Dysregulation of Pseudogenes/lncRNA-Hsa-miR-1-3p-PAICS Pathway Promotes the Development of NSCLC

Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma

Objective

Lung adenocarcinoma poses a major global health challenge and is a leading cause of cancer-related deaths worldwide. This study is a review of three molecular biomarkers screened by machine learning that are not only important in the occurrence and progression of lung adenocarcinoma but also have the potential to serve as biomarkers for clinical diagnosis, prognosis evaluation and treatment guidance.

Methods

Differentially expressed genes (DEGs) were identified using comprehensive GSE1987 and GSE18842 gene expression databases. A comprehensive bioinformatics analysis of these DEGs was conducted to explore enriched functions and pathways, relative expression levels, and interaction networks. Random Forest and LASSO regression analysis techniques were used to identify the three most significant target genes. The TCGA database and quantitative polymerase chain reaction (qPCR) experiments were used to verify the expression levels and receiver operating characteristic (ROC) curves of these three target genes. Furthermore, immune invasiveness, pan-cancer, and mRNA-miRNA interaction network analyses were performed.

Results

Eighty-nine genes showed increased expression and 190 genes showed decreased expression. Notably, the upregulated DEGs were predominantly associated with organelle fission and nuclear division, whereas the downregulated DEGs were mainly associated with genitourinary system development and cell-substrate adhesion. The construction of the DEG protein-protein interaction network revealed 32 and 19 hub genes with the highest moderate values among the upregulated and downregulated genes, respectively. Using random forest and LASSO regression analyses, the hub genes were employed to identify three most significant target genes.TCGA database and qPCR experiments were used to verify the expression levels and ROC curves of these three target genes, and immunoinvasive analysis, pan-cancer analysis and mRNA-miRNA interaction network analysis were performed.

Conclusion

Three target genes identified by machine learning: BUB1B, CENPF, and PLK1 play key roles in LUAD development of lung adenocarcinoma.

Novel Blood Biomarkers for Response Prediction and Monitoring of Stereotactic Ablative Radiotherapy and Immunotherapy in Metastatic Oligoprogressive Lung Cancer

Up to 80% of patients under immune checkpoint inhibitors (ICI) face resistance. In this context, stereotactic ablative radiotherapy (SABR) can induce an immune or abscopal response. However, its molecular determinants remain unknown. We present early results of a translational study assessing biomarkers of response to combined ICI and SABR (I-SABR) in liquid biopsy from oligoprogressive patients in a prospective observational multicenter study. Cohort A includes metastatic patients in oligoprogression to ICI maintaining the same ICI due to clinical benefit and who receive concomitant SABR. B is a comparative group of oligometastatic patients receiving only SABR. Blood samples are extracted at baseline (T1), after the first (T2) and last (T3) fraction, two months post-SABR (T4) and at further progression (TP). Response is evaluated by iRECIST and defined by the objective response rate (ORR)-complete and partial responses. We assess peripheral blood mononuclear cells (PBMCs), circulating cell-free DNA (cfDNA) and small RNA from extracellular vesicles. Twenty-seven patients could be analyzed (cohort A: n = 19; B: n = 8). Most were males with non-small cell lung cancer and one progressing lesion. With a median follow-up of 6 months, the last ORR was 63% (26% complete and 37% partial response). A decrease in cfDNA from T2 to T3 correlated with a good response. At T2, CD8+PD1+ and CD8+PDL1+ cells were increased in non-responders and responders, respectively. At T2, 27 microRNAs were differentially expressed. These are potential biomarkers of response to I-SABR in oligoprogressive disease.

Two ferroptosis-specific expressed genes NOX4 and PARP14 are considered as potential biomarkers for the diagnosis and treatment of diabetic retinopathy and atherosclerosis

OBJECTIVES

Both Diabetic retinopathy (DR) and Atherosclerosis (AS) are common complications in patients with diabetes, and they share major pathophysiological similarities and have a common pathogenesis. Studies performed to date have demonstrated that ferroptosis plays a vital part in the occurrence and development of DR and AS, but its mechanism in the two diseases remains poorly understood.

METHODS

DR Chip data (GSE60436 and GSE102485) and AS chip data (GSE100927 and GSE57691) were obtained from the Gene Expression Omnibus (GEO) database. The screening of the differential expression genes (DEGs) was analyzed using the limma package, and the genes related to ferroptosis were obtained from the FerrDb V2 database. Two key genes (NOX4 and PARP14) were identified through external datasets validation and receiver operating characteristic (ROC) curve analysis. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were used to conduct a functional enrichment analysis, and miRNA-mRNA networks were established. The CIBERSORT algorithm was applied to identify the immune cell infiltration between the disease group and control group. Next, the correlations between key genes and infiltrating immune cells were investigated by the Spearman method. Finally, the correlation between 2 key genes and ferroptosis markers was confirmed.

