A multiOmics approach to identify altered ion channels across breast cancer subtypes

A multiOmics approach to identify altered ion channels across breast cancer subtypes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A multiOmics approach to identify altered ion channels across breast cancer subtypes K. T. Shreya Parthasarathi, Jyoti Sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7526346/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Nov, 2025 Read the published version in In Silico Research in Biomedicine → Version 1 posted You are reading this latest preprint version Abstract A multiOmics approach unifies patient-specific datasets to deepen insights into molecular aspects of cancer. Breast cancer cells often exhibit membrane potential deregulation driven by alterations in ion channel activity and distribution. Deregulation of ion channels could result in chemo-resistance, proliferation stimulation and tumor growth maintenance. Here, differentially expressed ion channels (DEICs), differentially methylated regions (DMRs) associated with those ion channels and, copy number alterations in the DEICs and their associated DMRs were identified using publicly available transcriptomic, methylomic and genomic datasets of patients with breast cancer subtypes. The expression of DEICs was further compared using cell line expression profiles available from the DepMap project. Additionally, prognostically significant ion channels were identified using Kaplan-Meier survival plots. 79 ion channels including 22, 9, 9, 20 and 19 were differentially expressed in luminalA, luminalB, HER2, basal and normal-like subtypes, respectively. Of those, 27 ion channels including 10, 6, 4, 5 and 2 were associated with 161 differentially methylated enhancers and promoters in luminalA, luminalB, HER2, basal and normal-like subtypes, respectively. Several patients exhibited amplifications and deletions affecting the 27 ion channels and their associated DMRs. 9 ion channels indicated a positive correlation of expression alterations in cell lines expression profiles. ANO6 , SLC10A5 , and SLC31A1 in luminalA, KCNH4 in HER2-enriched and GRINA in basal-like were associated with survival in patients with subtypes of breast cancer. Most likely, the study marks the first step towards establishing oncochannels across breast cancer subtypes and encourages future research to investigate potential ion channels through experimental validation. PAM50 gene signature copy number alteration methylation RNA-Seq subtype classification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Breast cancer, one of the most prevalent cancer types in women worldwide, is a heterogeneous disease condition with distinct molecular and clinical behaviour that involves uncontrolled growth and division of breast cells. Owing to the heterogeneity observed, breast cancer samples can be classified into 5 major subtypes derived from PAM50 classification by Parker et al – lumA, lumB, HER2, basal and normal-like [ 1 ], [ 2 ]. This diversity not only adds on to the complexity in understanding the mechanisms involved, but also poses challenges for the development of clinic relevant strategies useful in terms of diagnosis, prognosis and improvement in patient care. In recent years a wide array of novel treatment strategies evolved that involve targeting specific signaling pathways to allow cell cycle management [ 3 ]. Advancements in Omics-based technologies are transforming the existing drugs into novel cancer therapeutics with an informed mechanistic perspective [ 3 ], [ 4 ], [ 5 ]. Previous studies have revealed that ion channels and transporters are essential regulators of mammary physiology as well as have a role in the initiation and progression of breast cancer [ 6 ], [ 7 ], [ 8 ], [ 9 ]. Recently, our group has identified altered ion channels in tumor and metastatic states of breast cancer highlighting the correlation of multiple co-expressed ion channels with epithelial to mesenchymal transition [ 9 ]. Upregulation of Na⁺/H⁺ exchanger 1 was reported to promote cell proliferation, invasion and migration in breast cancer MDA-MB-231 cell lines [ 10 ], [ 11 ]. Mitochondrial calcium uniporters were identified in promoting cell migration, lung metastasis and invasion. whereas potassium channels were reported to be associated with poor prognosis in breast cancer [ 12 ], [ 13 ]. Mice model-based study indicated a reduction in the metastatic breast cells on treatment of pharmacological inhibitors of store operated calcium channels [ 14 ]. Similarly, induced overexpression of the store operated calcium channel Orai1 in T47D breast cancer cells led to chemoresistance and was linked to the consequent downregulation of p53 through activation of PI3K [ 15 ]. The channel TRPM2 was as well implicated in enhancing anti-breast cancer drug cytotoxicity [ 16 ]. Furthermore, Zhang et al have previously demonstrated a correlation between autophagy associated protein LC3 and TRPC5 through the CaMKKβ/AMPKα/mTOR pathway [ 17 ]. The study had suggested the role of TRPC5 as an inducer of autophagy and as a target for the reversal of chemoresistance [ 17 ], [ 18 ]. Similarly, ion channels have been implicated in multiple other tumor types as well, reflecting their potential in tumor pathophysiology [ 19 ], [ 20 ]. Moreover, ion channels are therapeutic targets as they can be easily regulated by low-molecular-weight compounds when expressed on the plasma membrane [ 18 ]. Given the arsenal of ion channel-targeting drugs already in the market for various disorders, there is the possibility of rapid translation for oncologic patient care through drug repurposing strategies [ 18 ]. Investigation of ion channels in breast cancer subtypes and their interactions with the different regulatory molecules could pave way for novel clinical strategies beneficial for patients with breast cancer. Carcinogenesis is a complex process that generally involves molecular alterations at multiple levels including genomics, methylomics, and transcriptomics [ 21 ], [ 22 ]. The technological advancements leading to the availability of data at various omics levels have provided the computational biologists with promising platforms to interpret the complexity in the biological environment [ 22 ]. Taking into account this opportunity several studies have come up with various methods to classify the different subtypes of breast cancer by including multiOmics datasets [ 23 ], [ 24 ], [ 25 ], [ 26 ], [ 27 ], [ 28 ], [ 29 ], [ 30 ]. Additionally, there are also studies that have investigated the subtype specific factors affecting immune activity and the cross-talking pathways involved in breast cancer through integration of several omics layers [ 31 ], [ 32 ], [ 22 ]. In this study, publicly available genomic, methylomic and transcriptomic datasets of patients with breast cancer were downloaded from UCSC Xena browser. The DETs, altered methylated probes and the copy number alterations were identified using various computational strategies. Subsequently, the deregulated transcripts corresponding to ion channels in each breast cancer subtype were identified. The possible regulatory molecules of the ion channels were identified based on the outputs from the methylomic and genomic level data analysis. The identified altered ion channels were compared against gene expression profiles of various cell lines corresponding to breast cancer subtypes sourced from the DepMap data portal. Thereafter, the ion channels associated with survival of patients with breast cancer subtypes were identified on the basis of Cox regression analysis. The detection of significant ion channels in breast cancer subtypes may contribute towards the strategies leading to the development of novel approaches for management of breast cancer. Identifying the regulatory aspects of ion channels provides insights on the role of ion channels in tumor growth and progression and require further experimental validation. 2. Materials and Methods The workflow of the study is depicted in Fig. 1 . 2.1 Data Collection A list of 493 ion channels was curated from the HGNC database. MultiOmics datasets including data at the transcriptomic, methylomic and genomic levels corresponding to TCGA-GDC BRCA patient cohort were downloaded. RNA-Seq gene expression profiles with log2(read count + 1) values from Illumina platform representing the transcriptomic level, copy number alterations representing the genomic level data determined using GISTIC 2 and beta values from HM450 representing the methylomic level data were downloaded from UCSC Xena browser. The transcriptomic data and the methylomic data also included gene expression profiles and beta values from adjacent normal samples. The details on PAM50 subtypes corresponding to each tumor sample were extracted from the Additional data provided by Netanely et al [ 33 ]. 2.2 Data Preprocessing The overlapping samples across the three omics layers were pooled out. Each omic layer data was represented as a two-dimensional matrix with rows representing sample ids, columns representing transcript ids/probe ids/gene ids and cells representing the corresponding Omics layer values. Further, the column with the molecular subtype was included in the two-dimensional matrix and the subtypes were encoded into numerical forms for ease in data preprocessing (Additional Table 1 ). Table 1 Total number of samples across breast cancer subtypes. Total samples Luminal A samples Luminal B samples HER2-enriched samples Basal samples Normal-like samples 701 378 131 40 122 30 2.3 Identification of differentially expressed transcripts in subtypes of breast cancer R (v.4.1.3) Bioconductor package DESeq2 (v.1.34.0) was used to identify the subtype-wise differentially expressed transcripts [ 34 ]. The RNASeq log2(read count + 1) data matrix was transformed to raw reads. The upregulated and downregulated transcripts from each subtype with respect to adjacent normal samples were identified using an adjusted p-value cut-off of 0.05 and log2FC of |0.6|. The transcripts found differentially expressed exclusively across subtypes were procured. The ion channels exclusive to a particular subtype were extracted from the deregulated list of transcripts. For visualization in terms of a heatmap, the DESeq2 normalised read count matrix was converted to z-score matrix. 2.4 Identification of differentially methylated probes in subtypes of breast cancer The methylomic data matrix was filtered to exclude the probes with values in all samples as NaNs. ChAMP package (v.2.21.1) from R (v.4.4.1) Bioconductor was used to identify the subtype-wise differentially methylated probes [ 35 ]. The non CpG probes, probes identified as single nucleotide polymorphisms, multi-hit probes and probes located on XY chromosomes were filtered out. The data was further normalized using the BMIQ dilution algorithm. Thereafter, the hypermethylated and hypomethylated probes with respect to adjacent normal samples were identified using a p-value threshold of 0.05 and logFC of |0.2|. 2.5 Identification of occurrence of gene amplifications and deletions in subtypes of breast cancer Customized Python scripts were used to count the number of samples belonging to a subtype of breast cancer exhibiting gene amplifications or deletions in a particular gene using the genomic data matrix. 2.6 Identification of the association of the methylomic and genomic level data with the ion channels deregulated at transcriptomic level A list of enhancers and promoters were downloaded from the GeneHancer (v5.20) data resource. R Bioconductor packages GenomicRanges [ 36 ] and rtracklayer [ 37 ] were used to get the overlaps between the GeneHancer regulatory elements and the Illumina HM450 probes. Thereafter, the enhancers and promoters linked to the deregulated ion channels were extracted using customized Python scripts. Subsequently, the methylation status of those enhancers and promoters was examined using the output obtained from the analysis mentioned in section 2.4 . The genomic level status of the transcriptionally deregulated ion channels and their associated differentially methylated probes were inspected using the output obtained from the analysis mentioned in section 2.5 . 