Gene expression profiling after LINC00472 overexpression in an NSCLC cell line

FN1 and VEGFA Are Potential Therapeutic Targets in Glioblastoma as Determined by Bioinformatics Analysis

BACKGROUND/AIM

Glioblastoma is the most malignant brain tumor, and despite advances in treatment, survival rates are still dismal. Therefore, a comprehensive understanding of the underlying molecular mechanisms of glioblastoma is needed. This study suggests potential therapeutic targets in glioblastoma that may provide new therapeutic insights.

MATERIALS AND METHODS

To identify hub genes in glioblastoma, three datasets were selected from the GEO database. After screening DEGs using GEO2R, GO and KEGG analyses were performed using DAVID. The PPI network was visualized using Cytoscape and 7 hub genes were extracted. The prognostic potential of 7 hub genes was investigated using the Gliovis and GEPIA2 databases.

RESULTS

In total, 176 up-regulated and 263 down-regulated genes were identified. From the PPI network, 7 hub genes were identified including CAMK2A, DLG4, SNAP25, SYT1, MYC, FN1, and VEGFA. Out of the 7 hub genes identified, FN1 and VEGFA have been associated with a poor prognosis in glioblastoma based on the survival analysis.

CONCLUSION

This study suggests that high levels of FN1 and VEGFA expression are associated with a poor prognosis in glioblastoma and that both genes are promising targets for glioblastoma therapy. Bioinformatics analysis of DEGs revealed putative targets that might reveal the molecular mechanisms underlying glioblastoma.

Construction and evaluation of a metabolic correlation diagnostic model for diabetes based on machine learning algorithms

BACKGROUND

Diabetes mellitus (DM) is a prevalent chronic disease marked by significant metabolic dysfunctions. Understanding its molecular mechanisms is vital for early diagnosis and treatment strategies.

METHODS

We used datasets GSE7014, GSE25724, and GSE156248 from the GEO database to build a diagnostic model for DM using Random Forest (RF) and LASSO regression models. GSE20966 served as a validation cohort. DM patients were classified into two subtypes for functional enrichment analysis. Expression levels of key diagnostic genes were validated using quantitative real-time PCR (qRT-PCR) on Peripheral Blood Mononuclear Cells (PBMCs) from DM patients and healthy controls, focusing on CXCL12 and PPP1R12B with GAPDH as the internal control.

RESULTS

After de-batching the datasets, we identified 131 differentially expressed genes (DEGs) between DM and control groups, with 70 up-regulated and 61 down-regulated. Enrichment analysis revealed significant down-regulation in the IL-12 signaling pathway, JAK signaling post-IL-12 stimulation, and the ferroptosis pathway in DM. Five genes (CXCL12, MXRA5, UCHL1, PPP1R12B, and C7) were identified as having diagnostic value. The diagnostic model showed high accuracy in both the training and validation cohorts. The gene set also enabled the subclassification of DM patients into groups with distinct functional traits. qRT-PCR results confirmed the bioinformatics findings, particularly the up-regulation of CXCL12 and PPP1R12B in DM patients.

CONCLUSION

Our study pinpointed seven energy metabolism-related genes differentially expressed in DM and controls, with five holding diagnostic value. Our model accurately diagnosed DM and facilitated patient subclassification, offering new insights into DM pathogenesis.

MALAT1-regulated gene expression profiling in lung cancer cell lines

BACKGROUND

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and has a poor prognosis. Identifying biomarkers based on molecular mechanisms is critical for early diagnosis, timely treatment, and improved prognosis of lung cancer. MALAT1 has been reported to have overexpressed and tumor-promoting functions in NSCLC. It has been proposed as a potential biomarker for the diagnosis and prognosis of cancer. Therefore, this study was conducted to profile the changes in gene expression according to the regulation of expression of MALAT1 in NSCLC cell lines and to investigate the correlation through bioinformatic analysis of differentially expressed genes (DEGs).

