A meta-analysis of PCAT1; prostate cancer-associated transcript 1 gene as a prognostic biomarker for invasive breast carcinoma

doi:10.21203/rs.3.rs-1706081/v1

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A meta-analysis of PCAT1; prostate cancer-associated transcript 1 gene as a prognostic biomarker for invasive breast carcinoma

https://doi.org/10.21203/rs.3.rs-1706081/v1

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Prostate cancer-associated transcript 1 (PCAT1) is a long non-coding RNA (lncRNA), composed of more than 200 nucleotides. Its expression controls the proliferation, migration, invasion, and metastasis of different cancer. In the current study, we conducted a patient data derived metanalysis to study the possible role of PCAT1 as a prognostic biomarker for the invasive breast cancer (IBC). Our analysis demonstrated that, PCAT1 highly expressed in BRCA mutated BC patients’ cells compared to normal cells (fold change ~ 1.56). The overall survival rate of patients with higher PCAT1 expression significantly (p- 5.539e^− 5) reduced over time. Moreover, PCAT1 was significantly altered in PR (p- 1.672e-⁶), HER2 (p- 3.190e-⁶) negative and ER (p- 3.920e-⁴) positive primary IBC samples. In addition, alteration of PCAT1 also correlated with the histologic grade of the IBC (p- >10^− 10). On top of that, we found co-occurrence of PCAT1 with the oncogenes of CASC family i.e., CASC8, CASC11, and CASC19 in IBC. The genomic location of PCAT1, CASC8, CASC11, and CASC19 was chr8q24 and chr8q24 respectively which contains the loci responsible for different cancers including BC. In addition, the CASC8 and CASC19 overlapped with PCAT1 in genomic sequence. Taken together, PCAT1 expression is associated with poor clinical outcomes and co-occur with previously established oncogenes and biomarkers which suggest its usefulness as a prognostic biomarker for IBC.

Prostate cancer-associated transcript 1

long non-coding RNA

invasive breast carcinoma

estrogen receptor

cancer susceptibility candidate

Breast cancer (BC) is one of the most dominant causes of death in women. In accordance with the American Cancer Society (ACS), BC itself accounts for about 30% of all recently-identified female cancers, and 15% of cancer- related deaths (1). BC can be either invasive or non-invasive based on its molecular characterization while 90–95% of all breast cancer cases belong to the former subgroup (2). Among the two main molecular subtypes of invasive breast cancer (IBC), nearly 80% is accounted for invasive ductal carcinoma (IDC) and around 10% for invasive lobular carcinoma (ILC) (3). Non-invasive BC also called in situ or pre-cancer, usually remains between the ducts or milk lobules of the breast without growing in the normal cells whereas for IBC the normal cells of breast also become affected through lymph system and blood stream (4). That is why, IBC is a heterogeneous anomaly which causes the constitutional alteration in tumor cells that develops secondary tumor (5). To manage the BC patients, the present bottleneck is to diagnose the disease and identify the appropriate treatment on earlier stage (6). In this case, the biomarkers can serve as a clinical and diagnostic tool and play a significant role to figure out the biological processes i.e., cancer effectively (7). Therefore, to identify the BC in the initial stages the importance of biomarkers is increasing instead of the conventional diagnostic tools (8). Estrogen receptor (ER), progesterone receptor (PR), and human epidermal receptor protein (HER2) are well known hormonal biomarkers for IBC (9). Beyond these hormonal biomarkers various new biomarkers have been reported for their predicting and identifying ability to the IBC which are ubiquinone oxidoreductase core subunit S3 (NDUFS3) (10), RNA polymerase III specific TFIIIB subunit BRF2 (11), nerve growth factor (p75NGFR) (12), microRNA‑320a (miR‑320a) (13), heterogeneous nuclear ribonucleoproteins (hnRNPs) (14) etc.

