Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

Artificial neural network-based gene screening and immune cell infiltration analysis of osteosarcoma feature

BACKGROUND

The present study aimed to construct an artificial neural network (ANN) model that leverages characteristic genes associated with osteosarcoma (OS) to enable accurate prognostication for OS patients.

METHODS

Our research revealed 467 differentially expressed genes (DEGs) via gene expression contrast analysis, consisting of 345 downregulated genes and 122 upregulated genes. Gene Ontology (GO) enrichment analysis illuminated functions primarily encompassing T-cell activation, secretory granule lumen and antioxidant activity, among others. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we discovered significant correlations between the DEGs and certain pathways, including phagosome, Staphylococcus aureus infection and human T-cell leukemia virus 1 infection. We then screened out 30 characteristic DEGs (CDEGs) based on random forest analysis and constructed the ANN model using the gene score matrix. To verify the credibility and accuracy of the ANN model, we performed internal and external validation processes, which affirmed our model's predictive capabilities.

RESULTS

The study further delved into the analysis of immune cell infiltration and its correlation with the target CDEGs, revealing disparities in the infiltration of 22 types of immune cells across different groups and their interrelationships. Moreover, we probed the expression of the two foremost CDEGs (YES1 and MFNG) in OS and normal tissues. We noted a positive relationship between the expression of YES1 and MFNG in OS tissues and the clinicopathological characteristics of OS patients.

CONCLUSIONS

Collectively, the findings of the present study validate the effectiveness of the CDEGs-based ANN model in predicting OS patients, which might facilitate early diagnosis and treatment of OS.

Objective assessment of adrenocortical carcinoma driver genes and their correlation with tumor pyruvate kinase M2

Glandular cancers have a significant share of the total cancer patients all over the world. In the case of adrenocortical carcinomas (ACCs), although the benign form is more frequent and common, the malignant form provides a very less percentage of patients with five or more than five years of survival rate. There are gene alterations that are involved as a crucial factor behind the occurrence of ACCs. Out of these, the most prominent genetic alterations (PRKAR-1A, CTNNB1, ZNRF3, TP53, CCNE1 and TERF2 genes) are linked with a glycolytic enzyme pyruvate kinase M2 (PKM2), which converts phosphoenolpyruvate (PEP) to pyruvate in the glycolytic pathway. The involvementof PKM2 renders a cumulative effect through different pathways that may result in the onset of ACCs. Thus, this review aims to establish a link between ACCs, alterations of specific genes and PKM2.

Molecular genotyping of adrenocortical carcinoma: a systematic analysis of published literature 2019-2021

PURPOSE OF REVIEW

comprehensive molecular characterization of adrenocortical carcinoma (ACC) through next-generation sequencing and bioinformatics analyses is expanding the number of targets with potential prognostic and therapeutic value. We performed a critical review of recent published literature on genotyping of ACC.

RECENT FINDINGS

423 studies were published between 2019 and 2021. After manual curation we summarized selected evidence in two thematic areas: germline deoxyribonucleic acid (DNA) variations, genomic alterations and prognosis.

SUMMARY

the evolving genomic landscape of ACC requires target validation in terms of prognostic and predictive value within scientific consortia. Although the existing multiple driver genes are difficult targets in the perspective of precision oncology, alterations in DNA damage repair genes or in promoter hypermethylation could open new venues for repurposing of existing drugs in ACC.