Systematic Analysis of the Expression and Prognostic Significance of P4HA1 in Pancreatic Cancer and Construction of a lncRNA-miRNA-P4HA1 Regulatory Axis

[Plasma miRNA expression in patients with genetically confirmed multiple endocrine neoplasia type 1 syndrome and its phenocopies]

BACKGROUND

MEN-1 is a rare autosomal dominant disease caused by mutations in MEN1 gene encoding the menin protein. This syndrome is characterized by the occurrence of parathyroid tumors, gastroenteropancreatic neuroendocrine tumors, pituitary adenomas, as well as other endocrine and non-endocrine tumors. If a patient with the MEN-1 phenotype carry no mutations in the MEN1 gene, the condition considers a phenocopy of syndrome (phMEN1). The possible cause of this changes could be changes in epigenetic regulation, particularly in microRNA expression that might affect menin signaling pathways.

AIM

to identify differently expressed circulating miRNAs in plasma in patients with genetically confirmed MEN-1 syndrome, its phenocopies and healthy controls.

MATERIALS AND METHODS

single-center, case-control study was conducted. We assessed plasma microRNA expression in patients with genetically confirmed MEN-1 (gMEN1), phMEN1 and healthy controls. Morning plasma samples were collected from fasting patients and stored at -80°C. Total RNA isolation was performed using miRNeasy Mini Kit with QIAcube. The libraries were prepared by the QIAseq miRNA Library Kit following the manufacturer. Circulating miRNA sequencing was done on Illumina NextSeq 500 (Illumina). Subsequent data processing was performed using the DESeq2 bioinformatics algorithm.

RESULTS

we enrolled 21 consecutive patients with gMEN1 and 11 patients with phMEN1, along with 12 gender matched controls. Median age of gMEN1 was 38,0 [34,0; 41,0]; in phMEN1 - 59,0 [51,0; 60,0]; control - 59,5 [51,5; 62,5]. The gMEN1 group differed in age (p<0.01) but not gender (р=0.739) or BMI (р=0.116) compared to phMEN1 and controls group, the last two groups did not differ by these parameters (p>0.05). 25 microRNA were differently expressed in groups gMEN1 and phMEN1 (21 upregulated microRNAs, 4 - downregulated). Comparison of samples from the phMEN-1 group and relatively healthy controls revealed 10 differently expressed microRNAs: 5 - upregulated; 5 - downregulated. In the gMEN-1 and control groups, 26 differently expressed microRNAs were found: 24 - upregulated; 2 - downregulated. The miRNAs most differing in expression among the groups were selected for further validation by RT-qPCR (in the groups of gMEN1 vs phMEN1 - miR-3613-5p, miR-335-5p, miR-32-5p, miR-425-3p, miR-25-5p, miR-576-5p, miR-215-5p, miR-30a-3p, miR-141-3p, miR-760, miR-501-3p; gMEN1 vs control - miR-1976, miR-144-5p miR-532-3p, miR-375; as well as in phMEN1 vs control - miR-944, miR-191-5p, miR-98-5p).

CONCLUSION

In a pilot study, we detected microRNAs that may be expressed differently between patients with gMEN-1 and phMEN-1. The results need to be validated using different measurement method with larger sample size.

Multi-omics analysis reveals cuproptosis and mitochondria-based signature for assessing prognosis and immune landscape in osteosarcoma

Background

Osteosarcoma (OSA), the most common primary mesenchymal bone tumor, is a health threat to children and adolescents with a dismal prognosis. While cuproptosis and mitochondria dysfunction have been demonstrated to exert a crucial role in tumor progression and development, the mechanisms by which they are regulated in OSA still await clarification.

Methods

Two independent OSA cohorts containing transcriptome data and clinical information were collected from public databases. The heterogeneity of OSA were evaluated by single cell RNA (scRNA) analysis. To identify a newly molecular subtype, unsupervised consensus clustering was conducted. Cox relevant regression methods were utilized to establish a prognostic gene signature. Wet lab experiments were performed to confirm the effect of model gene in OSA cells.

