Upregulation of LINC00511 expression by DNA hypomethylation promotes the progression of breast cancer

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Breast cancer is the most common malignancy in women around the world. Intratumor and intertumoral heterogeneity persist in mammary tumors. Therefore, the identification of biomarkers is essential for the treatment of this malignancy. This study analyzed 28,143 genes expressed in 49 breast cancer cell lines using a Weighted Gene Co-expression Network Analysis to determine specific target proteins for Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes. Sixty-five modules were identified, of which five were characterized as having a high correlation with breast cancer subtypes. Genes overexpressed in the tumor were found to participate in the following mechanisms: regulation of the apoptotic process, transcriptional regulation, angiogenesis, signaling, and cellular survival. In particular, we identified the following genes, considered as hubs: IFIT3, an inhibitor of viral and cellular processes; ETS1, a transcription factor involved in cell death and tumorigenesis; ENSG00000259723 lncRNA, expressed in cancers; AL033519.3, a hypothetical gene; and TMEM86A, important for regulating keratinocyte membrane properties, considered as a key in Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes, respectively. The modules and genes identified in this work can be used to identify possible biomarkers or therapeutic targets in different breast cancer subtypes.

Correlation analysis of disulfidptosis-related gene signatures with clinical prognosis and immunotherapy response in sarcoma

Disulfidptosis, a newly discovered type of programmed cell death, could be a mechanism of cell death controlled by SLC7A11. This could be closely associated with tumor development and advancement. Nevertheless, the biological mechanism behind disulfidptosis-related genes (DRGs) in sarcoma (SARC) is uncertain. This study identified three valuable genes (SLC7A11, RPN1, GYS1) associated with disulfidptosis in sarcoma (SARC) and developed a prognostic model. The multiple databases and RT-qPCR data confirmed the upregulated expression of prognostic DRGs in SARC. The TCGA internal and ICGC external validation cohorts were utilized to validate the predictive model capacity. Our analysis of DRG riskscores revealed that the low-risk group exhibited a more favorable prognosis than the high-risk group. Furthermore, we observed a significant association between DRG riskscores and different clinical features, immune cell infiltration, immune therapeutic sensitivity, drug sensitivity, and RNA modification regulators. In addition, two external independent immunetherapy datasets and clinical tissue samples were collected, validating the value of the DRGs risk model in predicting immunotherapy response. Finally, the SLC7A11/hsa-miR-29c-3p/LINC00511, and RPN1/hsa-miR-143-3p/LINC00511 regulatory axes were constructed. This study provided DRG riskscore signatures to predict prognosis and response to immunotherapy in SARC, guiding personalized treatment decisions.

The Intergenic Type LncRNA (LINC RNA) Faces in Cancer with In Silico Scope and a Directed Lens to LINC00511: A Step toward ncRNA Precision

BACKGROUND

Long intergenic non-coding RNA, is one type of lncRNA, exerting various cellular activities, as does ncRNA, including the regulation of gene expression and chromatin remodeling. The abnormal expression of lincRNAs can induce or suppress carcinogenesis.

MAIN BODY

LincRNAs can regulate cancer progression through different mechanisms and are considered as potential drug targets. Genetic variations such as single nucleotide polymorphisms (SNPs) in lincRNAs may affect gene expression and messenger ribonucleic acid (mRNA) stability. SNPs in lincRNAs have been found to be associated with different types of cancer, as well. Specifically, LINC00511 has been known to promote the progression of multiple malignancies such as breast cancer, colorectal cancer, lung cancer, hepatocellular carcinoma, and others, making it a promising cancer prognostic molecular marker.

CONCLUSION

LincRNAs have been proved to be associated with different cancer types through various pathways. Herein, we performed a comprehensive literature and in silico databases search listing lncRNAs, lincRNAs including LINC00511, lncRNAs' SNPs, as well as LINC00511 SNPs in different cancer types, focusing on their role in various cancer types and mechanism(s) of action.

A review on the role of LINC00511 in cancer

Long Intergenic Non-Protein Coding RNA 511 (LINC00511) is an RNA gene being mostly associated with lung cancer. Further assessments have shown dysregulation of this lncRNA in a variety of cancers. LINC00511 has interactions with hsa-miR-29b-3p, hsa-miR-765, hsa-mir-150, miR-1231, TFAP2A-AS2, hsa-miR-185-3p, hsa-miR-29b-1-5p, hsa-miR-29c-3p, RAD51-AS1 and EZH2. A number of transcription factors have been identified that regulate expression of LINC00511. The current narrative review summarizes the role of LINC00511 in different cancers with an especial focus on its prognostic impact in human cancers.

