The Long Non-coding RNA LINC01705 Regulates the Development of Breast Cancer by Sponging miR-186-5p to Mediate TPR Expression as a Competitive Endogenous RNA

CircZCCHC2 decreases pirarubicin sensitivity and promotes triple-negative breast cancer development via the miR-1200/TPR axis

Triple-negative breast cancer (TNBC) has attracted attention due to its poor prognosis and limited treatment options. The mechanisms underlying the association between circular RNAs (circRNAs) and the occurrence and development of TNBC remain unclear. CircZCCHC2 is observed to be upregulated in TNBC cells, tissues, and plasma exosomes. Knockdown of circZCCHC2 inhibited the proliferation, migration, invasion, and epithelial-mesenchymal transition of TNBC cells in vitro and in vivo. Pirarubicin (THP) treatment downregulated circZCCHC2, and circZCCHC2 affected the sensitivity to THP. CircZCCHC2/miR-1200/translocated promoter region, the nuclear basket protein (TPR) pathway was cascaded and verified. It is demonstrated that circZCCHC2 plays a crucial role in the malignant progression of TNBC via the miR-1200/TPR axis, thereby activating the RAS-RAF-MEK-ERK pathway. The present results indicate that circZCCHC2 has the potential to serve as a novel prognostic biomarker for TNBC.

Construction and validation of a PANoptosis-related lncRNA signature for predicting prognosis and targeted drug response in thyroid cancer

Thyroid cancer (TC) is the most prevalent malignancy of the endocrine system. PANoptosis, a newly discovered cell death pathway, is of interest in tumor research. However, the relationship between PANoptosis-related lncRNAs (PRlncRNAs) and TC remains unclear. The study aimed to develop a prognostic model based on PRlncRNAs in TC. Gene expression data of PANoptosis-associated genes and clinical information on TC from The Cancer Genome Atlas (TCGA) database were analyzed by Pearson correlation analysis, univariate/multivariate Cox analysis, and Lasso Cox regression analysis. A PRlncRNA signature was constructed and used to develop a nomogram to predict overall survival (OS). We further explored the correlation between the risk score and tumor immune microenvironment, immune checkpoints, and drug sensitivity. Moreover, we verified the expression and biological function of lncRNAs in TC cell lines. Finally, seven PRlncRNAs were used to construct a prognostic model for predicting the OS of TC patients. We found that the risk score was associated with the tumor microenvironment (TME) and the expression of critical immune checkpoints. In addition, we screened for drugs that high- or low-risk TC groups might be sensitive to. Quantitative real-time polymerase chain reaction (qRT-PCR) results showed differential expression of four PRlncRNAs (GAPLINC, IDI2-AS1, LINC02154, and RBPMS-AS1) between tumor and normal tissues. Besides, a GEO database (GSE33630) was used to verify the expression differences of PRLncRNAs in THCA tissues and normal tissues. Finally, RBPMS-AS1 was found to inhibit the proliferation and migration of TC cells. In conclusion, we developed a PANoptosis-related lncRNA prognostic risk model that offers a comprehensive understanding of TME status in patients with TC and establishes a foundation for the choice of sensitive medications and immunotherapy.

A Novel Aging-Related Prognostic lncRNA Signature Correlated with Immune Cell Infiltration and Response to Immunotherapy in Breast Cancer

Breast cancer (BC) is among the most universal malignant tumors in women worldwide. Aging is a complex phenomenon, caused by a variety of factors, that plays a significant role in tumor development. Consequently, it is crucial to screen for prognostic aging-related long non-coding RNAs (lncRNAs) in BC. The BC samples from the breast-invasive carcinoma cohort were downloaded from The Cancer Genome Atlas (TCGA) database. The differential expression of aging-related lncRNAs (DEarlncRNAs) was screened by Pearson correlation analysis. Univariate Cox regression, LASSO-Cox analysis, and multivariate Cox analysis were performed to construct an aging-related lncRNA signature. The signature was validated in the GSE20685 dataset from the Gene Expression Omnibus (GEO) database. Subsequently, a nomogram was constructed to predict survival in BC patients. The accuracy of prediction performance was assessed through the time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, principal component analyses, decision curve analysis, calibration curve, and concordance index. Finally, differences in tumor mutational burden, tumor-infiltrating immune cells, and patients' response to chemotherapy and immunotherapy between the high- and low-risk score groups were explored. Analysis of the TCGA cohort revealed a six aging-related lncRNA signature consisting of MCF2L-AS1, USP30-AS1, OTUD6B-AS1, MAPT-AS1, PRR34-AS1, and DLGAP1-AS1. The time-dependent ROC curve proved the optimal predictability for prognosis in BC patients with areas under curves (AUCs) of 0.753, 0.772, and 0.722 in 1, 3, and 5 years, respectively. Patients in the low-risk group had better overall survival and significantly lower total tumor mutational burden. Meanwhile, the high-risk group had a lower proportion of tumor-killing immune cells. The low-risk group could benefit more from immunotherapy and some chemotherapeutics than the high-risk group. The aging-related lncRNA signature can provide new perspectives and methods for early BC diagnosis and therapeutic targets, especially tumor immunotherapy.