2.7 Comparison of ion channels deregulated at transcriptomic level with subtype-specific cell line expression profiles CCLE cell lines corresponding to luminal, HER2 amplified and basal subtypes of breast cancer along with non-cancerous cell line expression profiles available through DepMap project were downloaded using R Bioconductor depmap library [ 38 ]. The alteration patterns in expression in the cell lines with the non-cancerous cell lines for the differentially expressed ion channels were observed and visualized using boxplots. 2.8 Survival analysis of identified altered ion channels in breast cancer subtypes The clinical data corresponding to the vital status of the patients with each subtype of breast cancer was obtained using the R Bioconductor “RTCGA” package [ 39 ]. The correlation between gene expression data and 5-year survival rate of patients with each subtype was determined based on Cox proportional hazard regression analysis performed using the R Bioconductor “survival” package [ 40 ]. The log rank p-values and HR with a confidence interval of 95% was noted. The difference between high and low expressions with group cut-off criteria set to median value for the potential ion channels was visualized by generating KM survival plots using the R Bioconductor “survminer” package [ 41 ]. 3. Results 3.1 Collection of data and pre-processing RNA-Seq Illumina based log2(count + 1) values from 1,055 tumor samples and 162 adjacent normal samples were downloaded as transcriptomic dataset. HM450 BeadChip array beta values for genes in 793 tumor samples and 97 adjacent normal samples were downloaded as methylomic data. Putative copy number calls on 1,104 samples of patients with breast cancer determined using GISTIC 2.0 algorithm consisting of values for deletion as -1, neutral/no change as 0 and amplification as + 1 were downloaded as genomic data. 779 tumor samples overlapped across the three omics layers with 701 samples consisting of information related to PAM50 subtype (Table 1 ). The transcriptomic data matrix consisted of htseq-counts from 60,843 transcripts, methylomic data matrix had beta values for 4,85,577 probes and the genomic data matrix consisted of amplification and deletion values for 19,730 genes. 3.2 Gene expression patterns in ion channels across breast cancer subtypes DETs were identified in the five subtypes of breast cancer with 13,650 in lumA, 13,230 in lumB, 12,831 in HER2, 13,552 in basal and 8,205 in normal-like subtypes (Additional Fig. 1 ). Amidst the DETs, 22, 9, 9, 20 and 19 ion channels were found exclusively in lumA, lumB, HER2, basal and normal-like subtypes respectively (Fig. 2 , Additional table 2). 3.3 Methylation patterns across breast cancer subtypes DMPs were identified in the five subtypes of breast cancer, with 36,313 in lumA, 55,958 in lumB, 39,319 in HER2, 25,316 in basal and 2,292 in normal-like samples (Additional Fig. 2 ). 4,17,870 regulatory elements including 3,80,634 enhancers, 7,078 promoters and 30,158 enhancers/promoters were obtained from GeneHancer. 1,21,602 HM450 methylation probes overlapped with 40,780 GeneHancer elements including 32,262 enhancers, 332 promoters and 8,186 enhancer/promoters. The enhancers and promoters associated with the deregulated ion channel transcripts were identified from GeneHancer elements list that overlapped with the methylation probes. 34 enhancers and promoters corresponding to 58 methylation probes were associated with 10 deregulated ion channels from lumA subtype. 27 enhancers and promoters corresponding to 46 probes were associated with 6 deregulated ion channels from lumB subtype. Similarly, in the HER2 subtype, 7 enhancers and promoters linked to 17 methylation probes corresponded to 4 ion channels and in the basal subtype 18 enhancers and promoters mapping to 38 probes corresponded to 5 ion channels. In the normal-like subtype, only 2 enhancers and promoters that corresponded to 2 methylation probes were associated with 2 ion channels (Fig. 3 , 4 ). The distribution and location of the DMPs associated with deregulated ion channels in relation to the genomic features and CpG islands are depicted in Fig. 3 A and 3 B. In each subtype, most DMPs associated with the differentially expressed ion channels were present in the gene body and in the intergenic regions followed by 200–1500 base pairs upstream of TSS1500. Additionally, DMPs were also located in the region upto 200 base pairs from the TSS200, 5’ UTR, 3’UTR and in the 1st exon. In terms of DMP location in relation to CpG islands, the largest number of DMPs from lumA, lumB, HER2 and basal-like mapped to open-sea (the region beyond 4kb from CpG island) region followed by CpG islands, shore region (2kb from CpG island) and the shelf region (2–4 kb from CpG island). Two DMPs were associated with the deregulated ion channels from the normal-like subtype mapped to the CpG island region and open-sea region. Majority of DMPs were observed to be hypermethylated compared to the number of hypomethylated DMPs across subtypes (Fig. 3 C). No overlaps were identified between the DMPs across subtypes associated with subtype exclusive ion channels. 3.4 Copy number alterations across subtypes In lumA subtype, the ion channels and its associated enhancers/promoters located at chromosomes 1 and 11 were most frequently affected by CNAs followed by chromosomes 12, 13, 16, 17, 3, 8, 7 ,6 ,20, 22, 2, 19, 4, 9 and X. The ion channels SCNN1D (36/378, 9.5%), P2RX7 (26/378, 6.8%), SLC4A1 (20/378, 5.2%) and TRPV2 (12/378, 3.1%) were found to be deleted more often than amplified whereas the ion channels CATSPER1 (43/378, 11.3%), ITPR3 (12/378, 3.1%) and KCTD5 (20/378, 5.2%) were found to be amplified more often than deleted in lumA patients. The other ion channels CLIC5 , ANO6 , SLC12A8 and SLC10A6 were observed to have more or less equal number patients with deletions and amplifications in them. The enhancers/promoters PUSL1 (36/378, 9.5%) associated to SCNN1D , PTRF (21/378, 5.5%) associated with SLC4A1 and CENPV (12/378, 3.17%) associated with TRPV2 were found to be deleted more often than amplified in lumA patients. LTBP3 (42/378, 11.1%) and OVOL1 (42/378, 11.1%) associated with CATSPER1 and CLUAP1 (18/378, 4.7%) associated with KCTD5 were found to be amplified more frequently. Similarly, in lumB subtype, the ion channels and associated enhancers/promoters from chromosomes 17 and 1 were most frequently affected followed by chromosomes 9, 12, 5, 10, 16, 3, 6 and X. The ion channels found to be deleted in most of the lumB patients were SLC9A1 (25/131, 19%) and ANO2 (9/131, 6.8%) whereas CACNG3 (7/131, 5.3%), SLC9A3 (12/131, 9.1%) and SLC16A9 (9/131, 6.8%) were found to be mostly amplified as compared to deleted in cases with lumB subtype. The enhancers/promoters SYTL1 (25/131, 19%) and WASF2 (25/131, 19%) associated with SLC9A1 , CD9 (9/131, 6.8%) and VWF (9/131, 6.8%) associated with ANO2 were observed to be mostly deleted. In terms of HER2 subtype the ion channels and enhancer/promoters from chromosomes 17 was observed to be most frequently affected followed by the chromosomes 2, 11, 6, 15, 12 and 16. Deletions in the ion channels KCNH4 (10/40, 25%) and ANO7 (8/40, 20%) were present in most of cases with HER2 subtype whereas the other ion channels TRPC6 and SLC24A5 were observed to have comparative number of cases with amplifications and deletions. KRT222 (20/40, 50%) associated with KCNH4 had an equal number of cases with deletions and amplifications. Basal subtype showed the ion channels and enhancer/promoters on chromosomes 1, 8, 12, 15 and 17 to be affected most frequently by CNAs followed by the chromosomes 2, 11, 20, 4, 5, 14 and 22. The ion channels TRPM7 was found to be deleted in 22.1% (27/122) of cases with basal breast cancer subtype and the ion channels TMCO1, SLC26A11 , CACNA1G , KCNA5 and GRINA were found amplified in 32.23% (39/122), 36% (44/122), 8.1% (10/122), 27.8% (34/122) and 29.5% (36/122) respectively. NTF3 (32/122, 26.2%) associated with KCNA5 was found to be most frequently amplified. In normal-like subtype the ion channels and associated enhancer/promoters found to be most frequently affected by CNAs were present in the chromosomes 7, 1, 12 and 19 followed by X, 17, 10, 16, 3, 4 and 6. The ion channels SLC41A2 , CATSPERD , CLCNKB and CATSPER4 were found to be deleted in 6.6% (2/30) of the normal-like breast cancer cases in the study whereas SLC13A4 was observed to be amplified in 10% (3/30) of cases with the normal-like subtype. Individual gene amplifications and deletions are represented in Fig. 5 , 6 and Additional Fig. 3 . 3.5 Alterations in ion channels using cell line expression profiles Expression profiles for fourteen luminal cell lines, thirteen HER2 amplified cell lines, two basal-like cell lines and one non-cancerous cell line were obtained from DepMap project. The altered ion channels CATSPER1 , ITPR3 , TRPC4 , P2RX7 and TRPV2 from luminalA subtype, KCNJ14 from luminalB subtype and GJC2 , SLC8A3 from basal-like subtype showed positive correlation in terms of alterations in expression comparison with cell line expression alterations (Additional Fig. 4 ). 3.5 Association of potential ion channels with survival of patients from different subtypes The ion channels with log rank p -value and p (HR) < 0.1 were selected as suggestive ion channels in survival of patients with different subtypes of breast cancer. Among the ion channels identified as subtype exclusive ion channels, six were found to be prognostically suggestive in the different subtypes. Three ion channels from lumA subtype – ANO6 (HR:1.69, p(HR):0.04), SLC10A5 (HR:2.27, p(HR):0.0001), SLC31A1 (HR:1.69, p(HR):0.05), one from HER2 subtype – KCNH4 (HR:0.28, p(HR):0.02), one from basal subtype – GRINA (HR:2.28, p(HR): 0.03) and one from normal-like subtype – SLC41A3 (HR:0.04, p(HR):0.1) were identified. In lumA, patients with low expression of ANO6 (p-value:0.01), SLC10A5 (p-value:0.002) and SLC31A1 (p-value:0.01) were observed to have better survival than the patients with higher expression. Similarly, in basal subtype patients with low expression of GRINA (p-value:0.07) were observed to have better survival. In HER2 and normal-like subtype the patients with high expression of KCNH4 (p-value:0.1 ) and SLC41A3 (p-value:0.05) respectively were observed to have better survival than patients with lower expression suggesting that higher expression of these ion channels in the respective subtypes could lower the hazard ratio (Fig. 7 ). 4. Discussion Breast cancer subtypes present unique molecular characteristics and is one of the most common malignant tumors in women, with a high mortality rate globally making it a condition that requires attention. Aberrant channels can disrupt normal cellular activities, transforming them into malignant cells [ 42 ]. Although literature documents the deregulation of ion channels in breast cancer, finding effective targets remains a challenge. In this study an analysis pipeline integrating gene expressions, methylation patterns and copy number alterations was utilized to identify the ion channels in breast cancer subtypes. The analysis allowed the identification of ion channels deregulated in the molecular subtypes with details on the methylation status of its regulatory elements further relating to the alterations associated at the genomic level (Additional Fig. 5 ). Identified methylated regions regulating ion channels included both hyper and hypomethylation, challenging the norm that DNA methylation suppresses gene expression. 22 ion channels were deregulated exclusively in lumA subtype in the present study - GLRA4, SLC16A7, SLC12A8, CLCN2, ITPR3, CLIC5, KCTD5, SLC4A1, TRPC4, SLC10A5, KCTD17, CATSPER1, KCTD15, SLC31A1, SCN11A, SCN9A, P2RX7, TRPV2, SCNN1D, CLCN1, ANO6 and SLC10A6 . Of these, SLC12A8, CLIC5, KCTD5, TRPV2 and ANO6 were reported previously in breast cancer [ 43 ], [ 44 ], [ 45 ], [ 46 ], [ 47 ]. In the present study, SLC12A8 was downregulated. Previously SLC12A8 was reported as deregulated in breast cancer and its role in toll-like receptor/nod-like receptor (TLR/NLR) signaling pathway and peroxisome proliferator-activated receptors (PPAR) γ signaling pathway were discussed [ 43 ]. CLIC5 was upregulated and its associated enhancers/promoters were deregulated. CLIC5 was previously reported in prognosis and in immune/inflammatory chemokine regulation [ 44 ]. KCTD5 was downregulated whereas its associated enhancers and promoters were hypomethylated. Increased expression of KCTD5 was previously studied in breast cancer cell lines [ 45 ]. The hypermethylation of the enhancer CENPV at multiple genomic locations was identified to be associated with the downregulated ion channel TRPV2 consisting of 16 patients with amplifications and deletions. The upregulation of ANO6 in lumA subtype had dysregulations of methylation in 3 enhancers/promoters from the intergenic regions. ANO6 was reported to have a role in prognosis of patients with breast cancer [ 47 ]. 