Long non-coding RNAs (lncRNA), comprising more than 200 nucleotides, are known to control cell cycle regulation, lipid metabolism, cell differentiation and innate immune response (15). Although there is lack of information regarding the number of molecular subtypes of lncRNAs, it is clear that the major portions belongs to “genic” (overlap a protein-coding transcript) and “interogenic” (unlikely to overlap a protein-coding transcript) in human (16). Besides, open reading frames (ORFs), which monitor the protein coding gene expression (both through cis and trans mechanism), is absent within these ncRNAs (17). lncRNAs may accelerate the transcription by attaching with the transcriptional factors (18). On the other hand, they can also play negative role in transcription process by impeding the transcription initiation, elongation, or termination of another gene. Moreover, lncRNAs hinder the transcription factors to commute in the cytoplasm aiming to restrain them from targets (18). Therefore, diversity in mode of action of the lncRNAs indicates that these are the indispensable controller of transcription (17)(18).

There is a scientific connection between lncRNAs and cancer (19). lncRNAs are responsible for cancer development for their abnormal expression and act as oncogene in BC (20). As example, HOTAIR and BC-200 (brain cytoplasmic RNA 1) are two well-known lncRNAs which have significant role in BC progression as well as are prominent biomarker. HOTAIR, situated at the HOX loci which experiences deregulation at the time of BC development, shows overexpression in primary BC which play an indicator for metastatic BC along with death. Another lncRNA, BC200 although reveals in neurons rather than somatic cell, shows overexpression in various cancers with BC. Moreover, it is highly identical in IBC than normal tissues and benign tumor. Eventually it is clear that, lncRNAs act as prognostic biomarker in invasive breast carcinoma (18).

In this study, we explored the role of prostate cancer-associated transcript 1 (PCAT1) in IBC. PCAT1 previously reported to upregulated proliferation, migration, invasion, metastasis, and apoptosis resistance in BC (21). The mode of action of PCAT1 in cancer development demonstrate the involvement of several pathways including, i) participation of PCAT1 in double-stranded DNA break (DSBs) repair through regulation of BRCA2, ii) impairment of post-translational modifications by the complex of PCAT1 and polycomb repressive complexes 2 (PRC2), iii) interlinkage of PCAT1 with c-MYC, iv) association of PCAT1 with single nucleotide polymorphisms (SNPs), and v) inhibition of microRNA to their targets molecules (22). The expression analysis of PCAT1 identified the overexpression of PCAT1 in patient with BC (21). Moreover, up-regulated PCAT1 exacerbated hypoxia-associated BC advancement by interaction with the receptor of activated protein C kinase-1 (RACK1) (23). In hepatocellular carcinoma, PCAT1 was significantly up-regulated, and may act as biomarker (24)(25)(26). In colorectal cancer, it was also found to be significantly up-regulated in tumor tissue (~ 64%) compared to normal tissue (27). PCAT1 expression was also studied in the gastric cancer and a significant overexpression was reported in cancer tissues compared with extension of adjacent non-tumor tissues (28)(29). In addition, PCAT1 was found overexpressed in esophageal cancer (30)(31), osteosarcoma (32)(33), non-small cell lung cancer (34), bladder cancer (35), cervical cancer (36), and multiple myeloma (37). PCAT1 also enhanced the development of osteosarcoma through miR-508-3p/ZEB1 (38) and foster the proliferation of prostate cancer cell by regulating the BRCA2 tumor suppressor protein negatively (37). In addition, PCAT1 induced the development of cholangiocarcinoma by sponging miR-216a-3p (39) as well as developed clear cell renal cell carcinoma through up regulation of YAP by miR-656 and miR-539 (40). Moreover, PCAT1/miR-129/ABCB1 axis has been reported to have chemo- resistance ability in non-small cell lung cancer. In this study, we tried to analysis the prognostic potential of PCAT1 in IBC by patient data derived meta-analysis. As a lncRNA, our findings demonstrated a potential prognostic ability of PCAT1 and proposed its clinical importance to become a biomarker for IBC.