New Biomarkers Based on Dendritic Cells for Breast Cancer Treatment and Prognosis Diagnosis

Dendritic cells(DCs) play a protective role in the antitumor immunity of most cancers, which can be divided into conventional dendritic cells (cDCs) and plasmacytoid dendritic cells (pDCs). Most current studies are only based on either cDCs or pDCs for the study of the relationship between DCs and breast cancer prognosis, without combining the two together. We aimed to select new biomarkers from pDCs and cDCs. In this paper, the xCell algorithm was first used to calculate the cellular abundance of 64 types of immune cells and stromal cells in tumor samples from the TCGA database, and the high-abundance pDC group and cDC group were divided according to the results of a survival analysis. Then, we looked for the co-expressed gene module of highly infiltrating pDC and cDC patients with a weighted correlation network analysis (WGCNA) and screened out the hub genes, including RBBP5, HNRNPU, PEX19, TPR, and BCL9. Finally, we analyzed the biological functions of the hub genes, and the results showed that RBBP5, TPR, and BCL9 were significantly related to the immune cells and prognosis of patients, and RBBP5 and BCL9 were involved in responding to TCF-related instructions of the Wnt pathway. In addition, we also evaluated the response of pDCs and cDCs with different abundances to chemotherapy, and the results showed that the higher the abundance of pDCs and cDCs, the higher their sensitivity to drugs. This paper revealed new biomarkers related to DCs-among them, BCL9, TPR, and RBBP5 were proven to be closely related to dendritic cells in cancer. For the first time, this paper puts forward that HNRNPU and PEX19 are related to the prognosis of dendritic cells in cancer, which also provides new possibilities for finding new targets for breast cancer immunotherapy.

lncRNA WT1-AS attenuates hypoxia/ischemia-induced neuronal injury during cerebral ischemic stroke via miR-186-5p/XIAP axis

This study aimed to investigate the role and mechanism of long non-coding RNA (lncRNA) WT1 antisense RNA (WT1-AS) in cerebral ischemic stroke. The Starbase database and dual-luciferase reporter gene assay were used to analyze the interaction between lncRNA WT1 antisense RNA (lncRNA WT1-AS) and microRNA-186-5p (miR-186-5p). Reverse transcription-quantitative PCR analysis was performed to determine lncRNA WT1-AS and miR-186-5p levels. An oxygen glucose deprivation (OGD)-induced SH-SY5Y cell injury model was established. Cell viability and apoptosis were determined using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide and flow cytometric assays, respectively. Caspase 3 activity was evaluated using a caspase 3 activity detection kit. The results showed that miR-186-5p is a direct target of the lncRNA WT1-AS. In addition, lncRNA WT1-AS levels were downregulated and miR-186-5p levels were upregulated in the blood samples of patients with ischemic stroke and OGD-induced SH-SY5Y cells. WT1-AS overexpression promoted OGD-induced cell viability and reduced the cell apoptosis and caspase 3 activity. However, these effects were reversed by miR-186-5p overexpression. Furthermore, the results demonstrated that the X-linked inhibitor of apoptosis (XIAP) was directly targeted by miR-186-5p. Similarly, transfection with the miR-186-5p inhibitor reduced OGD-induced neuronal damage by upregulating XIAP expression. In conclusion, lncRNA WT1-AS attenuates hypoxia/ischemia-induced neuronal injury in cerebral ischemic stroke through the miR-186-5p/XIAP axis.

Multiple types of noncoding RNA are involved in potential modulation of PTTG1's expression and function in breast cancer

Breast cancer is a malignant type with morbidity ranking the first of women globally. As widely acknowledged, there exist close links between ncRNA-mRNA axis and breast cancer. In this study, we first overviewed expression and prognostic values of pituitary tumor transforming gene (PTTGs) in breast cancer. Next, two binding miRNAs (miR-186-5p and miR-655-3p) of PTTG1 in breast cancer were identified. Subsequently, several potential upstream ncRNAs of PTTG1-miR-186-5p/miR-655-3p axis in breast cancer were successively screened out, consisting of 11 lncRNAs, 17 circRNAs and 12 pseudogene-derived RNAs. Enrichment analysis for downstream target genes of PTTG1-miR-186-5p/miR-655-3p axis revealed that this axis is associated with TGF-beta signaling and MAPK signaling pathways. Further investigation demonstrated AURKA was one of the most key hub genes. Collectively, we established a potential PTTG1-related ncRNA-mRNA regulatory network in breast cancer.