9 ion channels exclusively deregulated in lumB subtype were PKD2L2, KCNJ14, ANO2, CACNG3, SLC9A1, SLC9A3, KCNJ8, SLC22A7 and SLC16A9. SLC9A1 was reported deregulated in previous studies. Upregulated SLC9A1 was associated with 2 deregulated enhancers/promoters. SLC9A1 has role in development of cancer, as a mediator of an increased extrusion of acid in tumor cells and was reported a potential target for treatment [ 48 ]. 9 ion channels – KCNG4, CNGA4, KCNH4, AQP8, GABRR2, TRPC6, ANO7, ASIC1 and SLC24A5 were identified as deregulated exclusively in HER2. AQP8 was upregulated. Elevated mRNA expression of AQP8 was predicted to have better relapse-free survival in all breast cancer patients [ 49 ]. TRPC6 was upregulated and was hypomethylated at genomic location chr11-101323578. Several studies have reported TRPC6 as overexpressed in cell lines and in biopsy tissues. Further its role in cell proliferation, migration and invasion were reported [ 50 ], [ 51 ] 20 ion channels – KCNN2, SLC9A8, SLC16A14, CACNA1I, GRINA, SLC30A9, SLC22A9, KCNA5, CACNA1G, SLC26A11, SLC10A7, GLRA1, SLC20A1, GJC2, KCNJ13, TMCO1, TRPM7, TRPM5, SLC8A3 and KCNK2 were identified to be deregulated in basal subtype from the current study. CACNA1I was upregulated and CACNA1G was downregulated with all its associated enhancers/promoters majorly including HOXB13 to be hypermethylated. A study reported patients with metastatic state of tumor had higher levels CACNA1I and lower levels of CACNA1G indicating it as a tumor suppressor [ 52 ]. KCNA5 was downregulated. KCNA5 interacts with caveolin and further aids in early transformation and proliferation of mammary cells [ 53 ]. SLC20A1 was upregulated. Higher level of SLC20A1 in ER + individuals was associated with 10-year survival along with lower expression of KMT2C gene [ 54 ]. GJC2 was identified as downregulated. It was previously reported to cause lymphedema in patients with breast cancer [ 55 ]. TRPM7 was found to be upregulated. It was reported to regulate EGF-induced STAT3 phosphorylation and EMT marker vimentin expression [ 51 ]. 19 ion channels – KCNJ12, GJB1, GRIN2C, GABRR1, CLCNKB, TRPC5, GJD3, MIP, SLC9A5, CACNA1S, CATSPERD, SLC26A26, KCNH3, SLC41A2, CATSPER4, SLC13A4, SLC41A3, GRIK5 and KCNAB3 were identified exclusively deregulated in normal-like subtype. GJB1 was upregulated in patients with normal-like breast cancer and was also significantly overexpressed in breast cancer stem cells [ 56 ]. TRPC5 was upregulated. Exosomal TRPC5 was linked to chemotherapy resistance in breast cancer patients [ 57 ]. The study further laid down suggestive ion channels in terms of association with patient survival attributed to a subtype. However, number of patients in each subtype of breast cancer were limited to attain a statistical significance of 0.05, thus, 0.1 was taken to allow detection of meaningful trends that warrant further investigation. It is evident that multiple studies have been carried out to understand the role of ion channels in breast cancer. However, altered ion channels across breast cancer subtypes were not known. Here, we identified potential ion channels altered at multiple omics layers across breast cancer subtypes. Overall, the study underlines the importance of studying the ion channels at a multiOmics level to assess the downstream regulatory effects in the different subtypes of breast cancer and further requires experimental validation. Abbreviations LumA – LuminalA LumB – LuminalB HGNC – Human Genome Organization Gene Nomenclature Committee UCSC – University of California, Santa Cruz GDC – Genomic Data Commons TCGA – The Cancer Genome Atlas CCLE - Cancer Cell Line Encyclopedia BRCA – Breast cancer ChAMP – Chip Analysis Methylation Pipeline BMIQ – Beta MIxture Quantile HR – Hazard ratios KM – Kaplan-Meier DET – Differentially expressed transcripts DMP – Differentially methylated probes CNA – Copy number alterations FC – Fold change HM450 - Illumina's Infinium HumanMethylation450 GISTIC – Genomic Identification of Significant Targets in Cancer TSS – Transcript start site UTR – Untranslated region Declarations Acknowledgements We would like to thank the authors of the manuscripts for making the datasets used in this study publicly available. We thank Sanjib Chaudhary for helpful discussions. JS would like to thank Council of Scientific and Industrial Research (CSIR), Government of India [37WS(0114)/2023-24/EMR-II/ASPIRE] and Indian Council of Medical Research (ICMR), Government of India for the research support. KTSP was supported by ICMR, Government of India [BMI/12(95)2021]. JS was a recipient of the Bio-CARe Women Scientists award from the Department of Biotechnology (DBT), Government of India. Funding sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data accessibility The Python and R scripts written in support of this publication is publicly available at our GitHub repository and can be accessed through the following link “https://github.com/js-iob/Breast_cancer_subtypes_multiOmics” Author contributions Conceptualization, JS; methodology, JS, KTSP; investigation, JS and KTSP; writing—original draft, KTSP; writing—review and editing, KTSP and JS; funding acquisition JS; supervision, JS. All authors contributed to the article and approved the submitted version. References Parker JS et al (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27(8):1160–1167 Parker JS et al (2023) Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes. J Clin Oncol 41(26):4192–4199 Barzaman K et al (2020) Breast cancer: Biology, biomarkers, and treatments. Int Immunopharmacol 84:106535 Nagarajan D, McArdle SEB (2018) Immune Landscape of Breast Cancers. Biomedicines, 6(1) Shah AN, Cristofanilli M (2017) The Growing Role of CDK4/6 Inhibitors in Treating Hormone Receptor-Positive Advanced Breast Cancer. Curr Treat Options Oncol 18(1):6 Ma Z et al (2020) Function of ion transporters in maintaining acid-base homeostasis of the mammary gland and the pathophysiological role in breast cancer. Am J Physiol Regul Integr Comp Physiol 318(1):R98–R111 Rao MRMaR (2020) Subtype specific targeting of calcium signaling in breast cancer. ClinicalKey Lu C et al (2020) Pathological role of ion channels and transporters in the development and progression of triple-negative breast cancer. Cancer Cell Int 20:377 Parthasarathi KTS et al (2022) In Silico Analysis of Ion Channels and Their Correlation with Epithelial to Mesenchymal Transition in Breast Cancer. Cancers (Basel), 14(6) Lin Y et al (2011) NHE1 mediates MDA-MB-231 cells invasion through the regulation of MT1-MMP. Exp Cell Res 317(14):2031–2040 Wang J et al (2015) CIAPIN1 targets Na(+)/H(+) exchanger 1 to mediate MDA-MB-231 cells' metastasis through regulation of MMPs via ERK1/2 signaling pathway. Exp Cell Res 333(1):60–72 Yu C et al (2017) Mitochondrial calcium uniporter as a target of microRNA-340 and promoter of metastasis via enhancing the Warburg effect. Oncotarget 8(48):83831–83844 Alvarez-Baron CP et al (2011) The two-pore domain potassium channel KCNK5: induction by estrogen receptor alpha and role in proliferation of breast cancer cells. Mol Endocrinol 25(8):1326–1336 Yang S, Zhang JJ, Huang XY (2009) Orai1 and STIM1 are critical for breast tumor cell migration and metastasis. Cancer Cell 15(2):124–134 Hasna J et al (2018) Orai3 calcium channel and resistance to chemotherapy in breast cancer cells: the p53 connection. Cell Death Differ 25(4):693–707 Koh DW et al (2015) Enhanced cytotoxicity in triple-negative and estrogen receptor–positive breast adenocarcinoma cells due to inhibition of the transient receptor potential melastatin-2 channel. Oncol Rep 34(3):1589–1598 Zhang P et al (2017) TRPC5-induced autophagy promotes drug resistance in breast carcinoma via CaMKKbeta/AMPKalpha/mTOR pathway. Sci Rep 7(1):3158 Altamura C et al (2022) Ion Channel Involvement in Tumor Drug Resistance. J Pers Med, 12(2) Parthasarathi KTS et al (2023) Aberrations in ion channels interacting with lipid metabolism and epithelial-mesenchymal transition in esophageal squamous cell carcinoma. Front Mol Biosci 10:1201459 Sharma J et al (2019) Somatic Mutations Profile of a Young Patient With Metastatic Urothelial Carcinoma Reveals Mutations in Genes Involved in Ion Channels. Front Oncol 9:435 Sharma J et al (2020) Editorial: Multi-Omics Approaches to Study Signaling Pathways. Front Bioeng Biotechnol 8:829 Wang L et al (2014) Integrating multi-omics for uncovering the architecture of cross-talking pathways in breast cancer. PLoS ONE 9(8):e104282 Sheetal Rajpal VK, Agarwal M, Kumar N (2021) Deep Learn Based Model Breast Cancer Subtype Classif arXiv:211103923 Choi JM, Chae H (2023) moBRCA-net: a breast cancer subtype classification framework based on multi-omics attention neural networks. BMC Bioinformatics 24(1):169 Meshoul S, Shaiba BA, AlBinali H (2022) S., Explainable Multi-Class Classification Based on Integrative Feature Selection for Breast Cancer Subtyping. Rohani N, Eslahchi C (2020) Classifying Breast Cancer Molecular Subtypes by Using Deep Clustering Approach. Front Genet 11:553587 Thalor A et al (2022) Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer. Comput Struct Biotechnol J 20:1618–1631 Tao M et al (2019) Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data. Genes (Basel), 10(3) Lin Y et al (2020) Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data. Genes (Basel), 11(8) Huang Y, Zeng P, Zhong C (2024) Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning. BMC Bioinformatics 25(1):132 Ding R et al (2023) Identification of Breast Cancer Subtypes by Integrating Genomic Analysis with the Immune Microenvironment. ACS Omega 8(13):12217–12231 Li X et al (2021) Uncovering the Subtype-Specific Molecular Characteristics of Breast Cancer by Multiomics Analysis of Prognosis-Associated Genes, Driver Genes, Signaling Pathways, and Immune Activity. Front Cell Dev Biol 9:689028 Netanely D et al (2016) Expression and methylation patterns partition luminal-A breast tumors into distinct prognostic subgroups. Breast Cancer Res 18(1):74 Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550 Tian Y et al (2017) ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics 33(24):3982–3984 Lawrence M et al (2013) Software for computing and annotating genomic ranges. PLoS Comput Biol 9(8):e1003118 Lawrence M, Gentleman R, Carey V (2009) rtracklayer: an R package for interfacing with genome browsers. Bioinformatics 25(14):1841–1842 Killian T (2021) Exploiting the DepMap cancer dependency data using the depmap R package. F1000Research W C (2025) RTCGA.mRNA: mRNA datasets from The Cancer Genome Atlas Project. Therneau TM (2024) A Package for Survival Analysis in R. Alboukadel Kassambara MK, Biecek P, Fabian S (2024) survminer: Drawing Survival Curves using 'ggplot2'. Prevarskaya N, Skryma R, Shuba Y (2018) Ion Channels Cancer: Are Cancer Hallm Oncochannelopathies? Physiol Rev 98(2):559–621 Li L et al (2021) Solute carrier family 12 member 8 impacts the biological behaviors of breast carcinoma cells by activating TLR/NLR signaling pathway. Cytotechnology 73(1):23–34 Yau C et al (2010) A multigene predictor of metastatic outcome in early stage hormone receptor-negative and triple-negative breast cancer. Breast Cancer Res 12(5):R85 Rivas J et al (2020) KCTD5, a novel TRPM4-regulatory protein required for cell migration as a new predictor for breast cancer prognosis. FASEB J 34(6):7847–7865 Elbaz M et al (2018) TRPV2 is a novel biomarker and therapeutic target in triple negative breast cancer. Oncotarget 9(71):33459–33470 Wu ZH et al (2021) The role of ferroptosis in breast cancer patients: a comprehensive analysis. Cell Death Discov 7(1):93 Boedtkjer E et al (2013) Contribution of Na+,HCO3(-)-cotransport to cellular pH control in human breast cancer: a role for the breast cancer susceptibility locus NBCn1 (SLC4A7). Int J Cancer, 132(6): pp. 1288-99 Zhu L et al (2019) Significant prognostic values of aquaporin mRNA expression in breast cancer. Cancer Manag Res 11:1503–1515 Jardin I et al (2018) TRPC6 Channels Are Required for Proliferation, Migration and Invasion of Breast Cancer Cell Lines by Modulation of Orai1 and Orai3 Surface Exposure. Cancers (Basel), 10(9) Dhennin-Duthille I et al (2011) High expression of transient receptor potential channels in human breast cancer epithelial