METHODS

MALAT1 expression levels were measured using RT-qPCR. The biological functions of MALAT1 in NSCLC were analyzed by cell counting, colony forming, wound-healing, and Transwell invasion assays. In addition, gene expression profiling in response to the knockdown of MALAT1 was analyzed by transcriptome sequencing, and differentially expressed genes regulated by MALAT1 were performed by GO and KEGG pathway enrichment analyses. Bioinformatic databases were used for gene expression analysis and overall survival analysis.

RESULTS

Comparative analysis versus MALAT1 expression in MRC5 cells (a normal lung cell line) and the three NSCLC cell lines showed that MALAT1 expression was significantly higher in the NSCLC cells. MALAT1 knockdown decreased cell survival, proliferation, migration, and invasion in all three NSCLC cell lines. RNA-seq analysis of DEGs in NSCLC cells showed 198 DEGs were upregulated and 266 DEGs downregulated by MALAT1 knockdown in all three NSCLC cell lines. Survival analysis on these common DEGs performed using the OncoLnc database resulted in the selection of five DEGs, phosphoglycerate mutase 1 (PGAM1), phosphoglycerate mutase 4 (PGAM4), nucleolar protein 6 (NOL6), nucleosome assembly protein 1 like 5 (NAP1L5), and sestrin1 (SESN1). The gene expression levels of these selected DEGs were proved to gene expression analysis using the TNMplot database.

CONCLUSION

MALAT1 might function as an oncogene that enhances NSCLC cell survival, proliferation, colony formation, and invasion. RNA-seq and bioinformatic analyses resulted in the selection of five DEGs, PGAM1, PGAM4, NOL6, NAP1L5, and SESN1, which were found to be closely related to patient survival and tumorigenesis. We believe that further investigation of these five DEGs will provide valuable information on the oncogenic role of MALAT1 in NSCLC.

A review on the role of LINC00472 in malignant and non-malignant disorders

Long intergenic non-protein coding RNA 472 (LINC00472) has been shown to regulate diverse cellular functions and contribute to the etiology of human disorders. LINC00472 gene is located on 6q13 and has different alternatively spliced transcripts. Expression pattern and function of LINC00472 have been evaluated in different types of cancers and some other disorders, including atherosclerosis, sepsis-induced acute hepatic injury, atrial fibrillation, neuropathic pain, primary biliary cholangitis and sepsis-induced cardiac dysfunction. This lincRNA can serve as a sponge for miR-24-3p, miR-196b-5p, miR-23a-3p, miR-93-5p, miR-4311, miR-455-3p and a number of other miRNAs. LINC00472 is able to regulate several pathways, including MEK/ERK, NF-kB, PTEN/PI3K/AKT, and STAT3 signaling pathways. This raises some concerning aspects that need to be investigated further and clarified in relation to diseases. Increasing our understanding of LINC00472's crucial roles will open new doors for creating effective therapeutic approaches against cancer and related diseases. The current study aims at providing an overview of functions of LINC00472 in malignant and non-malignant disorders.

Long non-coding RNA in glioma: novel genetic players in temozolomide resistance

Glioma is the most common primary malignant brain tumor in adults and accounts for approximately 80% of brain and central nervous system tumors. In 2021, the World Health Organization (WHO) published a new taxonomy for glioma based on its histological features and molecular alterations. Isocitrate dehydrogenase (IDH) catalyzes the decarboxylation of isocitrate, a critical metabolic reaction in energy generation in cells. Mutations in the IDH genes interrupt cell differentiation and serve as molecular biomarkers that can be used to classify gliomas. For example, the mutant IDH is widely detected in low-grade gliomas, whereas the wild type is in high-grade ones, including glioblastomas. Long non-coding RNAs (lncRNAs) are epigenetically involved in gene expression and contribute to glioma development. To investigate the potential use of lncRNAs as biomarkers, we examined lncRNA dysregulation dependent on the IDH mutation status. We found that several lncRNAs, namely, AL606760.2, H19, MALAT1, PVT1 and SBF2-AS1 may function as glioma risk factors, whereas AC068643.1, AC079228.1, DGCR5, FAM13A-AS1, HAR1A and WDFY3-AS2 may have protective effects. Notably, H19, MALAT1, PVT1, and SBF2-AS1 have been associated with temozolomide resistance in glioma patients. This review study suggests that targeting glioma-associated lncRNAs might aid the treatment of glioma.

