The ALDH Family Contributes to Immunocyte Infiltration, Proliferation and Epithelial-Mesenchymal Transformation in Glioma

Wang, Zeyu; Mo, Yuyao; Tan, Ying; Wen, Zhihui; Dai, Ziyu; Zhang, Hao; Zhang, Xun; Feng, Shi Ting; Liang, Xisong; Song, Tao; Cheng, Quan

doi:10.3389/fimmu.2021.756606

Cited by 20 publications

(17 citation statements)

References 49 publications

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“…He et al discovered that the lncRNA DCGR5 suppresses tumor growth in glioma cells through the miR-21/Smad7 and miR-23a/PTEN axis [ 29 ]. There are also some previous LGG-related prognostic models with good predictive efficacy [ 30 – 33 ]. Pyroptosis is thought to be linked to the proliferation and migration of cancer cells.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Integration Develops a Pyroptosis-Related lncRNA Model to Enhance the Predicted Value of Low-Grade Glioma Patients

Wang

et al. 2022

Journal of Oncology

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Background. Molecular features have been included in the categorization of gliomas because they may be excellent predictors of tumor prognosis. Lower-grade glioma (LGGs, which comprise grade 2 and grade 3 gliomas) patients have a wide variety of outcomes. The goal of this research is to investigate a pyroptosis-based long noncoding RNA (lncRNA) profile and see whether it can be used to predict LGG prognosis. Methods. The Genotype-Tissue Expression (GTEx) and Cancer Genome Atlas (TCGA) datasets were utilized to get RNA data and clinical information for this research. Six considerably related lncRNAs (AL355574.1, AL355974.2, Z97989.1, SNAI3-AS1, LINC02593, and CYTOR) were selected using Cox regression (univariate and multivariate) and LASSO Cox regression. A variety of statistical techniques, including ROC curves, nomogram, and Kaplan-Meier curves, were utilized to verify the risk score’s accuracy. Following that, bioinformatics studies were carried out to investigate the possible molecular processes that influence LGG prognosis. The variations in pathway enrichment were investigated using GSEA. The immune microenvironment inconsistencies were investigated using CIBERSORT, ESTIMATE, MCPcounter, TIMER algorithms, and ssGSEA. Results. We discovered six lncRNAs with distinct expression patterns that are linked to LGG prognosis. Kaplan-Meier studies showed a signature of high-risk lncRNAs associated with a poor prognosis for LGG. Furthermore, the AUC of the lncRNA signature was 0.763, indicating that they may be used to predict LGG prognosis. In predicting LGG prognosis, our risk assessment approach outperformed conventional clinicopathological characteristics. In the high-risk group of people, GSEA identified tumor-related pathways and immune-related pathways. Furthermore, T cell-related activities such as T cell coinhibition and costimulation, check point, APC coinhibition and costimulation, CCR, and inflammatory promoting were shown to be substantially different between the two groups in TCGA analysis. Immune checkpoints including PD-1, CTLA4, and PD-L1 were expressed differentially in the two groups as well. Conclusion. This study found that pyroptosis-based lncRNAs were useful in predicting LGG patients’ survival, suggesting that they may be used as a therapeutic target in the future.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Integration Develops a Pyroptosis-Related lncRNA Model to Enhance the Predicted Value of Low-Grade Glioma Patients

Wang

et al. 2022

Journal of Oncology

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“…ALDH3B1 and ALDH16A1 are preferentially overexpressed in HGG and can promote the proliferation and migration of glioma cells by regulating cell cycle and EMT processes. Furthermore, high expressions of ALDH3B1 and ALDH16A1 are positively associated with worse treatment response and shorter OS in GBM patients 204 …”

Section: Aldhs As Csc and Prognosis Markers In Tumorsmentioning

confidence: 99%

“…Furthermore, high expressions of ALDH3B1 and ALDH16A1 are positively associated with worse treatment response and shorter OS in GBM patients. 204 …”

Section: Aldhs As Csc and Prognosis Markers In Tumorsmentioning

confidence: 99%

Aldehyde dehydrogenase in solid tumors and other diseases: Potential biomarkers and therapeutic targets

