper-methylation in NPY and FAIM2 genes associated with poor prognosis in Indian patient cohort

Ankita Chatterjee et al. Cancer Cell Int. .

Abstract

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading cancers worldwide and has a poor survival, with a 5-year survival rate of only 8.5%. In this study we investigated altered DNA methylation associated with PDAC severity and prognosis.

Methods: Methylome data, generated using 450 K bead array, was compared between paired PDAC and normal samples in the TCGA cohort (n = 9) and our Indian cohort (n = 7). The total Indian Cohort (n = 75) was split into cohort 1 (n = 7), cohort 2 (n = 22), cohort 3 (n = 26) and cohort 4 (n = 20).Validation of differential methylation (6 selected CpG loci) and associated gene expression for differentially methylated genes (10 selected gDMs) were carried out in separate validation cohorts, using MSP, RT-PCR and IHC correlations between methylation and gene expression were observed in TCGA, GTEx cohorts and in validation cohorts. Kaplan-Meier survival analysis was done to study differential prognosis, during 2-5 years of follow-up.

Results: We identified 156 DMPs, mapped to 91 genes (gDMs), in PDAC; 68 (43.5%) DMPs were found to be differentially methylated both in TCGA cohort and our cohort, with significant concordance at hypo- and hyper-methylated loci. Enrichments of "regulation of ion transport", "Interferon alpha/beta signalling", "morphogenesis and development" and "transcriptional dysregulation" pathways were observed among 91 gDMs. Hyper-methylation of NPY and FAIM2 genes with down-regulated expression in PDAC, were significantly associated with poor prognosis in the Indian patient cohort.

Conclusions: Ethnic variations among populations may determine the altered epigenetic landscape in the PDAC patients of the Indian cohort. Our study identified novel differentially methylated genes (mainly NPY and FAIM2) and also validated the previously identified differentially methylated CpG sites associated with PDAC cancer patient's survival. Comparative analysis of our data with TCGA and CPTAC cohorts showed that both NPY and FAIM2 hyper-methylation and down-regulations can be novel epigenetically regulated genes in the Indian patient population, statistically significantly associated with poor survival and advanced tumour stages.

Keywords: 450K DNA methylation; Epigenetically Dysregulated Signalling pathways; NPY and FAIM2hyper-methylation; Pancreatic-ductal adenocarcinoma; Poor survival; Prognostic epigenetic marker.

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Conflict of interest statement

The authors don’t have any competing interest to disclose.

Figures

Fig. 1
Fig. 1
A Hierarchical clustering of the samples based on the top-ranked DMPs (n = 156): Illustrative heat map denoting the Hierarchical Clustering of the PDAC samples (n = 7) based on the top-ranked DMPs (n = 156). Blue to red denoted increase in beta value (hyper-methylation). The cancer samples were marked as Blue and the normal samples were marked as Black (B) Genomic annotations of DMPs-CpG islands (Islands, Shores (± 2 KB from the boundaries of the islands), Shelves (± 2 KB from the boundaries of the shores) and Open Sea) or the transcription start site (5ʹ UTR, Exon 1, Promoter, Body, Non-genic). Distribution of the hypo-methylated and hyper-methylated DMPs across different segments of the genome. C Venn diagram showing common and uncommon DMPs in the TCGA cohort and Indian cohort. Among the 7832 DMPs, 68 DMPs were common to our study finding. D Correlation plot between the delta beta values at these 68 DMPs in TCGA data (for 9 PAAD patients) and in Indian cohort (n = 7)
Fig. 2
Fig. 2
Changes of methylation with increasing severity of cancer phenotypes in the TCGA cohort. A Changes in methylation at the hypo-methylated DMPs from Stage I to Stage IV of cancer in the TCGA cohort. B Changes in methylation at the hyper-methylated DMPs from mild to severe stages of cancer in the TCGA cohort. C Higher expression of hypo-methylated gDMs observed from Stage I to Stage IV cancer samples. D Changes in expression of hyper-methylated gDMs across stages of TCGA cohort. E Survival plots showing differences in disease prognosis among the TCGA cohort patients with high and low expressions of the gDMs
Fig. 3
Fig. 3
Agarose gel electrophoresis of methylation specific PCR products: In all subfigures first lane represents 100 bp ladder, next two lane methylated tumour and normal PCR products and proceeding two lanes are unmethylated tumour and normal PCR products. A Representative agarose gel images for the methylation status of pathway enriched loci namely KCNA6 (M:145 bp,U:147 bp), RASSF1 (M:193 bp,U:197 bp)and SIGIRR (M:192 bp,U:192 bp). B Representative agarose gel images for the differential promoter methylation status of FAIM2(M:278 bp,U:286 bp), NPY(M:264 bp,U:266 bp) and FOXE1(M:275 bp,U:277 bp). For each subset the same patient’s tumour and normal has been used. C Percentage of methylation in cancer and normal samples was shown in bar graph. Triple independent validation of the respective methylation status of above genes across all 22 (Adjacent tumour and normal) paired samples were done by Agarose gel electrophoresis of methylation specific PCR products. “M” represents methylated PCR product; “U” represents, unmethylated PCR products; “N” represents Adjacent Normal Sample; “T” represents Tumour sample
Fig. 4
Fig. 4
Validation of differential expressions of gDMs in validation cohort 3 (n = 26). A Hypo-methylated gDMs. B Hyper-methylated gDMs. C gDMs with broad promoter methylation
Fig. 5
Fig. 5
Functional annotations of gDMs. A Enrichment of biological processes among the 91 gDMs. B and C Network analysis with Metascape, where each enriched biological processwas coloured distinctly.Each term in the network is represented by a circle node, where its size is proportional to the number of input genes fall into that term, and its colour represent its cluster identity (i.e., nodes of the same color belong to the same cluster). Terms with a similarity score > 0.3 are linked by an edge (the thickness of the edge represents the similarity score). D MCODE networks showing interconnection between proteins. E Enrichment of transcription factors, known to regulate subsets of gDMs
Fig. 6
Fig. 6
Differential methylation and expressions of NPY and FAIM2 in the TCGA cohort. A Differential survival of patients in the TCGA cohort with “high” and “low” expressions of NPY and FAIM2. B Correlations between beta value and gene expression of NPY and FAIM2, among the cancer samples of the TCGA cohort. C Differences in expressions of NPY and FAIM2 genes between cancer and normal samples of the TCGA cohort. D Correlation between the expressions of FAIM2 and NPY genes in the TCGA cohort cancer samples. E Expression levels of FAIM2 and NPY genes across stages of cancer
Fig. 7
Fig. 7
Representative images of immunohistochemistry (IHC) showing expressions of Faim2 and Npy proteins in PDAC and paired control tissues (n = 20): Antibody specific to Npy and Faim2, were used for comparison and images were clicked at both 20× and 40×. Every Slide consisted of 3 independent sections for IHC. The representation is expression of Npy in normal tissues (A) and in cancer (B). Expression of Faim2 in normal (C) and in cancer (D). A and C took more browner spotsin normal cells than (B) and (D) of the proteins in compared to cancer tissues. E and F H&E section of both tumour and normal were taken at similar resolutions. The bigger figures were taken in 20× and the sub-figures in smaller boxes were taken in 40×. Every sample had 3 sections on a slide with similar results to avoid technical biasness
Fig. 8
Fig. 8
A Differential protein expressions of NPY and FAIM2 in the cancer samples compared to control samples in the TCGA cohort. B–D Overall survival analysis showing the difference in disease prognosis among patients in the validation cohort 3, with different levels of expressions of NPY and FAIM2
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