The Involvement of Long Non-Coding RNAs in Glutamine-Metabolic Reprogramming and Therapeutic Resistance in Cancer

Metabolic alterations that support the supply of biosynthetic molecules necessary for rapid and sustained proliferation are characteristic of cancer. Some cancer cells rely on glutamine to maintain their energy requirements for growth. Glutamine is an important metabolite in cells because it not only links to the tricarboxylic acid cycle by producing α-ketoglutarate by glutaminase and glutamate dehydrogenase but also supplies other non-essential amino acids, fatty acids, and components of nucleotide synthesis. Altered glutamine metabolism is associated with cancer cell survival, proliferation, metastasis, and aggression. Furthermore, altered glutamine metabolism is known to be involved in therapeutic resistance. In recent studies, lncRNAs were shown to act on amino acid transporters and glutamine-metabolic enzymes, resulting in the regulation of glutamine metabolism. The lncRNAs involved in the expression of the transporters include the abhydrolase domain containing 11 antisense RNA 1, LINC00857, plasmacytoma variant translocation 1, Myc-induced long non-coding RNA, and opa interacting protein 5 antisense RNA 1, all of which play oncogenic roles. When it comes to the regulation of glutamine-metabolic enzymes, several lncRNAs, including nuclear paraspeckle assembly transcript 1, XLOC_006390, urothelial cancer associated 1, and thymopoietin antisense RNA 1, show oncogenic activities, and others such as antisense lncRNA of glutaminase, lincRNA-p21, and ataxin 8 opposite strand serve as tumor suppressors. In addition, glutamine-dependent cancer cells with lncRNA dysregulation promote cell survival, proliferation, and metastasis by increasing chemo- and radio-resistance. Therefore, understanding the roles of lncRNAs in glutamine metabolism will be helpful for the establishment of therapeutic strategies for glutamine-dependent cancer patients.

LncRNA RP11‑805J14.5 functions as a ceRNA to regulate CCND2 by sponging miR‑34b‑3p and miR‑139‑5p in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common lung cancer with high incidence. The prognosis of LUAD is poor due to its aggressive behavior. Long non‑coding RNAs (lncRNAs) have been reported as a key modulator on LUAD progression. Therefore, the present study aimed to clarify the molecular mechanism of lncRNAs in LUAD development. The expression of lncRNA RP11‑805J14.5 (RP11‑805J14.5) in LUAD tissues and cells was quantified based on the data in The Cancer Genome Atlas (TCGA). Cell viability was determined using Cell Counting Kit‑8 method. Apoptotic cells were sorted and determined by flow cytometry. Cell migration and invasion abilities were detected by the Transwell assay. Luciferase reporter experiment and RNA pull‑down assay were utilized to determine the interactions between RP11‑805J14.5, microRNA (miR)‑34b‑3p, miR‑139‑5p, and cyclin D2 (CCND2). A xenograft tumor was established to determine tumor growth in vivo. RP11‑805J14.5 was highly expressed in LUAD and associated with poor survival of LUAD patients. Knockdown of RP11‑805J14.5 suppressed LUAD cell growth, invasion, migration and tumor growth, indicating that RP11‑805J14.5 is an important regulator of LUAD. Our study demonstrated that the regulation of RP11‑805J14.5 on LUAD was mediated by CCND2 whose expression was regulated by sponging miR‑34b‑3p and miR‑139‑5p. The expression of RP11‑805J14.5 was increased in LUAD, and the knockdown of RP11‑805J14.5 expression suppressed LUAD cell growth, invasion and migration by downregulating CCND2 by sponging miR‑34b‑3p and miR‑139‑5p, indicating that RP11‑805J14.5 could be a prospective target for LUAD therapy.