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Expression profiles of small non-coding RNAs in breast cancer tumors characterize clinicopathological features and show prognostic and predictive potential.

ABSTRACT: Precision medicine approaches are required for more effective therapies for cancer. As small non-coding RNAs (sncRNAs) have recently been suggested as intriguing candidates for cancer biomarkers and have shown potential also as novel therapeutic targets, we aimed at profiling the non-miRNA sncRNAs in a large sample set to evaluate their role in invasive breast cancer (BC). We used small RNA sequencing and 195 fresh-frozen invasive BC and 22 benign breast tissue samples to identify significant associations of small nucleolar RNAs, small nuclear RNAs, and miscellaneous RNAs with the clinicopathological features and patient outcome of BC. Ninety-six and five sncRNAs significantly distinguished (Padj < 0.01) invasive local BC from benign breast tissue and metastasized BC from invasive local BC, respectively. Furthermore, 69 sncRNAs significantly associated (Padj < 0.01) with the tumor grade, hormone receptor status, subtype, and/or tumor histology. Additionally, 42 sncRNAs were observed as candidates for prognostic markers and 29 for predictive markers for radiotherapy and/or tamoxifen response (P < 0.05). We discovered the clinical relevance of sncRNAs from each studied RNA type. By introducing new sncRNA biomarker candidates for invasive BC and validating the potential of previously described ones, we have guided the way for further research that is warranted for providing novel insights into BC biology.

SUBMITTER: Karkkainen E

PROVIDER: S-EPMC9803687 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Expression profiles of small non-coding RNAs in breast cancer tumors characterize clinicopathological features and show prognostic and predictive potential.

Kärkkäinen Emmi E Heikkinen Sami S Tengström Maria M Kosma Veli-Matti VM Mannermaa Arto A Hartikainen Jaana M JM

Scientific reports 20221230 1

Precision medicine approaches are required for more effective therapies for cancer. As small non-coding RNAs (sncRNAs) have recently been suggested as intriguing candidates for cancer biomarkers and have shown potential also as novel therapeutic targets, we aimed at profiling the non-miRNA sncRNAs in a large sample set to evaluate their role in invasive breast cancer (BC). We used small RNA sequencing and 195 fresh-frozen invasive BC and 22 benign breast tissue samples to identify significant as ...[more]

PMID: 36585466

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Project description:BackgroundLong non-coding RNAs (lncRNAs) have been implicated in diagnosis and prognosis in various cancers. However, few lncRNA signatures have been established for prediction of gastrointestinal stromal tumors (GIST). We aimed to explore a lncRNA signature profile that associated with clinical relevance by mining data from Gene Expression Ominus (GEO) and Surveillance, Epidemiology, and End Results (SEER) Program.MethodsUsing a lncRNA-mining approach, we performed non-negative matrix factorization (NMF) consensus algorithm in Gastrointestinal stromal tumors (GISTs) cohorts (61 patients from GSE8167 and GSE17743) to cluster LncRNA expression profiles. Comparative markers selection, and Gene Set Enrichment Analysis (GSEA) algorithm were performed between distinct molecular subtypes of GIST. The survival rate of GIST patients from SEER stratified by gender were compared by Kaplan-Meier method and log-rank analysis. lncRNA-mRNA co-expression analysis was performed by Pearson correlation coefficients (PCC) using R package LINC. Somatic copy number alterations of GIST patients (GSE40966) were analyzed via web server GenePattern GISTIC2 algorithm.ResultsA total of four lncRNA molecular subtypes of GIST were identified with distinct biological pathways and clinical characteristics. LncRNA expression profiles well clustered the GIST samples into small size (<5 mm) and large size tumors (>5 mm), which is a fundamental index for GIST malignancy diagnosis. Several lncRNAs with abundant expression (LRRC75A-AS1, HYMAI, NEAT1, XIST and FTX) were closely associated with tumor size, which may suggest to be biomarkers for the GIST malignancy. Particularly, LRRC75A-AS1 was positively associated with tumor diameters and suggested an oncogene in GIST. Co-expression analysis suggested that chromosome region 17p11.2-p12 may contribute to the oncogenic process in malignant GIST. Interestingly, the gender had a strong influence on clustering by lncRNA expression profile. Data from the Surveillance, Epidemiology, and End Results (SEER) Program were further explored and 7983 patients who were diagnosed with GISTs from 1973 to 2014 were enrolled for analysis. The results also showed the favorable prognosis for female patients. The survival rate between male and female with GIST was statistically significant (P < 0.0001). Gene set enrichment analysis (GSEA) indicated distinct pathways between female and male, and malignant GIST was associated with several cancer metabolism and cell cycle associated pathways.ConclusionsThis lncRNAs-based classification for GISTs may provide a molecular classification applicable to individual GIST that has implications to influence lncRNA markers selection and prediction of tumor progression.

Dataset Information

Expression profiles of small non-coding RNAs in breast cancer tumors characterize clinicopathological features and show prognostic and predictive potential.

Publications

Expression profiles of small non-coding RNAs in breast cancer tumors characterize clinicopathological features and show prognostic and predictive potential.

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