Results

We determined 30 distinct cell clusters and assessed OSA heterogeneity and stemness scRNA analysis. Then, univariate Cox analysis identified 24 candidate genes which were greatly associated with the prognosis of OSA. Based on these prognostic genes, we obtained two molecular subgroups. After conducting step Cox regression, three model genes were selected to construct a signature showing a favorable performance to forecast clinical outcome. Our proposed signature could also evaluate the response to chemotherapy and immunotherapy of OSA cases.

Conclusion

We generated a novel risk model based on cuproptosis and mitochondria-related genes in OSA with powerful predictive ability in prognosis and immune landscape.

CD96 as a Potential Diagnostic Biomarker and New Target for Skin Cutaneous Melanoma

Skin cutaneous melanoma has high morbidity and mortality. Identification of reliable and quantitative melanoma biomarkers could facilitate an early diagnosis and improve survival and morbidity rates. CD96 has a significant role in adjusting immune function. Although the abnormal expression of CD96 has been reported to participate in carcinogenesis in many human types of cancer, the bioinformatics role of the CD96 in melanoma is unknown. Expression degrees and their underlying functions were first studied by this study. According to TCGA, GTEx, and gene expression profile interaction analysis dataset in this paper, compared with normal skin tissues, CD96 was expressed at higher levels in human cutaneous melanoma skin tissues. Meanwhile, we detected the relative CD96 expression levels by immunohistochemistry. Gene functional enrichment analyses were applied through cBioPortal database analysis. CD96 was clearly upregulated in skin cutaneous melanoma patients and carried out its effects through regulating several signaling pathways, containing the JAK-STAT, PI3K-Akt, and MAPK. Taken together, the analysis results indicated that CD96 could be used as a new clinical bioindicator as well as an underlying medicinal target for cutaneous melanoma.

The Role of microRNA in Pancreatic Cancer

MicroRNAs (miRNAs) are small ribonucleic acid molecules that play a key role in regulating gene expression. The increasing number of studies undertaken on the functioning of microRNAs in the tumor formation clearly indicates their important potential in oncological therapy. Pancreatic cancer is one of the deadliest cancers. The expression of miRNAs released into the bloodstream appears to be a good indicator of progression and evaluation of the aggressiveness of pancreatic cancer, as indicated by studies. The work reviewed the latest literature on the importance of miRNAs for pancreatic cancer development.

Comprehensive Analysis of Gene Expression Profiles Identifies a P4HA1-Related Gene Panel as a Prognostic Model in Colorectal Cancer Patients

Objective: Colorectal cancer (CRC) is the leading cause of mortality worldwide. Growing evidence suggests that the current pathological staging system is inadequate for efficient and accurate prognosis. In this study, we aim to build a prognosis model to predict the survival outcome of CRC patients by using gene expression profiles from The Cancer Genome Atlas (TCGA). Materials and Methods: Univariate and multivariate Cox regression analysis were used to assess the relationship between clinical factors and P4HA1 expression regarding the prognosis of patients with colon adenocarcinoma (COAD). The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to select prognostic differential expression genes (DEGs) for the construction of prognostic risk score model. Kaplan-Meier and receiver operating characteristic (ROC) survival analysis were used to assess the performance of the model on both TCGA cohort and an independent dataset GSE39582. Results: Overexpression of P4HA1 was confirmed to be associated with poor clinical outcome of colon cancer patients in both TCGA and GSE39582 cohorts. Using the TCGA cohort, we identified 1528 DEGs related to elevated P4HA1 expression, and we established a 11-gene panel to construct the prognostic risk score model by LASSO Cox regression analysis based on their expression profiles. The 11-gene signature was further validated in the independent dataset GSE39582. Time-dependent ROC curves indicated good performance of our model in predicting 1, 2, and 3-years overall survival in COAD patients. Additionally, gene set enrichment analysis indicated that the 11-gene signature was related to pathways involved in tumor progression. Conclusions: Together, we have established a 11-gene signature significantly associated with prognosis in COAD patients, which could serve as a promising tool for clinical application in the future.