2.1 Genetic alterations and expression analysis of PCAT1 gene in multiple cancers: We used cBioportal to study genetic alteration of PCAT1. The PCAT1 had genetically altered in 13.38% of IBC patients when investigated through pancancer atlas dataset of 32 different cancers (Fig. 2A). A significant copy number alteration (CNA) i.e., amplification has noticed throughout different cancers data set. We have further utilized GEPIA to explore the transcriptional expression of PCAT1 in BARCA mutated BC patients. Data revealed that PCAT1 highly expressed (1.56-fold; BRCA- 0.14, normal cell- 0.09) compared to normal cells (Fig. 2 B). Based on these findings we considered PCAT1 gene for our analysis in IBC.

2.2 Genetic alteration of PCAT1 in IBC: We explored 12 studies (4807 samples) (Fig. 3A) of IBC of cBioportal which were CPTAC (41), METABRIC (42), Provisional (43), TCGA (44), INSERM (45), MSK (46), SMC 2018 (47), British Columbia (48), MSKCC (49), British Columbia (50), Broad (51), and Sanger (52) to figure out the percentage of patient having PCAT1 alteration. We found that (Fig. 3A), PCAT1 amplified in considerable number of patients in first 5 studies which were 35.25%, 24.44%, 21.52%, 19.29%, and 17.5% respectively. Besides, in Oncoprint data of 12 studies, (Fig. 3B) PCAT1 gene showed amplification in 20% of IBC patients. Among first 5 studies of (Fig. 3A), we considered 2 studies i.e., METABRIC (42) and CPTAC (41) (2,631 samples) where patients were diagnosed for primary BC (42) (41). Besides, we investigated INSERM (45) data which considered only metastatic tumor (45). Therefore, we intended to find out the genetic alteration, survival rate, clinical outcomes, and tendency of co-occurrence of PCAT1 with other oncogenes in patients with primary and metastatic BC.

2.3 PCAT1 gene mainly amplified in primary and metastatic BC: It was observed that, according to the CPTAC (41), and METABRIC (42) the PCAT1 gene experienced amplification in 35% (Fig. 4A) and 24% (Fig. 4B) of primary BC patients respectively. On top of that, considering these 2 studies (Fig. 4C) at a time we found amplification of PCAT1 in 25% of patients. Besides, 18% of metastatic BC patients showed amplification (Fig. 4D) during analyzing the INSERM (45) data set. Besides the Z-score of mRNA expression data (Fig. 4E) represented significant (p- 1.717e^-3) relation with CNA of PCAT1 from CPTAC (41), and METABRIC (42). Moreover, INSERIM (45) data represented the CNA of PCAT1 among patients with metastatic BC (Fig: 4F).

2.4 Survival rate decreased in patients, diagnosed for primary BC, with PCAT1 gene alteration: The patients who were suffering from primary BC with amplified PCAT1 gene had median survival of 132.33 months (p value 5.539e-5) as compared to median survival (167.93 months) of patients without PCAT1 alteration (Fig. 5A). This data indicates that the amplification of PCAT1 significantly reduced the overall survival rate of patients with primary BC. From Kaplan–Meier plot, we also found the downward trend of survival rate with progression of months while PCAT1 overexpressed (Fig. 5B). However, the survival rate in case of metastatic BC was not investigated in INSERM (45) data set.

2.5 PCAT1 gene expression is related with ER, PR, and HER2 expression of patients: When we explored the clinical attributes from cBioportal we figured out that the PCAT1 significantly altered in PR (p- 1.672e^-6) and HER2 (p-3.190 e^-6) negative as well as ER (p- 3.920e^-4) positive primary BC samples (Fig. 6A-6C). However, these clinical attributes, in case of metastatic tumor, were not present in INSERM Cancer (45) dataset. After understanding the relation of PCAT1 expression which co-occurs significantly (p- <0.001) with Estrogen Receptor 1 (ESR1) (Fig. 6D), we then intended to check how this lncRNA related with patient age and histologic grade.