cells and tissues: correlation with pathological parameters. Cell Physiol Biochem 28(5):813–822 Ragab Ibrahim FAE et al (2022) Insights on possible interplay between epithelial-mesenchymal transition and T-type voltage gated calcium channels genes in metastatic breast carcinoma. Heliyon 8(8):e10160 Liu J et al (2016) Expression of KCNA5 Protein in Human Mammary Epithelial Cell Line Associated with Caveolin-1. J Membr Biol 249(4):449–457 Sato K, Akimoto K (2017) Expression Levels of KMT2C and SLC20A1 Identified by Information-theoretical Analysis Are Powerful Prognostic Biomarkers in Estrogen Receptor-positive Breast Cancer. Clin Breast Cancer 17(3):e135–e142 Finegold DN et al (2012) Connexin 47 mutations increase risk for secondary lymphedema following breast cancer treatment. Clin Cancer Res 18(8):2382–2390 Zekri AN et al (2022) Genetic profiling of different phenotypic subsets of breast cancer stem cells (BCSCs) in breast cancer patients. Cancer Cell Int 22(1):423 Wang M et al (2018) Effect of exosome biomarkers for diagnosis and prognosis of breast cancer patients. Clin Transl Oncol 20(7):906–911 Additional Declarations The authors declare no competing interests. Supplementary Files ParthasarathiBreastCancerSsubtypesISRBsupplementaryMaterial072125.pdf Supplementary material AdditionalTable2.xlsx Additional Table2: List of ion channels exclusively deregulated across breast cancer subtypes along with their log fold change and p-values Cite Share Download PDF Status: Published Journal Publication published 12 Nov, 2025 Read the published version in In Silico Research in Biomedicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7526346","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509643684,"identity":"61324978-dd6e-4868-a77d-4a8f055d01dd","order_by":0,"name":"K. T. Shreya Parthasarathi","email":"","orcid":"","institution":"Institute of Bioinformatics","correspondingAuthor":false,"prefix":"","firstName":"K.","middleName":"T. Shreya","lastName":"Parthasarathi","suffix":""},{"id":509643685,"identity":"09a02c29-81d3-46c0-965c-4c07d9678dc4","order_by":1,"name":"Jyoti Sharma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYBACxgYgkVAhkcAGYlYgxPBqYWxIOAPVcgYkxEZAC1gXYxtDApgJ0UJAPfOM5OMPHs6zyONjb257cKDiXjT//ObGjz8YbPLlHXBYMSMtsSFxm0QxG8/BdoMDZ4pzZxxjbJbmYUiz3HgAl5YcQ5CWxDYgkv7YlpDbcIyxjZmB4bCBIQ4vQbTMgWiROAjUMh+ohfEHQS0NSFo2ALUw8AC1yOPwPmPPs8QZCcfAfmmTOHAmIXfjsUSgXwzSDAxwaDFsTz7w8UdNXZ58e/sziQMVCbnzDh9/+PFHhY2BPA6HGU5IwCoOtMLgAHYt8vw4JIBSOGwZBaNgFIyCEQcAqIViWWqT2sMAAAAASUVORK5CYII=","orcid":"","institution":"Institute of Bioinformatics","correspondingAuthor":true,"prefix":"","firstName":"Jyoti","middleName":"","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2025-09-03 11:12:55","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7526346/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7526346/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.insi.2025.100113","type":"published","date":"2025-11-13T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90892359,"identity":"b272052a-4320-4dae-90e6-c074d1a4ae50","added_by":"auto","created_at":"2025-09-09 11:19:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":919945,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of workflow to identify ion channels in breast cancer using multiOmics data\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure17.png","url":"https://assets-eu.researchsquare.com/files/rs-7526346/v1/ea4c0f88ccae8058689b26fb.png"},{"id":90891628,"identity":"b6683b4b-31f4-4d70-81df-7fb3837087ba","added_by":"auto","created_at":"2025-09-09 11:11:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2265640,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed ion channels across breast cancer subtypes.\u003c/strong\u003e (A) Venn diagram depicting the number of overall transcripts that overlapped across lumA, lumB, HER2, basal and normal-like subtypes of breast cancer (B) Venn diagram depicting the number of ion channels that overlapped across lumA, lumB, HER2, basal and normal-like subtypes of breast cancer (C) Heatmap depicting Z-scores in the deregulated ion channels across patients with breast cancer subtypes. Upregulation in ion channels is indicated in shades of red. Downregulation in ion channels is indicated in shades of green. The Y-axis consists of the HGNC symbols of deregulated ion channels. X-axis consists of patients with different subtypes of breast cancer. The scale on the right indicates the shades based on Z-score values whereas the scale on the top indicates the groups of different patients based on PAM50 classification.\u003c/p\u003e","description":"","filename":"Figure26.png","url":"https://assets-eu.researchsquare.com/files/rs-7526346/v1/3b921ae76eb4fef64688b854.png"},{"id":90891620,"identity":"38a321ce-5a14-4bf5-943e-a2fdcfa9b607","added_by":"auto","created_at":"2025-09-09 11:11:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2237549,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMethylation pattern of enhancers/promoters associated with deregulated ion channels.\u003c/strong\u003e (A) Distribution of differentially methylated probes (DMPs) associated with ion channels throughout genomic regions (B) Distribution of DMPs associated with ion channels in genomic locations divided into island, opensea, shelf and shore according to the distance between the CpG island and DMPs (C) Total number of hypomethylated and hypermethylated probes identified as associated with deregulated ion channels (D) Distribution of beta values of enhancer/promoters associated with deregulated ion channels across patients with different subtypes of breast cancer\u003c/p\u003e","description":"","filename":"Figure35.png","url":"https://assets-eu.researchsquare.com/files/rs-7526346/v1/23f00e1ad2cbe0fa719286a3.png"},{"id":90892364,"identity":"66f07c10-5fca-498d-be01-8907a474ac7d","added_by":"auto","created_at":"2025-09-09 11:19:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":267722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork depicting the ion channels with their deregulated enhancers and promoters\u003c/strong\u003e. In the network the circles indicate ion channels, squares are the enhancers and diamonds are the enhancers/promoters. Red indicates upregulation/hypermethylation and blue indicates downregulation/hypomethylation. The networks (A)-(J) are from LumA subtype, (K)-(P) are from LumB subtype, (Q)-(T) are from HER2 subtype, (U)-(Y) are from basal subtype and (Z)-(AA) are from normal-like subtype\u003c/p\u003e","description":"","filename":"Figure45.png","url":"https://assets-eu.researchsquare.com/files/rs-7526346/v1/5ed4a8407ff4dd8df905ed82.png"},{"id":90894070,"identity":"089aa41c-9e70-41c5-aebd-01a6727bf832","added_by":"auto","created_at":"2025-09-09 11:27:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":31104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercentage of amplifications and deletions observed in ion channels and their associated enhancers/promoters across breast cancer subtypes\u003c/strong\u003e (A) Percentage of patients exhibiting CNA events in deregulated ion channels and their associated enhancers/promoters along the genome identified in lumA subtype (B) Percentage of patients exhibiting CNA events in deregulated ion channels and their associated enhancers/promoters along the genome identified in lumB subtype (C) Percentage of patients exhibiting CNA events in deregulated ion channels and their associated enhancers/promoters along the genome identified in HER2 subtype (D) Percentage of patients exhibiting CNA events in deregulated ion channels and their associated enhancers/promoters along the genome identified in basal subtype (E) Percentage of patients exhibiting CNA events in deregulated ion channels and their associated enhancers/promoters along the genome identified in normal-like subtype\u003c/p\u003e","description":"","filename":"Figure55.png","url":"https://assets-eu.researchsquare.com/files/rs-7526346/v1/7082bd3dc4fd00fb830d0966.png"},{"id":90892360,"identity":"eef22c56-8d89-4b41-b54b-4de805ba26d8","added_by":"auto","created_at":"2025-09-09 11:19:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":156511,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient-wise distribution of amplifications and deletions of ion channels and their associated enhancers/promoters across breast cancer subtypes\u003c/strong\u003e (A) Patient-wise distribution of amplifications and deletions in deregulated ion channels across breast cancer patients from different molecular subtypes (B) Patient-wise distribution of amplifications and deletions in enhancers/promoters associated with deregulated ion channels across breast cancer patients from different molecular subtypes\u003c/p\u003e","description":"","filename":"Figure64.png","url":"https://assets-eu.researchsquare.com/files/rs-7526346/v1/f7a93e1f6fc430887cbad6b0.png"},{"id":90891629,"identity":"c6862031-d588-4026-bfd6-5fb4f5320b6b","added_by":"auto","created_at":"2025-09-09 11:11:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":109419,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of deregulated ion channels with survival of patients\u003c/strong\u003e (A), (B), (C), (D), (E), (F) Kaplan-Meier survival curves indicating association of high and low expression of \u003cem\u003eANO6, SLC10A5, SLC31A1 \u003c/em\u003efrom lumA, \u003cem\u003eKCNH4 \u003c/em\u003efrom HER2, \u003cem\u003eGRINA \u003c/em\u003efrom basal and \u003cem\u003eSLC41A3 \u003c/em\u003efrom normal-like with survival of patients from the respective subtype (G), (H), (I), (J), (K), (L) Boxplots indicating the gene expression values of the 6 ion channels identified to be suggestive of prognosis in the different subtypes of breast cancer\u003c/p\u003e","description":"","filename":"Figure73.png","url":"https://assets-eu.researchsquare.com/files/rs-7526346/v1/410c2f728143f2caed623f5f.png"},{"id":98820722,"identity":"a10f24d6-e20c-4b1e-92ab-6a1e014c1cca","added_by":"auto","created_at":"2025-12-22 17:17:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8101558,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7526346/v1/d318dbf7-f2ce-4ab6-a589-6514cdd2a3f4.pdf"},{"id":90892362,"identity":"c6027af4-d36a-4a41-b579-20f2767713df","added_by":"auto","created_at":"2025-09-09 11:19:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":325036,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary material\u003c/p\u003e","description":"","filename":"ParthasarathiBreastCancerSsubtypesISRBsupplementaryMaterial072125.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7526346/v1/abfbd646376fe120e45af37e.pdf"},{"id":90891618,"identity":"31ec0945-ed7b-4044-8d22-742b4aa48fe6","added_by":"auto","created_at":"2025-09-09 11:11:57","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16961,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional Table2: List of ion channels exclusively deregulated across breast cancer subtypes along with their log fold change and p-values\u003c/p\u003e","description":"","filename":"AdditionalTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7526346/v1/cf606152ace09ab24e2dfabb.