Mass Spectrometry-Based Proteomic Discovery of Prognostic Biomarkers in Adrenal Cortical Carcinoma

Jang, Han Na; Moon, Seong-Yong; Jung, Kyeong Cheon; Kim, Sang Wan; Kim, Hyeyoon; Han, Dohyun; Kim, Jung Hee

doi:10.3390/cancers13153890

Cited by 22 publications

(17 citation statements)

References 70 publications

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“…Studies have shown that WBSCR16 is essential for mitochondrial function (37)(38)(39), and is involved in the regulation of oxidative phosphorylation (40,41). In cancer, WBSCR16 has been found to be significantly correlated with the survival of adrenal cortical carcinoma (42). The role of DTX3L in glioma remains largely unexplored (18).…”

Section: Discussionmentioning

confidence: 99%

Bromodomain containing 4 transcriptionally activated Deltex E3 ubiquitin ligase 2 contributes to glioma progression and predicts an unfavorable prognosis

Wu¹,

Shen²,

Lu³

et al. 2022

Ann Transl Med

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Background: Glioblastoma multiforme (GBM) is the most common type of glioma, and the most aggressive brain malignancy in adults. This study sought to identify novel survival-status related markers, and examine their function in glioma. Methods:The gene expression, survival heatmaps, and Kaplan-Meier survival plots of the genes were analyzed by using gene expression profiling interactive analysis (GEPIA) dataset, Linked Omics. The singlecell data analysis and tumor immune infiltration analysis was conducted by Tumor Immune Estimation Resource (TIMER) dataset. DBTRG and U251 cells with silenced Deltex E3 ubiquitin ligase 2 (DTX2) expression were constructed and used for Cell Counting Kit 8 (CCK-8), and wound healing assay in vitro.Chromatin immunoprecipitation sequencing (ChIP-seq) analysis was used to explore the histone activation marks and transcription factors DTX2 promoter. Dual-luciferase assays were carried out to detect the luciferase activities of bromodomain containing 4 (BRD4) binding to DTX2. Results:We first conducted a survival-status analysis to identify survival status-related genes in The Cancer Genome Atlas GBM and low-grade glioma data sets. A subsequent analysis identified 3 novel prognostic biomarkers; that is, DTX2, cytochrome P450 oxidoreductase, and Williams-Beuren syndrome chromosomal region 16 protein. In the validation Chinese Glioma Genome Atlas data sets, DTX2 showed the best performance, and was examined in a further analysis. Next, 3 short-hairpin ribonucleic acids were designed to silence DTX2 expression, and CCK-8 and wound-healing assays were applied to study the function of DTX2. We found that DTX2-silenced glioma cells exhibited a significant decrease in their growth and migration capabilities. Finally, the molecular basis for increased DTX2 in glioma was investigated via ChIP-Seq analysis and luciferase assays. The analysis revealed that DTX2 was transcriptionally activated by BRD4. Conclusions:In conclusion, BRD4 transcriptionally activates DTX2, contributes to glioma progression, predicts an unfavorable prognosis, and could provide new options for glioma prognosis prediction and treatment.

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Section: Discussionmentioning

confidence: 99%

Bromodomain containing 4 transcriptionally activated Deltex E3 ubiquitin ligase 2 contributes to glioma progression and predicts an unfavorable prognosis

Wu¹,

Shen²,

Lu³

et al. 2022

Ann Transl Med

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“…Two recent studies [ 72 , 73 ] used ML to identify new biomarkers that could be employed for prognostic purposes for adrenocortical carcinoma (ACC), a rare and aggressive cancer that arises from the cells of the outer layer of the adrenal gland. The prognosis for ACC is generally poor, with a 5-year survival rate of only about 10–20%, so early detection is crucial for improving the chances of survival, as well as identifying new markers.…”

Section: Ai Application In Rare Diseasesmentioning

confidence: 99%

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

et al. 2023

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Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1–9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.

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“…Different families of proteins are involved in the regulation of EVs production by endocytic pathway, such as endosomal sorting complexes required for transport (ESCRT) or Rab family. Interestingly, a recent proteomic study performed by liquid chromatographytandem mass spectrometry (LC-MS/MS) of 45 formalin-fixed paraffin-embedded ACC tissues showed that in the top 20 of differentially expressed proteins (DEPs) comparing stage 1-2 vs. 3-4 tumors, two DEPs belong to Rab family: RABL3 e RABAC1 (79). RABAC1 was previously found in late endosomes (80).…”

Section: Crosstalk Between Autophagy and Extracellular Vesicles Bioge...mentioning

confidence: 99%

Modulation of Autophagy in Adrenal Tumors

Sousa

Pereira²,

Pignatelli³

2022

Front. Endocrinol.

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Adrenal masses are one of the most common tumors in humans. The majority are benign and non-functioning and therefore do not require immediate treatment. In contrast, the rare adrenal malignant tumors are often highly aggressive and with poor prognosis. Besides usually being detected in advanced stages, often already with metastases, one of the reasons of the unfavorable outcome of the patients with adrenal cancer is the absence of effective treatments. Autophagy is one of the intracellular pathways targeted by several classes of chemotherapeutics. Mitotane, the most commonly used drug for the treatment of adrenocortical carcinoma, was recently shown to also modulate autophagy. Autophagy is a continuous programmed cellular process which culminates with the degradation of cellular organelles and proteins. However, being a dynamic mechanism, understanding the autophagic flux can be highly complex. The role of autophagy in cancer has been described paradoxically: initially described as a tumor pro-survival mechanism, different studies have been showing that it may result in other outcomes, namely in tumor cell death. In adrenal tumors, this dual role of autophagy has also been addressed in recent years. Studies reported both induction and inhibition of autophagy as a treatment strategy of adrenal malignancies. Importantly, most of these studies were performed using cell lines. Consequently clinical studies are still required. In this review, we describe what is known about the role of autophagy modulation in treatment of adrenal tumors. We will also highlight the aspects that need further evaluation to understand the paradoxical role of autophagy in adrenal tumors.

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Mass Spectrometry-Based Proteomic Discovery of Prognostic Biomarkers in Adrenal Cortical Carcinoma

Cited by 22 publications

References 70 publications

Bromodomain containing 4 transcriptionally activated Deltex E3 ubiquitin ligase 2 contributes to glioma progression and predicts an unfavorable prognosis

Bromodomain containing 4 transcriptionally activated Deltex E3 ubiquitin ligase 2 contributes to glioma progression and predicts an unfavorable prognosis

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

Modulation of Autophagy in Adrenal Tumors

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