2.6 PCAT1 did not correlated with patient age while showed positive correlation with Neoplasm histologic grade of tumor: While investigation of the primary BC data set was conducted using cBioportal, there is no significant correlation of PCAT1 expression with patient age was detected (Fig. 7A). However, this relation was not evaluated in INSERM (45) dataset. In addition, we analyzed the correlation between the PCAT1 expressions with neoplasm histologic grade 1, 2 and 3 which can differentiate between normal and cancerous cells. Moreover, it is also found that, in primary BC samples with different grade of tumor, the PCAT1 amplified significantly (68.42%, p- >10^-10) in grade 3 cancer compared to grade 2 and 1 tumor (28.85% and 2.73% expression, respectively; Fig. 7B). These results indicated that the PCAT1 gene altered mostly in the grade 3 histological grade of cancer cells (Fig. 7C-7F). We also investigated the relation of ER expression with neoplastic histologic grade of primary BC from CPTAC (41) and METABRIC (42). Our findings showed that, in primary BC samples with grade 3, 2 and 1 tumor, the ESR1 amplified significantly (p- 1.985 e-3) in 45.08 %, 37.40% and 7.77 % samples respectively (Fig. 7G). These results indicated that, as like PCAT1, ER also altered in the highest percentage in samples with grade 3 cancer cells. This finding supported the notion of the co-occurrence of PCAT1 and ER in IBC.

2.7 PCAT1 gene amplification co-occurs with CASC8, CASC11, and CASC19 oncogenes: To find out the co-occurrence of PCAT1 expression with other oncogenes, we analyzed the cBioportal for the genes of highest genetic alteration (considering genes with most significant p value) in primary and metastatic BC patients. Our finding demonstrated that CASC8 altered in both primary and metastatic BC patients (~100%) (Fig. 8A, Fig. 8B). Besides in primary BC patients the anther two sub-groups of CASC family (CASC11 and CASC19) showed the highest alteration (100%). Moreover, genetic amplification of PCAT1 (25%), CASC8 (26%), CASC11 (24%), and CASC19 (23%) was observed from OncoPrint with the same data set of primary BC (Figure 9A). Similarly, we explored alteration of CASC8 (Fig. 9B) for metastatic cancer patients where the percentage for PCAT1 and CASC8 was 18% and 19% respectively. Concurrent genetic alteration of genes indicates that they may come up with the distinctive cancer subtypes (11). To identify mutual exclusivity and co-occurrence of CASC subfamily (CASC8, CASC11 and CASC19) and PCAT1 gene we queried the same data set of primary and metastatic BC from cBioportal. CASC8, CASC11 and CASC19 are previously known to have relation with BC as well as works as biomarker for different types of cancers including colorectal, esophageal (53)(54)(55)(56).The CASC8, CASC11, and CASC19 significantly (p- < 0.001) co-occurred with PCAT1 (Fig. 9C) in primary BC. Likely, the CASC8 co-occurs significantly (p value < 0.001) with PCAT1 in metastatic BC (Fig. 9D). In addition, we investigated the overall survival of primary BC patients with concurrent alteration in PCAT1, CASC8, CASC11, and CASC19 (Fig. 9E). The patients with alteration of these genes (Fig. 9E) have median survival of 125.60 months (p- 5.8e-6) as compared to median survival (168.97 months) of patients without alteration of genes. Therefore, this data indicated that the co-occurring alteration of PCAT1 with CASC8, CASC11, and CASC19 significantly reduced the overall survival rate of patients with BC.