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA multiOmics approach to identify altered ion channels across breast cancer subtypes\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast cancer, one of the most prevalent cancer types in women worldwide, is a heterogeneous disease condition with distinct molecular and clinical behaviour that involves uncontrolled growth and division of breast cells. Owing to the heterogeneity observed, breast cancer samples can be classified into 5 major subtypes derived from PAM50 classification by Parker et al \u0026ndash; lumA, lumB, HER2, basal and normal-like [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This diversity not only adds on to the complexity in understanding the mechanisms involved, but also poses challenges for the development of clinic relevant strategies useful in terms of diagnosis, prognosis and improvement in patient care. In recent years a wide array of novel treatment strategies evolved that involve targeting specific signaling pathways to allow cell cycle management [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Advancements in Omics-based technologies are transforming the existing drugs into novel cancer therapeutics with an informed mechanistic perspective [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies have revealed that ion channels and transporters are essential regulators of mammary physiology as well as have a role in the initiation and progression of breast cancer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Recently, our group has identified altered ion channels in tumor and metastatic states of breast cancer highlighting the correlation of multiple co-expressed ion channels with epithelial to mesenchymal transition [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Upregulation of Na⁺/H⁺ exchanger 1 was reported to promote cell proliferation, invasion and migration in breast cancer MDA-MB-231 cell lines [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Mitochondrial calcium uniporters were identified in promoting cell migration, lung metastasis and invasion. whereas potassium channels were reported to be associated with poor prognosis in breast cancer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Mice model-based study indicated a reduction in the metastatic breast cells on treatment of pharmacological inhibitors of store operated calcium channels [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, induced overexpression of the store operated calcium channel \u003cem\u003eOrai1\u003c/em\u003e in T47D breast cancer cells led to chemoresistance and was linked to the consequent downregulation of p53 through activation of \u003cem\u003ePI3K\u003c/em\u003e [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The channel \u003cem\u003eTRPM2\u003c/em\u003e was as well implicated in enhancing anti-breast cancer drug cytotoxicity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, Zhang et al have previously demonstrated a correlation between autophagy associated protein LC3 and TRPC5 through the CaMKKβ/AMPKα/mTOR pathway [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The study had suggested the role of TRPC5 as an inducer of autophagy and as a target for the reversal of chemoresistance [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Similarly, ion channels have been implicated in multiple other tumor types as well, reflecting their potential in tumor pathophysiology [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, ion channels are therapeutic targets as they can be easily regulated by low-molecular-weight compounds when expressed on the plasma membrane [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Given the arsenal of ion channel-targeting drugs already in the market for various disorders, there is the possibility of rapid translation for oncologic patient care through drug repurposing strategies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Investigation of ion channels in breast cancer subtypes and their interactions with the different regulatory molecules could pave way for novel clinical strategies beneficial for patients with breast cancer.\u003c/p\u003e\u003cp\u003eCarcinogenesis is a complex process that generally involves molecular alterations at multiple levels including genomics, methylomics, and transcriptomics [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The technological advancements leading to the availability of data at various omics levels have provided the computational biologists with promising platforms to interpret the complexity in the biological environment [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Taking into account this opportunity several studies have come up with various methods to classify the different subtypes of breast cancer by including multiOmics datasets [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, there are also studies that have investigated the subtype specific factors affecting immune activity and the cross-talking pathways involved in breast cancer through integration of several omics layers [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, publicly available genomic, methylomic and transcriptomic datasets of patients with breast cancer were downloaded from UCSC Xena browser. The DETs, altered methylated probes and the copy number alterations were identified using various computational strategies. Subsequently, the deregulated transcripts corresponding to ion channels in each breast cancer subtype were identified. The possible regulatory molecules of the ion channels were identified based on the outputs from the methylomic and genomic level data analysis. The identified altered ion channels were compared against gene expression profiles of various cell lines corresponding to breast cancer subtypes sourced from the DepMap data portal. Thereafter, the ion channels associated with survival of patients with breast cancer subtypes were identified on the basis of Cox regression analysis. The detection of significant ion channels in breast cancer subtypes may contribute towards the strategies leading to the development of novel approaches for management of breast cancer. Identifying the regulatory aspects of ion channels provides insights on the role of ion channels in tumor growth and progression and require further experimental validation.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThe workflow of the study is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 \u003cb\u003eData Collection\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eA list of 493 ion channels was curated from the HGNC database. MultiOmics datasets including data at the transcriptomic, methylomic and genomic levels corresponding to TCGA-GDC BRCA patient cohort were downloaded. RNA-Seq gene expression profiles with log2(read count\u0026thinsp;+\u0026thinsp;1) values from Illumina platform representing the transcriptomic level, copy number alterations representing the genomic level data determined using GISTIC 2 and beta values from HM450 representing the methylomic level data were downloaded from UCSC Xena browser. The transcriptomic data and the methylomic data also included gene expression profiles and beta values from adjacent normal samples. The details on PAM50 subtypes corresponding to each tumor sample were extracted from the Additional data provided by Netanely et al [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 \u003cb\u003eData Preprocessing\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe overlapping samples across the three omics layers were pooled out. Each omic layer data was represented as a two-dimensional matrix with rows representing sample ids, columns representing transcript ids/probe ids/gene ids and cells representing the corresponding Omics layer values. Further, the column with the molecular subtype was included in the two-dimensional matrix and the subtypes were encoded into numerical forms for ease in data preprocessing (Additional Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTotal number of samples across breast cancer subtypes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal samples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLuminal A samples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLuminal B samples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHER2-enriched samples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBasal samples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNormal-like samples\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 \u003cb\u003eIdentification of differentially expressed transcripts in subtypes of breast cancer\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eR (v.4.1.3) Bioconductor package DESeq2 (v.1.34.0) was used to identify the subtype-wise differentially expressed transcripts [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The RNASeq log2(read count\u0026thinsp;+\u0026thinsp;1) data matrix was transformed to raw reads. The upregulated and downregulated transcripts from each subtype with respect to adjacent normal samples were identified using an adjusted p-value cut-off of 0.05 and log2FC of |0.6|. The transcripts found differentially expressed exclusively across subtypes were procured. The ion channels exclusive to a particular subtype were extracted from the deregulated list of transcripts. For visualization in terms of a heatmap, the DESeq2 normalised read count matrix was converted to z-score matrix.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 \u003cb\u003eIdentification of differentially methylated probes in subtypes of breast cancer\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe methylomic data matrix was filtered to exclude the probes with values in all samples as NaNs. ChAMP package (v.2.21.1) from R (v.4.4.1) Bioconductor was used to identify the subtype-wise differentially methylated probes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The non CpG probes, probes identified as single nucleotide polymorphisms, multi-hit probes and probes located on XY chromosomes were filtered out. The data was further normalized using the BMIQ dilution algorithm. Thereafter, the hypermethylated and hypomethylated probes with respect to adjacent normal samples were identified using a p-value threshold of 0.05 and logFC of |0.2|.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 \u003cb\u003eIdentification of occurrence of gene amplifications and deletions in subtypes of breast cancer\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eCustomized Python scripts were used to count the number of samples belonging to a subtype of breast cancer exhibiting gene amplifications or deletions in a particular gene using the genomic data matrix.\u003c/p\u003e\u003cp\u003e2.6 \u003cb\u003eIdentification of the association of the methylomic and genomic level data with the ion channels deregulated at transcriptomic level\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA list of enhancers and promoters were downloaded from the GeneHancer (v5.20) data resource. R Bioconductor packages GenomicRanges [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and rtracklayer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] were used to get the overlaps between the GeneHancer regulatory elements and the Illumina HM450 probes. Thereafter, the enhancers and promoters linked to the deregulated ion channels were extracted using customized Python scripts. Subsequently, the methylation status of those enhancers and promoters was examined using the output obtained from the analysis mentioned in section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe genomic level status of the transcriptionally deregulated ion channels and their associated differentially methylated probes were inspected using the output obtained from the analysis mentioned in section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.5\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Comparison of ion channels deregulated at transcriptomic level with subtype-specific cell line expression profiles\u003c/h2\u003e\u003cp\u003eCCLE cell lines corresponding to luminal, HER2 amplified and basal subtypes of breast cancer along with non-cancerous cell line expression profiles available through DepMap project were downloaded using R Bioconductor depmap library [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The alteration patterns in expression in the cell lines with the non-cancerous cell lines for the differentially expressed ion channels were observed and visualized using boxplots.