Distinct roles of ADIPOR1 and ADIPOR2: A pan-cancer analysis

Chen, Zhuoyuan; Yang, Hyun; Ren, Yunfeng; Yang, Ze; Huang, Jen‐Yu; Li, Cheng; Xiong, Ying; Yu, Bin

doi:10.3389/fendo.2023.1119534

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Some previous studies have verified these observations: (i) Gene MYB (in cluster-1) was reported to form fusion genes with NFIB due to the recurrent chromosomal translocation, which serves as a clear example of genotypic–phenotypic correlation for triple-negative breast cancer 44 . (ii) Dysregulation of gene ADIPOR1 (in cluster-3) is widely observed in many cancers, but its genomic alteration frequencies are low 45 . This is consistent with CGMega that attributes HiC-1, HiC-5, chromatin accessibility and active histone modification H3K4me3 to ADIPOR1 .…”

Section: Resultsmentioning

confidence: 99%

CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection

Li,

Han,

Sun

et al. 2024

Nat Commun

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Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1, PPM1A and DLG2. We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.

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Section: Resultsmentioning

confidence: 99%

CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection

Li,

Han,

Sun

et al. 2024

Nat Commun

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“…ADIPOR2 shows a wide correlation with immune checkpoint gene expression in most cancers, and it may be a predictive factor or a new target for immunotherapy. 24 MAL is a T-cell differentiation protein, and the loss of MAL expression and methylation are related to cell signaling pathways, tumor development, and clinical prognosis. 25 Suzuki et al 26 showed that MAL methylation in NSCLC tumor tissues is higher than in normal lung tissues.…”

Section: Discussionmentioning

confidence: 99%

Development of a prognostic model for lung adenocarcinoma polarity‐related genes and analysis of immune landscape

Xu,

Du,

Jing

et al. 2024

Biotech and App Biochem

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Despite the progress made in the management of lung adenocarcinoma (LUAD), the overall prognosis for LUAD individuals remains suboptimal. While the role of cell polarity in tumor invasion and metastasis is well established, its prognostic significance in LUAD is still unknown. Differential analysis was performed on the Cancer Genome Atlas (TCGA)‐LUAD and normal lung tissue, and candidate genes were identified by intersecting differentially expressed genes with polarity‐related genes (PRGs). A prognostic model was constructed using univariate and multivariate Cox regression and LASSO regression. To enhance the robustness of the analysis, an independent prognostic analysis was conducted by incorporating relevant clinical information. The accuracy and sensitivity of the model were validated using survival analysis and ROC curves. Finally, immune landscape, immune therapy, tumor mutation burden, and drug sensitivity analysis were carried out on high‐ and low‐risk patients. Ten prognostic genes were screened to divide LUAD patients into different risk groups. Survival analysis, ROC curves, and univariate/multivariate Cox regression analyses collectively demonstrated the favorable predictive performance of the model, which could be an independent prognostic factor. The nomogram, in conjunction with the calibration curve, demonstrated the model's compelling predictive capacity in prognosticating the overall survival of LUAD individuals. Low‐risk LUAD patients exhibited heightened levels of immune cell infiltration, immune scores, and immune checkpoint expression compared to high‐risk individuals. So, they may have a greater likelihood of benefiting from immune therapy. The high‐risk group demonstrated a remarkably higher tumor mutation burden (TMB) in contrast with the low‐risk group. XAV‐939, Fulvestrant, and SR16157 may have potential value in the clinical use of LUAD. We revealed the potential linkage between PRGs and LUAD prognosis, and the application of these prognostic factors in risk stratification and prognosis prediction of LUAD patients may be of great significance.

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“…Moreover, adiponectin may promote tumor progression through enhanced angiogenesis [224]. Research in colorectal and breast cancer shows that adiponectin has a prominent pro-angiogenic activity and that increased AdipoRs expression is associated with cancer invasiveness and/or progression [154,203,260]. Adiponectin downregulates STAT3 phosphorylation and activation, which increases tumor cell activity [36].…”

Section: Angiogenesis Vm and Adiponectinmentioning

confidence: 99%

Role of Leptin and Adiponectin in Carcinogenesis

Bocian-Jastrzębska,

Malczewska-Herman,

Kos-Kudła

2023

Cancers

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Hormones produced by adipocytes, leptin and adiponectin, are associated with the process of carcinogenesis. Both of these adipokines have well-proven oncologic potential and can affect many aspects of tumorigenesis, from initiation and primary tumor growth to metastatic progression. Involvement in the formation of cancer includes interactions with the tumor microenvironment and its components, such as tumor-associated macrophages, cancer-associated fibroblasts, extracellular matrix and matrix metalloproteinases. Furthermore, these adipokines participate in the epithelial–mesenchymal transition and connect to angiogenesis, which is critical for cancer invasiveness and cancer cell migration. In addition, an enormous amount of evidence has demonstrated that altered concentrations of these adipocyte-derived hormones and the expression of their receptors in tumors are associated with poor prognosis in various types of cancer. Therefore, leptin and adiponectin dysfunction play a prominent role in cancer and impact tumor invasion and metastasis in different ways. This review clearly and comprehensively summarizes the recent findings and presents the role of leptin and adiponectin in cancer initiation, promotion and progression, focusing on associations with the tumor microenvironment and its components as well as roles in the epithelial–mesenchymal transition and angiogenesis.

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Distinct roles of ADIPOR1 and ADIPOR2: A pan-cancer analysis

Cited by 6 publications

References 29 publications

CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection

CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection

Development of a prognostic model for lung adenocarcinoma polarity‐related genes and analysis of immune landscape

Role of Leptin and Adiponectin in Carcinogenesis

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