To figure out the genome location of PCAT1, CASC8, CASC11, and CASC19 we used the Integrative Genomics Viewer (IGV) data (Fig. 10A-10D) and found that these were located in same chromosome (chr8q24) which is known to have BC risk region (57). Besides, we queried the Ensembl data, where we extracted a graph for PCAT1 location which indicates the overlapping tendency of PCAT1 with CASC8 and CASC19 together (Fig. 10E). Moreover, ENCOR data set (https://starbase.sysu.edu.cn/) was used for the analysis of the co-expression of PCAT1 with CASC8, CASC11, and CASC19. The finding represented the significant co-expression of PCAT1 with CASC8 (Fig. 11A, p value 1.04e-19), CASC11 (Fig. 11B; p- 4.57e-17), and CASC19 (Fig. 10C, p value 8.91e-44). These investigations concluded an interesting result of co-occurrence of PCAT1 with other three oncogenes and biomarker of cancers (CASC8, CASC11, and CASC19). So, the PCAT1 has the highest possibility to act as prognostic biomarker in IBC.

Cancer is fetal disease from the previous era to present time in accordance with the cancer statistics (1)(58). The development of invasive cancers resulted from alterations of gene and eventually changes of genetic expression. Moreover, the genetic expression of transcriptome is different in every stage of BC which is the main cause of invasiveness as well as metastasis (59). This interrelation of alternative expression of gene within various cases characterizes the prognostic biomarkers (59). PCAT1 is a lncRNA which is responsible for developing cancers through various mechanisms. Deviated expression of PCAT1 is known to have relation with carcinogenesis like prostate cancer, BC, hepatocellular carcinoma, colorectal cancer, gastric cancer, oesophageal cancer, osteosarcoma, non-small cell lung cancer, bladder cancer, cervical cancer, multiple myeloma etc. (22). In this meta-analysis, we intended to analysis PCAT1 to find out the expression, genetic alteration, correlation with CASC family members to prove the possibility as prognostic biomarker in IBC using various genomic tools. Through analysis the TCGA pancancer atlas we found out that within a considerable percentage of patients, with IBC, PCAT1 experienced amplification. According to GEPIA dataset, the expression of PCAT1 in BRCA mutated BC cells was higher than the normal cells. However, like other cancers, the overexpressed PCAT1 gene is known to cause the proliferation, migration, invasion, metastasis, and apoptosis resistance in BC indicating the worse outcome of patients (21). In our analysis, we explored the percentage of alteration of PCAT1 in IBC patients using cBioportal data analysis where significant amplification has been identified in five studies which include both primary and metastatic cancer patients. Therefore, amplification of PCAT1 is not only the variables which responsible for the total process of IBC development. In accordance to the PCAT1 gene status, it may be used as biomarker for BC before metastasis stage (21). Therefore, we analyzed the patients with primary BC from CPTAC (41) and METABRIC (42) for genetic alteration of PCAT1, overall survival rate, correlation with clinical outcomes like ER, PR, HER2 status, neoplasm histologic grade, and age of patients. Moreover, we also found out the genetic alteration in patients with metastatic BC from INSERM (45). We also used Human protein atlas data set (60) to represent the histopathology of three grades of IDC cell.

The PCAT1 amplified in considerable number of patients with primary BC and the overall survival rate significantly decreased in patients with altered PCAT1 gene. Besides, there is no significant correlation regarding age of patients of altered and unaltered group while PCAT1 alteration was significantly related with different grade of tumors where the highest number of patients with grade 3 tumors showed PCAT1 alteration. Three grades of cancer cells named by grade 1, grade 2 and grade 3 can distinguish between normal and cancerous cells. There is less difference between the grade 1 (grows slowly) cancer cell and normal cell and in case of grade 2 (grows faster than grade 1) the difference is higher than grade 1. However, the grade 3 (expands more destructively) cancerous cell shows highest difference between normal and cancer cells (4). PCAT1 alternation has been reported to have correlation with pathological grade and tumor size in BC patients through in vivo study (23). Additionally, in our analysis, the expression of PCAT1 was significantly higher in patients with ER positive, PR, and HER2 negative patients where PCAT1 showed significant co-occurrence tendency with alteration of ER. Moreover, the PCAT1 revealed considerable amplification in patients with metastatic BC from cBioportal.