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.8 \u003cb\u003eSurvival analysis of identified altered ion channels in breast cancer subtypes\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe clinical data corresponding to the vital status of the patients with each subtype of breast cancer was obtained using the R Bioconductor \u0026ldquo;RTCGA\u0026rdquo; package [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The correlation between gene expression data and 5-year survival rate of patients with each subtype was determined based on Cox proportional hazard regression analysis performed using the R Bioconductor \u0026ldquo;survival\u0026rdquo; package [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The log rank p-values and HR with a confidence interval of 95% was noted. The difference between high and low expressions with group cut-off criteria set to median value for the potential ion channels was visualized by generating KM survival plots using the R Bioconductor \u0026ldquo;survminer\u0026rdquo; package [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Collection of data and pre-processing\u003c/h2\u003e\u003cp\u003eRNA-Seq Illumina based log2(count\u0026thinsp;+\u0026thinsp;1) values from 1,055 tumor samples and 162 adjacent normal samples were downloaded as transcriptomic dataset. HM450 BeadChip array beta values for genes in 793 tumor samples and 97 adjacent normal samples were downloaded as methylomic data. Putative copy number calls on 1,104 samples of patients with breast cancer determined using GISTIC 2.0 algorithm consisting of values for deletion as -1, neutral/no change as 0 and amplification as +\u0026thinsp;1 were downloaded as genomic data. 779 tumor samples overlapped across the three omics layers with 701 samples consisting of information related to PAM50 subtype (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe transcriptomic data matrix consisted of htseq-counts from 60,843 transcripts, methylomic data matrix had beta values for 4,85,577 probes and the genomic data matrix consisted of amplification and deletion values for 19,730 genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Gene expression patterns in ion channels across breast cancer subtypes\u003c/h2\u003e\u003cp\u003eDETs were identified in the five subtypes of breast cancer with 13,650 in lumA, 13,230 in lumB, 12,831 in HER2, 13,552 in basal and 8,205 in normal-like subtypes (Additional Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Amidst the DETs, 22, 9, 9, 20 and 19 ion channels were found exclusively in lumA, lumB, HER2, basal and normal-like subtypes respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Additional table 2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Methylation patterns across breast cancer subtypes\u003c/h2\u003e\u003cp\u003eDMPs were identified in the five subtypes of breast cancer, with 36,313 in lumA, 55,958 in lumB, 39,319 in HER2, 25,316 in basal and 2,292 in normal-like samples (Additional Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). 4,17,870 regulatory elements including 3,80,634 enhancers, 7,078 promoters and 30,158 enhancers/promoters were obtained from GeneHancer. 1,21,602 HM450 methylation probes overlapped with 40,780 GeneHancer elements including 32,262 enhancers, 332 promoters and 8,186 enhancer/promoters. The enhancers and promoters associated with the deregulated ion channel transcripts were identified from GeneHancer elements list that overlapped with the methylation probes.\u003c/p\u003e\u003cp\u003e34 enhancers and promoters corresponding to 58 methylation probes were associated with 10 deregulated ion channels from lumA subtype. 27 enhancers and promoters corresponding to 46 probes were associated with 6 deregulated ion channels from lumB subtype. Similarly, in the HER2 subtype, 7 enhancers and promoters linked to 17 methylation probes corresponded to 4 ion channels and in the basal subtype 18 enhancers and promoters mapping to 38 probes corresponded to 5 ion channels. In the normal-like subtype, only 2 enhancers and promoters that corresponded to 2 methylation probes were associated with 2 ion channels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e,\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe distribution and location of the DMPs associated with deregulated ion channels in relation to the genomic features and CpG islands are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB. In each subtype, most DMPs associated with the differentially expressed ion channels were present in the gene body and in the intergenic regions followed by 200\u0026ndash;1500 base pairs upstream of TSS1500. Additionally, DMPs were also located in the region upto 200 base pairs from the TSS200, 5\u0026rsquo; UTR, 3\u0026rsquo;UTR and in the 1st exon. In terms of DMP location in relation to CpG islands, the largest number of DMPs from lumA, lumB, HER2 and basal-like mapped to open-sea (the region beyond 4kb from CpG island) region followed by CpG islands, shore region (2kb from CpG island) and the shelf region (2\u0026ndash;4 kb from CpG island). Two DMPs were associated with the deregulated ion channels from the normal-like subtype mapped to the CpG island region and open-sea region. Majority of DMPs were observed to be hypermethylated compared to the number of hypomethylated DMPs across subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). No overlaps were identified between the DMPs across subtypes associated with subtype exclusive ion channels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Copy number alterations across subtypes\u003c/h2\u003e\u003cp\u003eIn lumA subtype, the ion channels and its associated enhancers/promoters located at chromosomes 1 and 11 were most frequently affected by CNAs followed by chromosomes 12, 13, 16, 17, 3, 8, 7 ,6 ,20, 22, 2, 19, 4, 9 and X. The ion channels \u003cem\u003eSCNN1D\u003c/em\u003e (36/378, 9.5%), \u003cem\u003eP2RX7\u003c/em\u003e (26/378, 6.8%), \u003cem\u003eSLC4A1\u003c/em\u003e (20/378, 5.2%) and \u003cem\u003eTRPV2\u003c/em\u003e (12/378, 3.1%) were found to be deleted more often than amplified whereas the ion channels \u003cem\u003eCATSPER1\u003c/em\u003e (43/378, 11.3%), \u003cem\u003eITPR3\u003c/em\u003e (12/378, 3.1%) and \u003cem\u003eKCTD5\u003c/em\u003e (20/378, 5.2%) were found to be amplified more often than deleted in lumA patients. The other ion channels \u003cem\u003eCLIC5\u003c/em\u003e, \u003cem\u003eANO6\u003c/em\u003e, \u003cem\u003eSLC12A8\u003c/em\u003e and \u003cem\u003eSLC10A6\u003c/em\u003e were observed to have more or less equal number patients with deletions and amplifications in them. The enhancers/promoters \u003cem\u003ePUSL1\u003c/em\u003e (36/378, 9.5%) associated to \u003cem\u003eSCNN1D\u003c/em\u003e, \u003cem\u003ePTRF\u003c/em\u003e (21/378, 5.5%) associated with \u003cem\u003eSLC4A1\u003c/em\u003e and \u003cem\u003eCENPV\u003c/em\u003e (12/378, 3.17%) associated with \u003cem\u003eTRPV2\u003c/em\u003e were found to be deleted more often than amplified in lumA patients. \u003cem\u003eLTBP3\u003c/em\u003e (42/378, 11.1%) and \u003cem\u003eOVOL1\u003c/em\u003e (42/378, 11.1%) associated with \u003cem\u003eCATSPER1\u003c/em\u003e and \u003cem\u003eCLUAP1\u003c/em\u003e (18/378, 4.7%) associated with \u003cem\u003eKCTD5\u003c/em\u003e were found to be amplified more frequently. Similarly, in lumB subtype, the ion channels and associated enhancers/promoters from chromosomes 17 and 1 were most frequently affected followed by chromosomes 9, 12, 5, 10, 16, 3, 6 and X. The ion channels found to be deleted in most of the lumB patients were \u003cem\u003eSLC9A1\u003c/em\u003e (25/131, 19%) and \u003cem\u003eANO2\u003c/em\u003e (9/131, 6.8%) whereas \u003cem\u003eCACNG3\u003c/em\u003e (7/131, 5.3%), \u003cem\u003eSLC9A3\u003c/em\u003e (12/131, 9.1%) and \u003cem\u003eSLC16A9\u003c/em\u003e (9/131, 6.8%) were found to be mostly amplified as compared to deleted in cases with lumB subtype. The enhancers/promoters \u003cem\u003eSYTL1\u003c/em\u003e (25/131, 19%) and \u003cem\u003eWASF2\u003c/em\u003e (25/131, 19%) associated with \u003cem\u003eSLC9A1\u003c/em\u003e, \u003cem\u003eCD9\u003c/em\u003e (9/131, 6.8%) and \u003cem\u003eVWF\u003c/em\u003e (9/131, 6.8%) associated with \u003cem\u003eANO2\u003c/em\u003e were observed to be mostly deleted. In terms of HER2 subtype the ion channels and enhancer/promoters from chromosomes 17 was observed to be most frequently affected followed by the chromosomes 2, 11, 6, 15, 12 and 16. Deletions in the ion channels \u003cem\u003eKCNH4\u003c/em\u003e (10/40, 25%) and \u003cem\u003eANO7\u003c/em\u003e (8/40, 20%) were present in most of cases with HER2 subtype whereas the other ion channels \u003cem\u003eTRPC6\u003c/em\u003e and \u003cem\u003eSLC24A5\u003c/em\u003e were observed to have comparative number of cases with amplifications and deletions. \u003cem\u003eKRT222\u003c/em\u003e (20/40, 50%) associated with \u003cem\u003eKCNH4\u003c/em\u003e had an equal number of cases with deletions and amplifications. Basal subtype showed the ion channels and enhancer/promoters on chromosomes 1, 8, 12, 15 and 17 to be affected most frequently by CNAs followed by the chromosomes 2, 11, 20, 4, 5, 14 and 22. The ion channels \u003cem\u003eTRPM7\u003c/em\u003e was found to be deleted in 22.1% (27/122) of cases with basal breast cancer subtype and the ion channels \u003cem\u003eTMCO1, SLC26A11\u003c/em\u003e, \u003cem\u003eCACNA1G\u003c/em\u003e, \u003cem\u003eKCNA5\u003c/em\u003e and \u003cem\u003eGRINA\u003c/em\u003e were found amplified in 32.23% (39/122), 36% (44/122), 8.1% (10/122), 27.8% (34/122) and 29.5% (36/122) respectively. \u003cem\u003eNTF3\u003c/em\u003e (32/122, 26.2%) associated with \u003cem\u003eKCNA5\u003c/em\u003e was found to be most frequently amplified. In normal-like subtype the ion channels and associated enhancer/promoters found to be most frequently affected by CNAs were present in the chromosomes 7, 1, 12 and 19 followed by X, 17, 10, 16, 3, 4 and 6. The ion channels \u003cem\u003eSLC41A2\u003c/em\u003e, \u003cem\u003eCATSPERD\u003c/em\u003e, \u003cem\u003eCLCNKB\u003c/em\u003e and \u003cem\u003eCATSPER4\u003c/em\u003e were found to be deleted in 6.6% (2/30) of the normal-like breast cancer cases in the study whereas \u003cem\u003eSLC13A4\u003c/em\u003e was observed to be amplified in 10% (3/30) of cases with the normal-like subtype. Individual gene amplifications and deletions are represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Additional Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Alterations in ion channels using cell line expression profiles\u003c/h2\u003e\u003cp\u003eExpression profiles for fourteen luminal cell lines, thirteen HER2 amplified cell lines, two basal-like cell lines and one non-cancerous cell line were obtained from DepMap project. The altered ion channels \u003cem\u003eCATSPER1\u003c/em\u003e, \u003cem\u003eITPR3\u003c/em\u003e, \u003cem\u003eTRPC4\u003c/em\u003e, \u003cem\u003eP2RX7\u003c/em\u003e and \u003cem\u003eTRPV2\u003c/em\u003e from luminalA subtype, \u003cem\u003eKCNJ14\u003c/em\u003e from luminalB subtype and \u003cem\u003eGJC2\u003c/em\u003e, \u003cem\u003eSLC8A3\u003c/em\u003e from basal-like subtype showed positive correlation in terms of alterations in expression comparison with cell line expression alterations (Additional Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Association of potential ion channels with survival of patients from different subtypes\u003c/h2\u003e\u003cp\u003eThe ion channels with log rank \u003cem\u003ep\u003c/em\u003e-value and \u003cem\u003ep\u003c/em\u003e(HR)\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were selected as suggestive ion channels in survival of patients with different subtypes of breast cancer. Among the ion channels identified as subtype exclusive ion channels, six were found to be prognostically suggestive in the different subtypes. Three ion channels from lumA subtype \u0026ndash; \u003cem\u003eANO6\u003c/em\u003e (HR:1.69, p(HR):0.04), \u003cem\u003eSLC10A5\u003c/em\u003e (HR:2.27, p(HR):0.0001), \u003cem\u003eSLC31A1\u003c/em\u003e (HR:1.69, p(HR):0.05), one from HER2 subtype \u0026ndash; \u003cem\u003eKCNH4\u003c/em\u003e (HR:0.28, p(HR):0.02), one from basal subtype \u0026ndash; \u003cem\u003eGRINA\u003c/em\u003e (HR:2.28, p(HR): 0.03) and one from normal-like subtype \u0026ndash; \u003cem\u003eSLC41A3\u003c/em\u003e (HR:0.04, p(HR):0.1) were identified. In lumA, patients with low expression of \u003cem\u003eANO6\u003c/em\u003e (p-value:0.01), \u003cem\u003eSLC10A5\u003c/em\u003e (p-value:0.002) and \u003cem\u003eSLC31A1\u003c/em\u003e (p-value:0.01) were observed to have better survival than the patients with higher expression. Similarly, in basal subtype patients with low expression of \u003cem\u003eGRINA\u003c/em\u003e (p-value:0.07) were observed to have better survival. In HER2 and normal-like subtype the patients with high expression of \u003cem\u003eKCNH4\u003c/em\u003e (p-value:0.1 ) and \u003cem\u003eSLC41A3\u003c/em\u003e (p-value:0.05) respectively were observed to have better survival than patients with lower expression suggesting that higher expression of these ion channels in the respective subtypes could lower the hazard ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBreast cancer subtypes present unique molecular characteristics and is one of the most common malignant tumors in women, with a high mortality rate globally making it a condition that requires attention. Aberrant channels can disrupt normal cellular activities, transforming them into malignant cells [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Although literature documents the deregulation of ion channels in breast cancer, finding effective targets remains a challenge. In this study an analysis pipeline integrating gene expressions, methylation patterns and copy number alterations was utilized to identify the ion channels in breast cancer subtypes. The analysis allowed the identification of ion channels deregulated in the molecular subtypes with details on the methylation status of its regulatory elements further relating to the alterations associated at the genomic level (Additional Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Identified methylated regions regulating ion channels included both hyper and hypomethylation, challenging the norm that DNA methylation suppresses gene expression.