Xia

Liu

et al. 2023

MedComm

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The family of aldehyde dehydrogenases (ALDHs) contains 19 isozymes and is involved in the oxidation of endogenous and exogenous aldehydes to carboxylic acids, which contributes to cellular and tissue homeostasis. ALDHs play essential parts in detoxification, biosynthesis, and antioxidants, which are of important value for cell proliferation, differentiation, and survival in normal body tissues. However, ALDHs are frequently dysregulated and associated with various diseases like Alzheimer's disease, Parkinson's disease, and especially solid tumors. Notably, the involvement of the ALDHs in tumor progression is responsible for the maintenance of the stem‐cell‐like phenotype, triggering rapid and aggressive clinical progressions. ALDHs have captured increasing attention as biomarkers for disease diagnosis and prognosis. Nevertheless, these require further longitudinal clinical studies in large populations for broad application. This review summarizes our current knowledge regarding ALDHs as potential biomarkers in tumors and several non‐tumor diseases, as well as recent advances in our understanding of the functions and underlying molecular mechanisms of ALDHs in disease development. Finally, we discuss the therapeutic potential of ALDHs in diseases, especially in tumor therapy with an emphasis on their clinical implications.

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“…C. Disulfiram in glioblastoma: With potential utility in treating glioblastoma with temozolomide, disulfiram irreversibly inactivates P-gp [171][172][173]. As with other cancers, the ALDH-positive glioblastoma subpopulation has other stem attributes, and is more chemotherapy resistant than the ALDH-negative subpopulation [158,[174][175][176]. D. Disulfiram in NSCLC: As it does in other cancers, ALDH contributes to driving stemness and, hence, cytotoxicity resistance, in NSCLC [177][178][179][180][181].…”

Section: Disulfirammentioning

confidence: 99%

MDACT: A New Principle of Adjunctive Cancer Treatment Using Combinations of Multiple Repurposed Drugs, with an Example Regimen

Kast¹,

Alfieri

Assi

et al. 2022

Cancers

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In part one of this two-part paper, we present eight principles that we believe must be considered for more effective treatment of the currently incurable cancers. These are addressed by multidrug adjunctive cancer treatment (MDACT), which uses multiple repurposed non-oncology drugs, not primarily to kill malignant cells, but rather to reduce the malignant cells’ growth drives. Previous multidrug regimens have used MDACT principles, e.g., the CUSP9v3 glioblastoma treatment. MDACT is an amalgam of (1) the principle that to be effective in stopping a chain of events leading to an undesired outcome, one must break more than one link; (2) the principle of Palmer et al. of achieving fractional cancer cell killing via multiple drugs with independent mechanisms of action; (3) the principle of shaping versus decisive operations, both being required for successful cancer treatment; (4) an idea adapted from Chow et al., of using multiple cytotoxic medicines at low doses; (5) the idea behind CUSP9v3, using many non-oncology CNS-penetrant drugs from general medical practice, repurposed to block tumor survival paths; (6) the concept from chess that every move creates weaknesses and strengths; (7) the principle of mass—by adding force to a given effort, the chances of achieving the goal increase; and (8) the principle of blocking parallel signaling pathways. Part two gives an example MDACT regimen, gMDACT, which uses six repurposed drugs—celecoxib, dapsone, disulfiram, itraconazole, pyrimethamine, and telmisartan—to interfere with growth-driving elements common to cholangiocarcinoma, colon adenocarcinoma, glioblastoma, and non-small-cell lung cancer. gMDACT is another example of—not a replacement for—previous multidrug regimens already in clinical use, such as CUSP9v3. MDACT regimens are designed as adjuvants to be used with cytotoxic drugs.

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The ALDH Family Contributes to Immunocyte Infiltration, Proliferation and Epithelial-Mesenchymal Transformation in Glioma

Cited by 20 publications

References 49 publications

Machine Learning-Based Integration Develops a Pyroptosis-Related lncRNA Model to Enhance the Predicted Value of Low-Grade Glioma Patients

Machine Learning-Based Integration Develops a Pyroptosis-Related lncRNA Model to Enhance the Predicted Value of Low-Grade Glioma Patients

Aldehyde dehydrogenase in solid tumors and other diseases: Potential biomarkers and therapeutic targets

MDACT: A New Principle of Adjunctive Cancer Treatment Using Combinations of Multiple Repurposed Drugs, with an Example Regimen

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