We also investigated the correlation and co-occurrence of PCAT1 expression with three genes of cancer susceptibility candidate (CASC) family named CASC8, CASC11, and CASC19 which are known to have relation with BC and act as biomarker of different cancers (53) (54) (55) (56). From oncoprint analysis we found that considerable percentage of patients with primary BC experienced amplification of CASC8, CASC11, and CASC19 with PCAT1 analyzing same dataset. Likely the percentage was considerable regarding patients of metastatic BC having amplification of PCAT1 and CASC8. Moreover, PCAT1 had tendency to co-occur significantly (p- <0.001) with CASC8, CASC11, and CASC19 in primary cancer as well as CASC8 in metastatic BC. The overall survival rate in primary BC decreased significantly (p- 5.899e-6) having amplification of mentioned multiple genes. CASC8 is another lncRNA located in the 8q24 region and performs crucial role in the cMyc regulation. Here, the single nucleotide polymorphisms (SNPs) like rs7837328, rs6983267, and rs7014346 are mainly responsible for various cancers development e.g., prostate, breast, colorectal, and gastric cancers (53) (54). CASC11 and CASC19 are also located in the same chromosomal region (8q24) like CASC8 and show identical mechanism for BC progression (55) (56) Interestingly, the location of PCAT1 is also identified in the region of 8q24 of chromosome (35). Previous study revealed that risk loci for different epithelial cancers (i.e., colon, breast, and prostate) are situated in 8q24 region and various enhancer elements of these risk loci interact with MYC resulting in cancer development (57). Besides, Ensembl data showed the overlapping of PCAT1 gene with CASC8 and CASC19 genes. In addition, the expression of PCAT1 and CASC8, CASC11, and CASC19 co-occurred according to the ENCORI data. Considering these findings, it can be stated that PCAT1 has potential to become a prognostic biomarker for IBC patients and further studies require studying the role of PCAT1 in BC progression and its co-occurrence with family members of CASC family.

4.1 Search and inclusion/exclusion criteria: cBioportal, GEPIA, STRING, PhosphoSitePlus, IGV, GENEMENIA, Ensembl, ENCORI genomic web portal were used for this meta-analysis. Among these, we further selected the 5 web genomic portals to extract the data for further completion of this studies. The inclusion criteria were: 1) availability of information regarding the PCAT1 gene, and 2) availability of the required studies. The exclusion criteria were 1) unavailability of information regarding the PCAT1 gene, and 2) irrelevant data. After selection of databases the datasets were checked for statistical significance.

4.2 TCGA Pancancer Atlas and cBioPortal gene alteration analyses: cBioPortal is maintained by Cancer computational biologists and bioinformatics experts (11). We initially considered 12 studies (4807 samples) from cBioPortal (https://www.cbioportal.org/) to look at PCAT1 alterations in the IBC named Breast cancer (CPTAC, Cell 2020) (41), Breast Cancer (METABRIC, Nature 2012 & Nat Commun 2016) (42), The Metastatic Breast Cancer Project (Provisional, February 2020) (43), Breast Invasive Carcinoma (TCGA, cell 2015) (44), Metastatic Breast Cancer (INSERM,PLoS Med 2016) (45), Breast cancer (MSK, Clinical Cancer Res 2020) (46), Breast Cancer (SMC 2018) (47), Breast Cancer Xenografts (British Columbia, Nature 2015) (48), Breast Cancer (MSKCC, NPJ Breast Cancer 2019) (49), Breast Invasive Carcinoma (British Columbia, Nature 2012) (50), Breast Invasive Carcinoma (Broad, Nature 2012) (51), and Breast Invasive Carcinoma (Sanger, Nature 2012) (52). In the following step, we performed a comprehensive analysis using CPTAC (41) and METABRIC (42) (includes 2,631 samples). These studies included the patients suffering from primary BC and reported copy number alteration (CNA), gene expression as well as long-term clinical follow-up data (42)(61)(62)(41). Moreover, we explored INSERM (45) data set (where whole-exome sequencing was used to identify the metastasis) (includes 216 patients) for analysis of patients with metastatic cancer (45). In our current analysis of PCAT1 alterations in IBC, the frequency of genetic alterations, overall survival rate, ER, PR, and HER2 status with PCAT1 alteration, relation of age of patient and PCAT1 alteration, co-occurrence of PCAT1 gene with other oncogenes were assessed using the database of cBioPortal for Cancer Genomics.