\u003c/p\u003e\u003cp\u003e22 ion channels were deregulated exclusively in lumA subtype in the present study - \u003cem\u003eGLRA4, SLC16A7, SLC12A8, CLCN2, ITPR3, CLIC5, KCTD5, SLC4A1, TRPC4, SLC10A5, KCTD17, CATSPER1, KCTD15, SLC31A1, SCN11A, SCN9A, P2RX7, TRPV2, SCNN1D, CLCN1, ANO6\u003c/em\u003e and \u003cem\u003eSLC10A6\u003c/em\u003e. Of these, \u003cem\u003eSLC12A8, CLIC5, KCTD5, TRPV2\u003c/em\u003e and \u003cem\u003eANO6\u003c/em\u003e were reported previously in breast cancer [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In the present study, \u003cem\u003eSLC12A8\u003c/em\u003e was downregulated. Previously \u003cem\u003eSLC12A8\u003c/em\u003e was reported as deregulated in breast cancer and its role in toll-like receptor/nod-like receptor (TLR/NLR) signaling pathway and peroxisome proliferator-activated receptors (PPAR) γ signaling pathway were discussed [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. \u003cem\u003eCLIC5\u003c/em\u003e was upregulated and its associated enhancers/promoters were deregulated. \u003cem\u003eCLIC5\u003c/em\u003e was previously reported in prognosis and in immune/inflammatory chemokine regulation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. \u003cem\u003eKCTD5\u003c/em\u003e was downregulated whereas its associated enhancers and promoters were hypomethylated. Increased expression of \u003cem\u003eKCTD5\u003c/em\u003e was previously studied in breast cancer cell lines [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The hypermethylation of the enhancer \u003cem\u003eCENPV\u003c/em\u003e at multiple genomic locations was identified to be associated with the downregulated ion channel \u003cem\u003eTRPV2\u003c/em\u003e consisting of 16 patients with amplifications and deletions. The upregulation of \u003cem\u003eANO6\u003c/em\u003e in lumA subtype had dysregulations of methylation in 3 enhancers/promoters from the intergenic regions. \u003cem\u003eANO6\u003c/em\u003e was reported to have a role in prognosis of patients with breast cancer [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e9 ion channels exclusively deregulated in lumB subtype were \u003cem\u003ePKD2L2, KCNJ14, ANO2, CACNG3, SLC9A1, SLC9A3, KCNJ8, SLC22A7\u003c/em\u003e and \u003cem\u003eSLC16A9. SLC9A1\u003c/em\u003e was reported deregulated in previous studies. Upregulated \u003cem\u003eSLC9A1\u003c/em\u003e was associated with 2 deregulated enhancers/promoters. \u003cem\u003eSLC9A1\u003c/em\u003e has role in development of cancer, as a mediator of an increased extrusion of acid in tumor cells and was reported a potential target for treatment [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e9 ion channels \u0026ndash; \u003cem\u003eKCNG4, CNGA4, KCNH4, AQP8, GABRR2, TRPC6, ANO7, ASIC1\u003c/em\u003e and \u003cem\u003eSLC24A5\u003c/em\u003e were identified as deregulated exclusively in HER2. \u003cem\u003eAQP8\u003c/em\u003e was upregulated. Elevated mRNA expression of \u003cem\u003eAQP8\u003c/em\u003e was predicted to have better relapse-free survival in all breast cancer patients [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. \u003cem\u003eTRPC6\u003c/em\u003e was upregulated and was hypomethylated at genomic location chr11-101323578. Several studies have reported \u003cem\u003eTRPC6\u003c/em\u003e as overexpressed in cell lines and in biopsy tissues. Further its role in cell proliferation, migration and invasion were reported [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e20 ion channels \u0026ndash; \u003cem\u003eKCNN2, SLC9A8, SLC16A14, CACNA1I, GRINA, SLC30A9, SLC22A9, KCNA5, CACNA1G, SLC26A11, SLC10A7, GLRA1, SLC20A1, GJC2, KCNJ13, TMCO1, TRPM7, TRPM5, SLC8A3\u003c/em\u003e and \u003cem\u003eKCNK2\u003c/em\u003e were identified to be deregulated in basal subtype from the current study. \u003cem\u003eCACNA1I\u003c/em\u003e was upregulated and \u003cem\u003eCACNA1G\u003c/em\u003e was downregulated with all its associated enhancers/promoters majorly including \u003cem\u003eHOXB13\u003c/em\u003e to be hypermethylated. A study reported patients with metastatic state of tumor had higher levels \u003cem\u003eCACNA1I\u003c/em\u003e and lower levels of \u003cem\u003eCACNA1G\u003c/em\u003e indicating it as a tumor suppressor [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. \u003cem\u003eKCNA5\u003c/em\u003e was downregulated. \u003cem\u003eKCNA5\u003c/em\u003e interacts with caveolin and further aids in early transformation and proliferation of mammary cells [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. \u003cem\u003eSLC20A1\u003c/em\u003e was upregulated. Higher level of \u003cem\u003eSLC20A1\u003c/em\u003e in ER\u003csup\u003e+\u003c/sup\u003e individuals was associated with 10-year survival along with lower expression of \u003cem\u003eKMT2C\u003c/em\u003e gene [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. \u003cem\u003eGJC2\u003c/em\u003e was identified as downregulated. It was previously reported to cause lymphedema in patients with breast cancer [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. \u003cem\u003eTRPM7\u003c/em\u003e was found to be upregulated. It was reported to regulate EGF-induced STAT3 phosphorylation and EMT marker vimentin expression [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e19 ion channels \u0026ndash; \u003cem\u003eKCNJ12, GJB1, GRIN2C, GABRR1, CLCNKB, TRPC5, GJD3, MIP, SLC9A5, CACNA1S, CATSPERD, SLC26A26, KCNH3, SLC41A2, CATSPER4, SLC13A4, SLC41A3, GRIK5\u003c/em\u003e and \u003cem\u003eKCNAB3\u003c/em\u003e were identified exclusively deregulated in normal-like subtype. \u003cem\u003eGJB1\u003c/em\u003e was upregulated in patients with normal-like breast cancer and was also significantly overexpressed in breast cancer stem cells [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. \u003cem\u003eTRPC5\u003c/em\u003e was upregulated. Exosomal \u003cem\u003eTRPC5\u003c/em\u003e was linked to chemotherapy resistance in breast cancer patients [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The study further laid down suggestive ion channels in terms of association with patient survival attributed to a subtype. However, number of patients in each subtype of breast cancer were limited to attain a statistical significance of 0.05, thus, 0.1 was taken to allow detection of meaningful trends that warrant further investigation.\u003c/p\u003e\u003cp\u003eIt is evident that multiple studies have been carried out to understand the role of ion channels in breast cancer. However, altered ion channels across breast cancer subtypes were not known. Here, we identified potential ion channels altered at multiple omics layers across breast cancer subtypes. Overall, the study underlines the importance of studying the ion channels at a multiOmics level to assess the downstream regulatory effects in the different subtypes of breast cancer and further requires experimental validation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLumA \u0026ndash; LuminalA\u003c/p\u003e\n\u003cp\u003eLumB \u0026ndash; LuminalB\u003c/p\u003e\n\u003cp\u003eHGNC \u0026ndash; Human Genome Organization Gene Nomenclature Committee\u003c/p\u003e\n\u003cp\u003eUCSC \u0026ndash; University of California, Santa Cruz\u003c/p\u003e\n\u003cp\u003eGDC \u0026ndash; Genomic Data Commons\u003c/p\u003e\n\u003cp\u003eTCGA \u0026ndash; The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eCCLE - Cancer Cell Line Encyclopedia\u003c/p\u003e\n\u003cp\u003eBRCA \u0026ndash; Breast cancer\u003c/p\u003e\n\u003cp\u003eChAMP \u0026ndash; Chip Analysis Methylation Pipeline\u003c/p\u003e\n\u003cp\u003eBMIQ \u0026ndash; Beta MIxture Quantile\u003c/p\u003e\n\u003cp\u003eHR \u0026ndash; Hazard ratios\u003c/p\u003e\n\u003cp\u003eKM \u0026ndash; Kaplan-Meier\u003c/p\u003e\n\u003cp\u003eDET \u0026ndash; Differentially expressed transcripts\u003c/p\u003e\n\u003cp\u003eDMP \u0026ndash; Differentially methylated probes\u003c/p\u003e\n\u003cp\u003eCNA \u0026ndash; Copy number alterations\u003c/p\u003e\n\u003cp\u003eFC \u0026ndash; Fold change\u003c/p\u003e\n\u003cp\u003eHM450 - Illumina\u0026apos;s Infinium HumanMethylation450\u003c/p\u003e\n\u003cp\u003eGISTIC \u0026ndash; Genomic Identification of Significant Targets in Cancer\u003c/p\u003e\n\u003cp\u003eTSS \u0026ndash; Transcript start site\u003c/p\u003e\n\u003cp\u003eUTR \u0026ndash; Untranslated region\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the authors of the manuscripts for making the datasets used in this study publicly available. We thank Sanjib Chaudhary for helpful discussions. JS would like to thank Council of Scientific and Industrial Research (CSIR), Government of India [37WS(0114)/2023-24/EMR-II/ASPIRE] and Indian Council of Medical Research (ICMR), Government of India for the research support. KTSP was supported by ICMR, Government of India [BMI/12(95)2021]. JS was a recipient of the Bio-CARe Women Scientists award from the Department of Biotechnology (DBT), Government of India.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData accessibility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Python and R scripts written in support of this publication is publicly available at our GitHub repository and can be accessed through the following link \u0026ldquo;https://github.com/js-iob/Breast_cancer_subtypes_multiOmics\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, JS; methodology, JS, KTSP; investigation, JS and KTSP; writing\u0026mdash;original draft, KTSP; writing\u0026mdash;review and editing, KTSP and JS; funding acquisition JS; supervision, JS. All authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eParker JS et al (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27(8):1160\u0026ndash;1167\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParker JS et al (2023) Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes. J Clin Oncol 41(26):4192\u0026ndash;4199\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarzaman K et al (2020) Breast cancer: Biology, biomarkers, and treatments. Int Immunopharmacol 84:106535\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNagarajan D, McArdle SEB (2018) Immune Landscape of Breast Cancers. Biomedicines, 6(1)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShah AN, Cristofanilli M (2017) The Growing Role of CDK4/6 Inhibitors in Treating Hormone Receptor-Positive Advanced Breast Cancer. Curr Treat Options Oncol 18(1):6\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa Z et al (2020) Function of ion transporters in maintaining acid-base homeostasis of the mammary gland and the pathophysiological role in breast cancer. Am J Physiol Regul Integr Comp Physiol 318(1):R98\u0026ndash;R111\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRao MRMaR (2020) \u003cem\u003eSubtype specific targeting of calcium signaling in breast cancer.