Identification of Potential Long Non-Coding RNA Candidates that Contribute to Triple-Negative Breast Cancer in Humans through Computational Approach

Identification of hub fatty acid metabolism-related genes and immune infiltration in IgA nephropathy

AIMS

To investigate the potential mechanisms of fatty acid metabolism (FAM)-related genes in IgA nephropathy (IgAN) and to explore its immune cell infiltration characteristic.

METHODS

Datasets for IgAN and FAM-related genes were obtained from GEO and MSigDB database, respectively. We employed differential expression analysis and WGCNA to identify common genes. GO and KEGG analyses were performed to compare the differences between IgAN and control groups. Furthermore, LASSO logistic regression was applied to develop a predictive model based on FAM-related genes. The efficacy of this prognostic model was evaluated using ROC analysis. The infiltration of immune cells and immune-related functions were assessed with CIBERSORT tool. Finally, the identified key genes were validated in blood samples from IgAN and control patients, as well as in human mesangial cells (HMCs) following Gd-IgA stimulation using Real-time PCR.

RESULTS

A total of 12 hub genes linked to FAM were identified in patients with IgAN. A predictive model consisting of four genes was conducted through COX and LASSO regression analysis, revealing AUC values that indicate a relatively strong diagnostic capability. Immune infiltration analysis indicated that various immune cells have significant associations with IgAN. Additionally, Real-time PCR assays confirmed that the expression levels of hub genes were markedly reduced in IgAN patients and in Gd-IgA treated HMCs compared to controls.

CONCLUSION

This study employed bioinformatics methods to unveiled the immune cell infiltration associated with IgAN and to explore the potential genetic connection between FAM and IgAN. This could aid in predicting the risk of IgAN and enhance both diagnosis and prognosis of this condition.

Unraveling Biomarker Signatures in Triple-Negative Breast Cancer: A Systematic Review for Targeted Approaches

Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer, marked by poor outcomes and dismal prognosis. Due to the absence of targetable receptors, chemotherapy still represents the main therapeutic option. Therefore, current research is now focusing on understanding the specific molecular pathways implicated in TNBC, in order to identify novel biomarker signatures and develop targeted therapies able to improve its clinical management. With the aim of identifying novel molecular features characterizing TNBC, elucidating the mechanisms by which these molecular biomarkers are implicated in the tumor development and progression, and assessing the impact on cancerous cells following their inhibition or modulation, we conducted a literature search from the earliest works to December 2023 on PubMed, Scopus, and Web Of Science. A total of 146 studies were selected. The results obtained demonstrated that TNBC is characterized by a heterogeneous molecular profile. Several biomarkers have proven not only to be characteristic of TNBC but also to serve as potential effective therapeutic targets, holding the promise of a new era of personalized treatments able to improve its prognosis. The pre-clinical findings that have emerged from our systematic review set the stage for further investigation in forthcoming clinical trials.

[Study on the Role and Mechanism of METTL3 Mediating the Up-regulation of m6A Modified Long Non-coding RNA THAP7-AS1 in Promoting the Occurrence of Lung Cancer]

BACKGROUND

Lung cancer is a major threat to human health. The molecular mechanisms related to the occurrence and development of lung cancer are complex and poorly known. Exploring molecular markers related to the development of lung cancer is helpful to improve the effect of early diagnosis and treatment. Long non-coding RNA (lncRNA) THAP7-AS1 is known to be highly expressed in gastric cancer, but has been less studied in other cancers. The aim of the study is to explore the role and mechanism of methyltransferase-like 3 (METTL3) mediated up-regulation of N6-methyladenosine (m6A) modified lncRNA THAP7-AS1 expression in promoting the development of lung cancer.

METHODS

Samples of 120 lung cancer and corresponding paracancerous tissues were collected. LncRNA microarrays were used to analyze differentially expressed lncRNAs. THAP7-AS1 levels were detected in lung cancer, adjacent normal tissues and lung cancer cell lines by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The diagnostic value of THAP7-AS1 in lung cancer and the relationship between THAP7-AS1 expression and survival rate and clinicopathological parameters were analyzed. Bioinformatics analysis, methylated RNA immunoprecipitation (meRIP), RNA pull-down and RNA-immunoprecipitation (RIP) assay were used to investigate the molecular regulation mechanism of THAP7-AS1. Cell proliferation, migration, invasion and tumorigenesis of SPC-A-1 and NCI-H1299 cells were determined by MTS, colony-formation, scratch, Transwell and xenotransplantation in vivo, respectively. Expression levels of phosphoinositide 3-kinase/protein kenase B (PI3K/AKT) signal pathway related protein were detected by Western blot.