4.3 GEPIA dataset for gene expression analysis: Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/index.html) is an interactive web server for estimating mRNA expression data based on 9,736 tumors and 8,587 normal samples in the Cancer Genome Atlas (TCGA) and Genotype-tissue Expression dataset projects. GEPIA has key interactive and customizable functions, which include differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection, and dimensionality reduction analysis (63). We used GEPIA for the gene expression profile across all tumor samples and paired normal tissues (Bar plot) to find out the expression of PCAT1 gene in different tumor samples.

4.4 Integrative Genomics Viewer to determine gene location: IGV (https://igv.org/app/) is genome-based database which involves various information from huge genomic data. The data includes sequences of exome and genome, expression profile of coding and non-coding RNAs, outlining of copy number etc. This database assists the researcher to understand the genome extensively and make a connection with various diseases (64). Here, we utilized this data set to figure out the genomic location of PCAT1, CASC8, CASC11, and CASC19 genes

4.5 Ensembl to find out gene location: Ensembl (https://www.ensembl.org) is a database which includes annotation of genome and capable of fusing and summarizing exploratory data with subject genomes. It has three functions which are annotation of the vertebrate subphylum, interpretation of genome, and performing the analysis as per researcher requirement (65). Using this data set we analyzed the genes which overlap with PCAT1 gene.

4.6 ENCORI data set lncRNA-RNA interaction: ENCORI (https://starbase.sysu.edu.cn/index.php) previously known as starBase v2.0, indicates the interaction of RNA–RNA (sncRNA-RNA, lncRNA-RNA, mRNA-RNA) and protein-RNA interaction networks), pancancer analysis, miRNA-target interaction gathered from 37 individual studies (66). In this analysis, we used this database to analysis the co-expression of PCAT1 with CASC8, CASC11 and CASC19.

4.7 Human protein atlas to analysis the histopathology: Human protein atlas (https://www.proteinatlas.org/ ) project started from 2003 mainly focuses to depict all the human proteins in cells, tissues, and organs through combination of various omics tools, together with antibody-based imaging, mass spectrometry-based proteomics, transcriptomics, and systems biology. It includes ten different parts which focus on a specific feature of the genome-wide exploration of the human proteins(60). We explored this program to figure out the histopathology of grade 1, grade 2, grade 3 IDC cells.

PCAT1

Prostate cancer-associated transcript 1

LncRNA

Long non-coding RNA

IBC

Invasive breast cancer

Breast cancer

ACS

American Cancer Society

IDC

Invasive ductal carcinoma

ILC

Invasive lobular carcinoma

Estrogen receptor

Progesterone receptor

HER2

Human epidermal receptor

CNA

Copy number alteration

ESR1

Estrogen Receptor 1

IGV

Integrative Genomics Viewer

FUNDING

This research article is not funded from any source.

DECLARATION OF COMPETING INTEREST

The authors have declared that they have no competing financial interest or personal relationship that could appear to influence the work outlined in this article.

AUTHOR’S CONTRIBUTIONS

BC conducted the detail analysis and drafted the article. KA performed the initial analysis and organized the figures. MAS conceptualized, supervised, reviewed, and finalized the manuscript. All authors have read and agreed to the published version of the manuscript.

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A meta-analysis of PCAT1; prostate cancer-associated transcript 1 gene as a prognostic biomarker for invasive breast carcinoma

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