Expression and Function of C1orf132 Long-Noncoding RNA in Breast Cancer Cell Lines and Tissues

Shafaroudi, Afsaneh Malekzadeh; Sharifi‐Zarchi, Ali; Rahmani, Saeid; Nafissi, Nahid; Mowla, Seyed Javad; Lauria, Andrea; Oliviero, Salvatore; Matin, Maryam Moghaddam

doi:10.3390/ijms22136768

Cited by 11 publications

(7 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…COX regression was used to construct the prognostic risk model. The value of MIR29B2CHG (C1orf132) was significantly attenuated in BC ( 30 ). NEAT1 expression was elevated in BC samples, and the increased expression of NEAT1 was associated with a poor overall survival in BC patients ( 31 ).…”

Section: Discussionmentioning

confidence: 99%

Construction of ceRNA Networks Associated With CD8 T Cells in Breast Cancer

et al. 2022

View full text Add to dashboard Cite

BackgroundThe infiltration of CD8 T cells is usually linked to a favorable prognosis and may predict the therapeutic response of breast cancer patients to immunotherapy. The purpose of this research is to investigate the competing endogenous RNA (ceRNA) network correlated with the infiltration of CD8 T cells.MethodsBased on expression profiles, CD8 T cell abundances for each breast cancer (BC) patient were inferred using the bioinformatic method by immune markers and expression profiles. We were able to extract the differentially expressed RNAs (DEmRNAs, DEmiRNAs, and DElncRNAs) between low and high CD8 T-cell samples. The ceRNA network was constructed using Cytoscape. Machine learning models were built by lncRNAs to predict CD8 T-cell abundances. The lncRNAs were used to develop a prognostic model that could predict the survival rates of BC patients. The expression of selected lncRNA (XIST) was validated by quantitative real-time PCR (qRT-PCR).ResultsA total of 1,599 DElncRNAs, 89 DEmiRNAs, and 1,794 DEmRNAs between high and low CD8 T-cell groups were obtained. Two ceRNA networks that have positive or negative correlations with CD8 T cells were built. Among the two ceRNA networks, nine lncRNAs (MIR29B2CHG, NEAT1, MALAT1, LINC00943, LINC01146, AC092718.4, AC005332.4, NORAD, and XIST) were selected for model construction. Among six prevalent machine learning models, artificial neural networks performed best, with an area under the curve (AUC) of 0.855. Patients from the high-risk category with BC had a lower survival rate compared to those from the low-risk group. The qRT-PCR results revealed significantly reduced XIST expression in normal breast samples, which was consistent with our integrated analysis.ConclusionThese results potentially provide insights into the ceRNA networks linked with T-cell infiltration and provide accurate models for T-cell prediction.

show abstract

Section: Discussionmentioning

confidence: 99%

Construction of ceRNA Networks Associated With CD8 T Cells in Breast Cancer

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The miRNAs misregulated in obesity and Several lncRNAs have been found misregulated in breast cancer tissues, highlighting the diversity of genes that can contribute to disease progression. For instance, lncRNAs FGD5-AS1, NEAT1, KCNQ1OT1, HCG18, TTN-AS1, HCG18, OIP5-AS1, and CCDC18-AS1 have been detected as upregulated [157][158][159][160][161][162][163], while a downregulation was reported for miR29B2CHG and GAS5 [164,165]. In lung carcinomas, differential expression of lncRNAs has been observed depending on the cancer type.…”

Section: Discussionmentioning

confidence: 99%

miRNAs as Interconnectors between Obesity and Cancer

González-Sánchez,

Granados-López,

López-Hernández

et al. 2024

ncRNA

View full text Add to dashboard Cite

Obesity and cancer are a concern of global interest. It is proven that obesity may trigger the development or progression of some types of cancer; however, the connection by non-coding RNAs has not been totally explored. In the present review, we discuss miRNAs and lncRNAs dysregulation involved in obesity and some cancers, shedding light on how these conditions may exacerbate one another through the dysregulation of ncRNAs. lncRNAs have been reported as regulating microRNAs. An in silico investigation of lncRNA and miRNA interplay is presented. Our investigation revealed 44 upregulated and 49 downregulated lncRNAs in obesity and cancer, respectively. miR-375, miR-494-3p, miR-1908, and miR-196 were found interacting with 1, 4, 4 and 4 lncRNAs, respectively, which are involved in PPARγ cell signaling regulation. Additionally, miR-130 was found to be downregulated in obesity and reported as modulating 5 lncRNAs controlling PPARγ cell signaling. Similarly, miR-128-3p and miR-143 were found to be downregulated in obesity and cancer, interacting with 5 and 4 lncRNAs, respectively, associated with MAPK cell signaling modulation. The delicate balance between miRNA and lncRNA expression emerges as a critical determinant in the development of obesity-associated cancers, presenting these molecules as promising biomarkers. However, additional and deeper studies are needed to reach solid conclusions about obesity and cancer connection by ncRNAs.

show abstract

“…A significant decrease of MIR29B2CHG was observed in the triple negative types of breast cancer. It is a host gene for producing of miR-29b2 and miR-29c, which plays a suppressive role in the progression of breast cancer 41 .…”

Section: Discussionmentioning

confidence: 99%

Weighted correlation network analysis revealed novel long non-coding RNAs for colorectal cancer

Khameneh

Razi

Shamdani

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide, which after breast, lung and, prostate cancers, is the fourth prevalent cancer in the United States. Long non-coding RNAs (lncRNAs) have an essential role in the pathogenesis of CRC. Therefore, bioinformatics studies on lncRNAs and their target genes have potential importance as novel biomarkers. In the current study, publicly available microarray gene expression data of colorectal cancer (GSE106582) was analyzed with the Limma, Geoquery, Biobase package. Afterward, identified differentially expressed lncRNAs and their target genes were inserted into Weighted correlation network analysis (WGCNA) to obtain modules and hub genes. A total of nine differentially expressed lncRNAs (LINC01018, ITCH-IT, ITPK1-AS1, FOXP1-IT1, FAM238B, PAXIP1-AS1, ATP2B1-AS1, MIR29B2CHG, and SNHG32) were identified using microarray data analysis. The WGCNA has identified several hub genes for black (LMOD3, CDKN2AIPNL, EXO5, ZNF69, BMS1P5, METTL21A, IL17RD, MIGA1, CEP19, FKBP14), blue (CLCA1, GUCA2A, UGT2B17, DSC2, CA1, AQP8, ITLN1, BEST4, KLF4, IQCF6) and turquoise (PAFAH1B1, LMNB1, CACYBP, GLO1, PUM3, POC1A, ASF1B, SDCCAG3, ASNS, PDCD2L) modules. The findings of the current study will help to improve our understanding of CRC. Moreover, the hub genes that we have identified could be considered as possible prognostic/diagnostic biomarkers. This study led to the determination of nine lncRNAs with no previous association with CRC development.

show abstract

Expression and Function of C1orf132 Long-Noncoding RNA in Breast Cancer Cell Lines and Tissues

Cited by 11 publications

References 39 publications

Construction of ceRNA Networks Associated With CD8 T Cells in Breast Cancer

Construction of ceRNA Networks Associated With CD8 T Cells in Breast Cancer

miRNAs as Interconnectors between Obesity and Cancer

Weighted correlation network analysis revealed novel long non-coding RNAs for colorectal cancer

Contact Info

Product

Resources

About