RESULTS

Expression levels of THAP7-AS1 were higher in lung cancer tissues and cell lines (P<0.05). THAP7-AS1 has certain diagnostic value in lung cancer [area under the curve (AUC)=0.737], and its expression associated with overall survival rate, tumor size, tumor-node-metastasis (TNM) stage and lymph node metastasis (P<0.05). METTL3-mediated m6A modification enhanced THAP7-AS1 expression. The cell proliferation, migration, invasion and the volume and mass of transplanted tumor were all higher in the THAP7-AS1 group compared with the NC group and sh-NC group of SPC-A-1 and NCI-H1299 cells, while the cell proliferation, migration and invasion were lower in the sh-THAP7-AS1 group (P<0.05). THAP7-AS1 binds specifically to Cullin 4B (CUL4B). The cell proliferation, migration, invasion, and expression levels of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), phosphoinositide-3 kinase, catalytic subunit delta (PIK3CD), phospho-phosphatidylinositol 3-kinase (p-PI3K), phospho-protein kinase B (p-AKT) and phospho-mammalian target of rapamycin (p-mTOR) were higher in the THAP7-AS1 group compared with the Vector group of SPC-A-1 and NCI-H1299 cells (P<0.05).

CONCLUSIONS

LncRNA THAP7-AS1 is stably expressed through m6A modification mediated by METTL3, and combines with CUL4B to activate PI3K/AKT signal pathway, which promotes the occurrence and development of lung cancer.

Transcriptome and methylome sequencing reveals altered long non-coding RNA genes expression and their aberrant DNA methylation in equine sarcoids

Recent publications confirmed that long non-coding RNAs (lncRNAs) perform an essential function in gene-specific transcription regulation. Nevertheless, despite its important role, lncRNA has not yet been described in equine sarcoids, the skin neoplasia of horses. Therefore, the aim of this study is to deepen the knowledge about lncRNA expression in the pathogenesis of equine sarcoids and provide new insight into the regulatory function of lncRNA in the bovine papillomavirus-dependent neoplasia of horse dermal tissues. RNA sequencing (RNA-seq) data from 12 equine sarcoid samples and the corresponding controls were reanalyzed in this study. A total of 3396 differentially expressed (DE) lncRNAs and 128 DElncRNA-DE genes (DEGs) pairs were identified. Differentially expressed lncRNAs predicted target genes were enriched in pathways associated with inter alia the extracellular matrix disassembly and cancer pathways. Furthermore, methylation data from the same samples were integrated into the analysis, and 12 DElncRNAs were described as potentially disturbed by aberrant methylation. In conclusion, this study presents novel data about lncRNA's role in the pathogenesis of equine sarcoids.

Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models

Immunoglobulin-A-nephropathy (IgAN) is a kidney disease caused by the accumulation of IgAN deposits in the kidneys, which causes inflammation and damage to the kidney tissues. Various bioinformatics analysis-based approaches are widely used to predict novel candidate genes and pathways associated with IgAN. However, there is still some scope to clearly explore the molecular mechanisms and causes of IgAN development and progression. Therefore, the present study aimed to identify key candidate genes for IgAN using machine learning (ML) and statistics-based bioinformatics models. First, differentially expressed genes (DEGs) were identified using limma, and then enrichment analysis was performed on DEGs using DAVID. Protein-protein interaction (PPI) was constructed using STRING and Cytoscape was used to determine hub genes based on connectivity and hub modules based on MCODE scores and their associated genes from DEGs. Furthermore, ML-based algorithms, namely support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and partial least square discriminant analysis (PLS-DA) were applied to identify the discriminative genes of IgAN from DEGs. Finally, the key candidate genes (FOS, JUN, EGR1, FOSB, and DUSP1) were identified as overlapping genes among the selected hub genes, hub module genes, and discriminative genes from SVM, LASSO, and PLS-DA, respectively which can be used for the diagnosis and treatment of IgAN.

The functional significance and cross-talk of non-coding RNAs in triple negative and quadruple negative breast cancer

One of the leading causes of cancer-related deaths worldwide is breast cancer, among which triple-negative breast cancer (TNBC) is the most malignant and lethal subtype. This cancer accounts for 10-20% of all breast cancer deaths. Proliferation, tumorigenesis, and prognosis of TNBC are affected when the androgen receptor (AR) is not expressed, and it is classified as quadruple negative breast cancer (QNBC). Non-coding RNAs, such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), play a significant role in tumorigenesis by virtue of their oncogenic and tumor-suppressive properties. To regulate tumorigenesis, miRNAs interact with their target mRNAs and modulate their expression, whereas lncRNAs can either act alone or interact with miRNAs or other molecules through various signaling pathways. Conversely, circRNAs regulate tumorigenesis by acting as miRNA sponges predominantly. Recently, non-coding RNAs were studied comprehensively for their roles in tumor proliferation, progression, and metastasis. As a result of existing studies and research progress, non-coding RNAs have been implicated in TNBC, necessitating their use as biomarkers for future diagnostic applications. In this review, the non-coding RNAs are explicitly implicated in the regulation of breast cancer, and their cross-talk between TNBC and QNBC is also discussed.