Prognostication of Pancreatic Cancer Using The Cancer Genome Atlas Based Ferroptosis-Related Long Non-Coding RNAs

Background: Long non-coding RNAs (lncRNAs) are key regulators of pancreatic cancer development and are involved in ferroptosis regulation. LncRNA transcript levels serve as a prognostic factor for pancreatic cancer. Therefore, identifying ferroptosis-related lncRNAs (FRLs) with prognostic value in pancreatic cancer is critical.

Methods: In this study, FRLs were identified by combining The Cancer Genome Atlas (TCGA) and FerrDb databases. For training cohort, univariate Cox, Lasso, and multivariate Cox regression analyses were applied to identify prognosis FRLs and then construct a prognostic FRLs signature. Testing cohort and entire cohort were applied to validate the prognostic signature. Moreover, the nomogram was performed to predict prognosis at different clinicopathological stages and risk scores. A co-expression network with 76 lncRNA-mRNA targets was constructed.

Results: Univariate Cox analysis was performed to analyze the prognostic value of 193 lncRNAs. Furthermore, the least absolute shrinkage and selection operator and the multivariate Cox analysis were used to assess the prognostic value of these ferroptosis-related lncRNAs. A prognostic risk model, of six lncRNAs, including LINC01705, AC068620.2, TRAF3IP2-AS1, AC092171.2, AC099850.3, and MIR193BHG was constructed. The Kaplan Meier (KM) and time-related receiver operating characteristic (ROC) curve analysis were performed to calculate overall survival and compare high- and low-risk groups. There was also a significant difference in survival time between the high-risk and low-risk groups for the testing cohort and the entire cohort, with AUCs of .723, .753, respectively. Combined with clinicopathological characteristics, the risk model was validated as a new independent prognostic factor for pancreatic adenocarcinoma through univariate and multivariate Cox regression. Moreover, a nomogram showed good prediction.

Conclusion: The signature of six FRLs had significant prognostic value for pancreatic adenocarcinoma. They may be a promising therapeutic target in clinical practice.

The nomogram based on the 6-lncRNA model can promote the prognosis prediction of patients with breast invasive carcinoma

Long non-coding RNA (lncRNA) is a prognostic biomarker for many types of cancer. Here, we aimed to study the prognostic value of lncRNA in Breast Invasive Carcinoma (BRCA). We downloaded expression profiles from The Cancer Genome Atlas (TCGA) datasets. Subsequently, we screened the differentially expressed genes between normal tissues and tumor tissues. Univariate Cox, LASSO regression, and multivariate Cox regression analysis were used to construct a lncRNA prognostic model. Finally, a nomogram based on the lncRNAs model was developed, and weighted gene co-expression network analysis (WGCNA) was used to predict mRNAs related to the model, and to perform function and pathway enrichment. We constructed a 6-lncRNA prognostic model. Univariate and multivariate Cox regression analysis showed that the 6-lncRNA model could be used as an independent prognostic factor for BRCA patients. We developed a nomogram based on the lncRNAs model and age, and showed good performance in predicting the survival rates of BRCA patients. Also, functional pathway enrichment analysis showed that genes related to the model were enriched in cell cycle-related pathways. Tumor immune infiltration analysis showed that the types of immune cells and their expression levels in the high-risk group were significantly different from those in the low-risk group. In general, the 6-lncRNA prognostic model and nomogram could be used as a practical and reliable prognostic tool for invasive breast cancer.

LINC00894 Enhances the Progression of Breast Cancer by Sponging miR-429 to Regulate ZEB1 Expression

PURPOSE

Long non-coding RNAs (lncRNAs) are known to regulate tumorigenesis. Although breast cancer tissues show a high expression of LINC00894, its specific biological role in breast cancer progression is still unknown. In this study, lncRNA microarray was used to analyze the lncRNA expression in breast cancer tissues, and LINC00894 was selected for further analysis.

MATERIALS AND METHODS

Expression of LINC00894 in 45 pairs of breast cancer tissues and normal tissues obtained from patients with breast cancer was assessed by quantitative reverse transcription-PCR, while proliferation and invasion of breast cancer cells were assessed using a Cell Counting Kit-8 (CCK-8), EdU assay, colony formation experiment, and transwell assays. A dual-luciferase reporter gene assay and bioinformatics analysis were employed to detect potential targets of LINC00894. Additionally, RNA Binding Protein Immunoprecipitation (RIP) and Western blot assays were utilized to clarify its interaction and roles in the regulation of breast cancer progression.

RESULTS

High expression of LINC00894 was observed in breast cancer cells, and its overexpression significantly expedited cell proliferation and invasion. Moreover, LINC00894 positively regulated the expression of ZEB1 by competitively binding to miR-429.

CONCLUSION

Taken together, these results suggest that LINC00894 competitively binds to miR-429 to mediate ZEB1 expression; consequently, it is implicated to play a role in the progression of breast cancer.