\u003c/em\u003e ClinicalKey\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu C et al (2020) Pathological role of ion channels and transporters in the development and progression of triple-negative breast cancer. Cancer Cell Int 20:377\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParthasarathi KTS et al (2022) \u003cem\u003eIn\u003c/em\u003e Silico Analysis of Ion Channels and Their Correlation with Epithelial to Mesenchymal Transition in Breast Cancer. Cancers (Basel), 14(6)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin Y et al (2011) NHE1 mediates MDA-MB-231 cells invasion through the regulation of MT1-MMP. Exp Cell Res 317(14):2031\u0026ndash;2040\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J et al (2015) CIAPIN1 targets Na(+)/H(+) exchanger 1 to mediate MDA-MB-231 cells' metastasis through regulation of MMPs via ERK1/2 signaling pathway. Exp Cell Res 333(1):60\u0026ndash;72\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu C et al (2017) Mitochondrial calcium uniporter as a target of microRNA-340 and promoter of metastasis via enhancing the Warburg effect. Oncotarget 8(48):83831\u0026ndash;83844\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlvarez-Baron CP et al (2011) The two-pore domain potassium channel KCNK5: induction by estrogen receptor alpha and role in proliferation of breast cancer cells. Mol Endocrinol 25(8):1326\u0026ndash;1336\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang S, Zhang JJ, Huang XY (2009) Orai1 and STIM1 are critical for breast tumor cell migration and metastasis. Cancer Cell 15(2):124\u0026ndash;134\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHasna J et al (2018) Orai3 calcium channel and resistance to chemotherapy in breast cancer cells: the p53 connection. Cell Death Differ 25(4):693\u0026ndash;707\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoh DW et al (2015) Enhanced cytotoxicity in triple-negative and estrogen receptor\u0026ndash;positive breast adenocarcinoma cells due to inhibition of the transient receptor potential melastatin-2 channel. Oncol Rep 34(3):1589\u0026ndash;1598\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang P et al (2017) TRPC5-induced autophagy promotes drug resistance in breast carcinoma via CaMKKbeta/AMPKalpha/mTOR pathway. Sci Rep 7(1):3158\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAltamura C et al (2022) Ion Channel Involvement in Tumor Drug Resistance. J Pers Med, 12(2)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParthasarathi KTS et al (2023) Aberrations in ion channels interacting with lipid metabolism and epithelial-mesenchymal transition in esophageal squamous cell carcinoma. Front Mol Biosci 10:1201459\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharma J et al (2019) Somatic Mutations Profile of a Young Patient With Metastatic Urothelial Carcinoma Reveals Mutations in Genes Involved in Ion Channels. Front Oncol 9:435\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharma J et al (2020) Editorial: Multi-Omics Approaches to Study Signaling Pathways. Front Bioeng Biotechnol 8:829\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang L et al (2014) Integrating multi-omics for uncovering the architecture of cross-talking pathways in breast cancer. PLoS ONE 9(8):e104282\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSheetal Rajpal VK, Agarwal M, Kumar N (2021) Deep Learn Based Model Breast Cancer Subtype Classif arXiv:211103923\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoi JM, Chae H (2023) moBRCA-net: a breast cancer subtype classification framework based on multi-omics attention neural networks. BMC Bioinformatics 24(1):169\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeshoul S, Shaiba BA, AlBinali H (2022) S., \u003cem\u003eExplainable Multi-Class Classification Based on Integrative Feature Selection for Breast Cancer Subtyping.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRohani N, Eslahchi C (2020) Classifying Breast Cancer Molecular Subtypes by Using Deep Clustering Approach. Front Genet 11:553587\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThalor A et al (2022) Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer. Comput Struct Biotechnol J 20:1618\u0026ndash;1631\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTao M et al (2019) Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data. Genes (Basel), 10(3)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin Y et al (2020) Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data. Genes (Basel), 11(8)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang Y, Zeng P, Zhong C (2024) Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning. BMC Bioinformatics 25(1):132\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDing R et al (2023) Identification of Breast Cancer Subtypes by Integrating Genomic Analysis with the Immune Microenvironment. ACS Omega 8(13):12217\u0026ndash;12231\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi X et al (2021) Uncovering the Subtype-Specific Molecular Characteristics of Breast Cancer by Multiomics Analysis of Prognosis-Associated Genes, Driver Genes, Signaling Pathways, and Immune Activity. Front Cell Dev Biol 9:689028\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNetanely D et al (2016) Expression and methylation patterns partition luminal-A breast tumors into distinct prognostic subgroups. Breast Cancer Res 18(1):74\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLove MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTian Y et al (2017) ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics 33(24):3982\u0026ndash;3984\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLawrence M et al (2013) Software for computing and annotating genomic ranges. PLoS Comput Biol 9(8):e1003118\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLawrence M, Gentleman R, Carey V (2009) rtracklayer: an R package for interfacing with genome browsers. Bioinformatics 25(14):1841\u0026ndash;1842\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKillian T (2021) \u003cem\u003eExploiting the DepMap cancer dependency data using the depmap R package.\u003c/em\u003e F1000Research\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eW C (2025) \u003cem\u003eRTCGA.mRNA: mRNA datasets from The Cancer Genome Atlas Project.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTherneau TM (2024) \u003cem\u003eA Package for Survival Analysis in R.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlboukadel Kassambara MK, Biecek P, Fabian S (2024) \u003cem\u003esurvminer: Drawing Survival Curves using 'ggplot2'.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrevarskaya N, Skryma R, Shuba Y (2018) Ion Channels Cancer: Are Cancer Hallm Oncochannelopathies? Physiol Rev 98(2):559\u0026ndash;621\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi L et al (2021) Solute carrier family 12 member 8 impacts the biological behaviors of breast carcinoma cells by activating TLR/NLR signaling pathway. Cytotechnology 73(1):23\u0026ndash;34\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYau C et al (2010) A multigene predictor of metastatic outcome in early stage hormone receptor-negative and triple-negative breast cancer. Breast Cancer Res 12(5):R85\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRivas J et al (2020) KCTD5, a novel TRPM4-regulatory protein required for cell migration as a new predictor for breast cancer prognosis. FASEB J 34(6):7847\u0026ndash;7865\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElbaz M et al (2018) TRPV2 is a novel biomarker and therapeutic target in triple negative breast cancer. Oncotarget 9(71):33459\u0026ndash;33470\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu ZH et al (2021) The role of ferroptosis in breast cancer patients: a comprehensive analysis. Cell Death Discov 7(1):93\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoedtkjer E et al (2013) \u003cem\u003eContribution of Na+,HCO3(-)-cotransport to cellular pH control in human breast cancer: a role for the breast cancer susceptibility locus NBCn1 (SLC4A7).\u003c/em\u003e Int J Cancer, 132(6): pp. 1288-99\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu L et al (2019) Significant prognostic values of aquaporin mRNA expression in breast cancer. Cancer Manag Res 11:1503\u0026ndash;1515\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJardin I et al (2018) TRPC6 Channels Are Required for Proliferation, Migration and Invasion of Breast Cancer Cell Lines by Modulation of Orai1 and Orai3 Surface Exposure. Cancers (Basel), 10(9)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDhennin-Duthille I et al (2011) High expression of transient receptor potential channels in human breast cancer epithelial cells and tissues: correlation with pathological parameters. Cell Physiol Biochem 28(5):813\u0026ndash;822\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRagab Ibrahim FAE et al (2022) Insights on possible interplay between epithelial-mesenchymal transition and T-type voltage gated calcium channels genes in metastatic breast carcinoma. Heliyon 8(8):e10160\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J et al (2016) Expression of KCNA5 Protein in Human Mammary Epithelial Cell Line Associated with Caveolin-1. J Membr Biol 249(4):449\u0026ndash;457\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSato K, Akimoto K (2017) Expression Levels of KMT2C and SLC20A1 Identified by Information-theoretical Analysis Are Powerful Prognostic Biomarkers in Estrogen Receptor-positive Breast Cancer. Clin Breast Cancer 17(3):e135\u0026ndash;e142\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFinegold DN et al (2012) Connexin 47 mutations increase risk for secondary lymphedema following breast cancer treatment. Clin Cancer Res 18(8):2382\u0026ndash;2390\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZekri AN et al (2022) Genetic profiling of different phenotypic subsets of breast cancer stem cells (BCSCs) in breast cancer patients. Cancer Cell Int 22(1):423\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang M et al (2018) Effect of exosome biomarkers for diagnosis and prognosis of breast cancer patients. Clin Transl Oncol 20(7):906\u0026ndash;911\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Institute of Bioinformatics","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PAM50, gene signature, copy number alteration, methylation, RNA-Seq, subtype classification","lastPublishedDoi":"10.21203/rs.3.rs-7526346/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7526346/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA multiOmics approach unifies patient-specific datasets to deepen insights into molecular aspects of cancer. Breast cancer cells often exhibit membrane potential deregulation driven by alterations in ion channel activity and distribution. Deregulation of ion channels could result in chemo-resistance, proliferation stimulation and tumor growth maintenance. Here, differentially expressed ion channels (DEICs), differentially methylated regions (DMRs) associated with those ion channels and, copy number alterations in the DEICs and their associated DMRs were identified using publicly available transcriptomic, methylomic and genomic datasets of patients with breast cancer subtypes. The expression of DEICs was further compared using cell line expression profiles available from the DepMap project. Additionally, prognostically significant ion channels were identified using Kaplan-Meier survival plots. 79 ion channels including 22, 9, 9, 20 and 19 were differentially expressed in luminalA, luminalB, HER2, basal and normal-like subtypes, respectively. Of those, 27 ion channels including 10, 6, 4, 5 and 2 were associated with 161 differentially methylated enhancers and promoters in luminalA, luminalB, HER2, basal and normal-like subtypes, respectively. Several patients exhibited amplifications and deletions affecting the 27 ion channels and their associated DMRs. 9 ion channels indicated a positive correlation of expression alterations in cell lines expression profiles. \u003cem\u003eANO6\u003c/em\u003e, \u003cem\u003eSLC10A5\u003c/em\u003e, and \u003cem\u003eSLC31A1\u003c/em\u003e in luminalA, \u003cem\u003eKCNH4\u003c/em\u003e in HER2-enriched and \u003cem\u003eGRINA\u003c/em\u003e in basal-like were associated with survival in patients with subtypes of breast cancer. Most likely, the study marks the first step towards establishing oncochannels across breast cancer subtypes and encourages future research to investigate potential ion channels through experimental validation.\u003c/p\u003e","manuscriptTitle":"A multiOmics approach to identify altered ion channels across breast cancer subtypes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 11:11:52","doi":"10.21203/rs.3.rs-7526346/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aecd792f-1037-41e4-b3d4-bf5985aa0177","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T17:17:41+00:00","versionOfRecord":{"articleIdentity":"rs-7526346","link":"https://doi.org/10.1016/j.insi.2025.100113","journal":{"identity":"in-silico-research-in-biomedicine","isVorOnly":true,"title":"In Silico Research in Biomedicine"},"publishedOn":"2025-11-13 00:00:00","publishedOnDateReadable":"November 13th, 2025"},"versionCreatedAt":"2025-09-09 11:11:52","video":"","vorDoi":"10.1016/j.insi.2025.100113","vorDoiUrl":"https://doi.org/10.1016/j.insi.2025.100113","workflowStages":[]},"version":"v1","identity":"rs-7526346","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7526346","identity":"rs-7526346","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}