NLR family CARD domain containing 5 promotes hypoxia-induced cancer progress and carboplatin resistance by activating PI3K/AKT via carcinoembryonic antigen related cell adhesion molecule 1 in non-small cell lung cancer

It is well known that non-small cell lung cancer (NSCLC) is a malignant tumor with high incidence in the world. We aimed to clarify a possible target and identify its precise molecular biological mechanism in NSCLC. NLR family CARD domain containing 5 (NLRC5) is widely expressed in tissues and exerts a vital role in anti-tumor immunity. We determined NLRC5 expression by RT-qPCR and western blot assay. The role of NLRC5 in the development of NSCLC was assessed by a loss-of-function assay. CCK-8, Annexin-V-FITC/PI Apoptosis Detection Kit, Transwell, and wound healing assays were used to determine the cell functions. Drug resistance-related proteins were analyzed by western blot assay. Furthermore, the modulation of NLRC5 on carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) expression and subsequent PI3K/AKT signaling was assessed. In this study, a hyper-expression of NLRC5 was found in NSCLC tissues and cell lines. Knockdown of NLRC5 suppressed cell viability, invasion, and migration, and furthermore promoted cell apoptosis in NSCLC cells. Moreover, under normoxia or hypoxia treatment, the upregulation of NLRC5 was related to carboplatin resistance. NLRC5 silencing increased carboplatin-resistant cell chemosensitivity, as evidenced by the increase in the cell inhibition rate and decrease in drug resistance-related protein expression. Mechanistically, NLRC5 knockdown inhibited the expression of CEACAM1 and subsequently blocked the PI3K/AKT signaling pathway. In conclusion, NLRC5 promotes the malignant biological behaviors of NSCLC cells by activating the PI3K/AKT signaling pathway via the regulation of CEACAM1 expression under normoxia and hypoxia.

Molecular Classification of Breast Cancer Utilizing Long Non-Coding RNA (lncRNA) Transcriptomes Identifies Novel Diagnostic lncRNA Panel for Triple-Negative Breast Cancer

Simple Summary

Breast cancer is the most commonly diagnosed cancer in women today and accounts for thousands of cancer-related deaths each year. While some breast cancer subtypes can be easily diagnosed and targeted for therapy, triple-negative breast cancer, which lacks receptor expression, is the most challenging to diagnose and treat. In this study, we use multiple RNA sequencing data to look specifically at long non-coding RNA (lncRNA) expression portraits at the transcript level and to identify lncRNA-based biomarkers associated with each breast cancer subtype. Receiver operating characteristic (ROC) analysis was used to validate their diagnostic potential, which was validated in two independent cohorts. Several lncRNA transcripts were found to be enriched in TNBC across all validation cohorts. Binary regression analysis identified a four lncRNA transcript signature with the highest diagnostic power for TNBC as potential novel biomarkers for diagnostic and therapeutic intervention. Interestingly, several of the identified lncRNAs were shown to have prognostic potential in TNBC.

Abstract

Breast cancer remains the world’s most prevalent cancer, responsible for around 685,000 deaths globally despite international research efforts and advances in clinical management. While estrogen receptor positive (ER+), progesterone receptor positive (PR+), and human epidermal growth factor receptor positive (HER2+) subtypes are easily classified and can be targeted, there remains no direct diagnostic test for triple-negative breast cancer (TNBC), except for the lack of receptors expression. The identification of long non-coding RNAs (lncRNAs) and the roles they play in cancer progression has recently proven to be beneficial. In the current study, we utilize RNA sequencing data to identify lncRNA-based biomarkers associated with TNBC, ER+ subtypes, and normal breast tissue. The Marker Finder algorithm identified the lncRNA transcript panel most associated with each molecular subtype and the receiver operating characteristic (ROC) analysis was used to validate the diagnostic potential (area under the curve (AUC) of ≥8.0 and p value < 0.0001). Focusing on TNBC, findings from the discovery cohort were validated in an additional two cohorts, identifying 13 common lncRNA transcripts enriched in TNBC. Binary regression analysis identified a four lncRNA transcript signature (ENST00000425820.1, ENST00000448208.5, ENST00000521666.1, and ENST00000650510.1) with the highest diagnostic power for TNBC. The ENST00000671612.1 lncRNA transcript correlated with worse refractory free survival (RFS). Our data provides a step towards finding a novel diagnostic lncRNA-based panel for TNBC with potential therapeutic implications.