RESULTS

Nine ferroptosis differentially expressed genes (DE-FRGs) between DR and AS were identified in this study. NOX4 and PARP14 were selected as key genes for further analysis by external datasets and ROC curve analysis. The key genes NOX4, PARP14 and their correlated genes (such as CYBA, NOX1, NOX3, CYBB, PARP9, PARP10, and PARP15) are mainly enriched in oxidoreductase activity, protein ADP-ribosylation, superoxide metabolic process, reactive oxygen species metabolic process, PID pathway, and VEGFA-VEGFR2 pathway. A miRNA-mRNA network was constructed, and we got 12 miRNAs correlated with the target gene NOX4, 38 miRNAs correlated with the target gene PARP14. Three common miRNAs (hsa-miR-1-3p, hsa-miR-129-2-3p, and hsa-miR-155-5p) were observed in the network. Immune infiltration analysis displayed that activated B cell, MDSC, and Type 17 T helper cell are the common immune cells involved in the immune infiltration process of DR and AS. The results revealed that there are significant correlations between two key genes and most ferroptosis marker genes no matter in DR or AS.

CONCLUSION

Ferroptosis-related genes NOX4 and PARP14 may be common biomarkers of DR and AS. Both were associated with immune infiltration in patients with DR and AS. Our data provide a theoretical basis for the early diagnosis and immunotherapy of the two diseases.

Identification of shared pathogenetic mechanisms between COVID-19 and IC through bioinformatics and system biology

COVID-19 increased global mortality in 2019. Cystitis became a contributing factor in SARS-CoV-2 and COVID-19 complications. The complex molecular links between cystitis and COVID-19 are unclear. This study investigates COVID-19-associated cystitis (CAC) molecular mechanisms and drug candidates using bioinformatics and systems biology. Obtain the gene expression profiles of IC (GSE11783) and COVID-19 (GSE147507) from the Gene Expression Omnibus (GEO) database. Identified the common differentially expressed genes (DEGs) in both IC and COVID-19, and extracted a number of key genes from this group. Subsequently, conduct Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DEGs. Additionally, design a protein-protein interaction (PPI) network, a transcription factor gene regulatory network, a TF miRNA regulatory network, and a gene disease association network using the DEGs. Identify and extract hub genes from the PPI network. Then construct Nomogram diagnostic prediction models based on the hub genes. The DSigDB database was used to forecast many potential molecular medicines that are associated with common DEGs. Assess the precision of hub genes and Nomogram models in diagnosing IC and COVID-19 by employing Receiver Operating Characteristic (ROC) curves. The IC dataset (GSE57560) and the COVID-19 dataset (GSE171110) were selected to validate the models' diagnostic accuracy. A grand total of 198 DEGs that overlapped were found and chosen for further research. FCER1G, ITGAM, LCP2, LILRB2, MNDA, SPI1, and TYROBP were screened as the hub genes. The Nomogram model, built using the seven hub genes, demonstrates significant utility as a diagnostic prediction model for both IC and COVID-19. Multiple potential molecular medicines associated with common DEGs have been discovered. These pathways, hub genes, and models may provide new perspectives for future research into mechanisms and guide personalised and effective therapeutics for IC patients infected with COVID-19.

The important role of miR-1-3p in cancers

Cancer is a malignant tumor that seriously threatens human life and health. At present, the main treatment methods include surgical resection, chemotherapy, radiotherapy, and immunotherapy. However, the mechanism of tumor occurrence and development is complex, and it produces resistance to some traditional treatment methods, leading to treatment failure and a high mortality rate for patients. Therefore, exploring the molecular mechanisms of tumor occurrence, development, and drug resistance is a very important task. MiRNAs are a type of non-coding small RNA that regulate a series of biological effects by binding to the 3'-UTR of the target mRNA, degrading the mRNA, or inhibiting its translation. MiR-1-3p is an important member of them, which is abnormally expressed in various tumors and closely related to the occurrence and development of tumors. This article introduces miR-1-3p from multiple aspects, including its production and regulation, role in tumor occurrence and development, clinical significance, role in drug resistance, and approaches for targeting miR-1-3p. Intended to provide readers with a comprehensive understanding of the important role of miR-1-3p in tumors.

PAICS as a potential target for cancer therapy linking purine biosynthesis to cancer progression

Tumor cells are required to undergo metabolic reprogramming for rapid development and progression, and one of the metabolic characteristics of cancer cells is the excessive synthesis and utilization of nucleotides. Abnormally increased nucleotides and their metabolites not only directly accelerate tumor cell progression but also indirectly act on stromal cells in the tumor microenvironment (TME) via a paracrine manner to regulate tumor progression. Purine nucleotides are mainly produced via de novo nucleotide synthesis in tumor cells; therefore, intervening in their synthesis has emerged as a promising strategy in anti-tumor therapy. De novo purine synthesis is a 10-step reaction catalyzed by six enzymes to synthesize inosine 5-monophosphate (IMP) and subsequently synthesize AMP and GMP. Phosphoribosylaminoimidazole carboxylase/phosphori-bosylaminoimidazole succinocarboxamide synthetase (PAICS) is a bifunctional enzyme that catalyzes de novo purine synthesis. Aberrantly elevated PAICS expression in various tumors is associated with poor prognosis. Evidence suggests that PAICS and its catalytic product, N-succinylcarboxamide-5-aminoimidazole ribonucleotide (SAICAR), could inhibit tumor cell apoptosis and promote the growth, epithelial-mesenchymal transition (EMT), invasion, and metastasis by regulating signaling pathways such as pyruvate kinase M2 (PKM2), extracellular signal-related kinases 1 and 2 (ERK1/2), focal adhesion kinase (FAK) and so on. This review summarizes the structure, biological functions and the molecular mechanisms of PAICS in cancer development and discusses its potential to be a target for tumor therapy.