Anoikis in cancer: The role of lipid signaling

Raeisi, Mortaza; Zehtabi, Mojtaba; Velaei, Kobra; Fayyazpour, Parisa; Aghaei, Negar; Mehdizadeh, Amir

doi:10.1002/cbin.11896

Cited by 40 publications

(27 citation statements)

References 144 publications

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“…Disruption of cell-cell attachment or cell-ECM attachment leads to a form of apoptosis called “anoikis” ( Raeisi et al, 2022 ). This process can eliminate misaligned or shed cells under physiological or pathological conditions, contributing to the realization of tissue homeostasis.…”

Section: Introductionmentioning

confidence: 99%

A novel anoikis-related prognostic signature associated with prognosis and immune infiltration landscape in clear cell renal cell carcinoma

Chen

Liu²,

Zhu³

et al. 2022

Front. Genet.

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Background: Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma (RCC). Anoikis plays an essential function in tumourigenesis, whereas the role of anoikis in ccRCC remains unclear.Methods: Anoikis-related genes (ARGs) were collected from the MSigDB database. According to univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to select the ARGs associated with the overall rate (OS). Multivariate Cox regression analysis was conducted to identify 5 prognostic ARGs, and a risk model was established. The Kaplan-Meier survival analysis was used to evaluate the OS rate of ccRCC patients. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSVA) were utilized to investigate the molecular mechanism of patients in the low- and high-risk group. ESTIMATE, CIBERSOT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape. Consensus clustering analysis was performed to divide the patients into different subgroups.Results: A fresh risk model was constructed based on the 5 prognostic ARGs (CHEK2, PDK4, ZNF304, SNAI2, SRC). The Kaplan-Meier survival analysis indicated that the OS rate of patients with a low-risk score was significantly higher than those with a high-risk score. Consensus clustering analysis successfully clustered the patients into two subgroups, with a remarkable difference in immune infiltration landscape and prognosis. The ESTIMATE, CIBERSORT, and ssGSEA results illustrated a significant gap in immune infiltration landscape of patients in the low- and high-risk group. Enrichment analysis and GSVA revealed that immune-related signaling pathways might mediate the role of ARGs in ccRCC. The nomogram results illustrated that the ARGs prognostic signature was an independent prognostic predictor that distinguished it from other clinical characteristics. TIDE score showed a promising immunotherapy response of ccRCC patients in different risk subgroups and cluster subgroups.Conclusion: Our study revealed that ARGs play a carcinogenic role in ccRCC. Additionally, we firstly integrated multiple ARGs to establish a risk-predictive model. This study highlights that ARGs could be implemented as a stratification factor for individualized and precise treatment in ccRCC patients.

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Section: Introductionmentioning

confidence: 99%

A novel anoikis-related prognostic signature associated with prognosis and immune infiltration landscape in clear cell renal cell carcinoma

Chen

Liu²,

Zhu³

et al. 2022

Front. Genet.

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“…In many types of malignant tumors, meanwhile, we know that changes in carbohydrate metabolism, amino acid metabolism, and lipid metabolism mechanisms are associated with anoikis resistance, especially increased fatty acid uptake, and biosynthesis. [26] Likewise, at the initial stage of metastasis, malignantly transformed cells are easily killed by immune cells in the microenvironment, so malignant cells must transform their metabolic state to maintain their ability to metastasize and spread. In our risk score group, T cells gamma delta were distinguished to be the most crucial TICs.…”

Section: Discussionmentioning

confidence: 99%

Development of a prognostic signature based on anoikis-related genes in hepatocellular carcinoma with the utilization of LASSO-cox method

Shi

Mao

et al. 2023

Medicine

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To develop a signature based on anoikis-related genes (ARGs) for predicting the prognosis of patients with hepatocellular carcinoma (HCC), and to elucidate the molecular mechanisms involved. In this study, bioinformatic algorithms were applied to integrate and analyze 777 HCC RNA-seq samples from the cancer genome atlas and international cancer genome consortium repositories. A prognostic signature was developed via the least absolute shrinkage and selection operator-cox regression method. To evaluate the accuracy of the signature in predicting events, multi-type technical means, such as Kaplan–Meier plots, receiver operating characteristic curve analysis, nomogram construction, and univariate and multivariate Cox regression studies were performed. We investigated the underlying molecular biological mechanisms and immune mechanisms of the signature using gene set enrichment analysis and the CIBERSORT R package, respectively. Meanwhile, immunohistochemical staining acquired from the human protein atlas was used to confirm the differential expression levels of hub genes involved in the prognostic signature. We developed an HCC prognostic signature with a collection of 5 ARGs, and the prognostic value was successfully assessed and verified in both the test and validation cohorts. The risk scores calculated by the prognostic signature were proved to be an independent negative prognostic factor for overall survival. A set of nomograms based on risk scores was established and found to be effective in predicting OS. Further investigation of the underlying molecular biological mechanisms and immune mechanisms indicated that the signature may be relevant to metabolic dysregulation and infiltration of gamma delta T cells in the tumor. The survival prognosis of HCC patients can be predicted by the anoikis-related prognostic signature, and it serves as a valuable reference for individualized HCC therapy.

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“…Studies have shown that tumor cell resistance to anoikis determines its survival after detachment from the ECM, which is a prerequisite for malignant tumors to acquire invasive and metastatic colonization [7,8] . Therefore, anoikis may be a potential therapeutic treatment target for tumors [5] . Tumor cells can resist anoikis in the following ways: through autocrine growth factors or paracrine signaling by neighboring cells, resulting in activation of pro-survival signaling pathways; alteration of integrin expression patterns of integrins; and, reactive oxygen species (ROS)-mediated activation of growth factor receptors in a ligand-independent manner to escape apoptosis [9] and activation of epithelial-mesenchymal transition (EMT) [10] .…”

Section: Introductionmentioning

confidence: 99%

Development of an anoikis-related gene signature for lung squamous cell carcinoma to predict prognosis, immune landscape, and immunotherapy response

Liu

2023

Preprint

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Background Lung squamous cell carcinoma (LUSC) is a highly invasive malignancy associated with a poor prognosis. Anoikis plays a crucial role in the malignant progression of various cancers. However, studies focusing on the role of anoikis in LUSC are needed. Methods A total of 357 anoikis-related genes (ARGs) were obtained from the GeneCards database and Harmonizome portals. Univariate Cox regression was used to identify prognostic ARGs for patients with LUSC, and the R package ConsensusClusterPlus was used to determine the subtypes. Lasso regression was used to build a prognostic risk model called the ARG score. Functional enrichment analysis was used to investigate the potential biological function of the ARGs. The ESTIMATE, ssGSEA, and CIBERSOT algorithms were used to evaluate the pattern of immune infiltration of patients with different LUSC subtypes and risk groups. The R package timeROC, survival, and survminer were used to test the accuracy of the prognostic model and to construct a nomogram. RT-PCR was used to detect differential expression of the 9 hub genes in LUSC and normal lung epithelial cells. Finally, OncoPredict was used to identify drugs with therapeutic value for patients in the high and low-risk groups. Results A total of 138 differentially expressed ARGs were selected, among which 15 survival-related ARGs were identified, and 3 subtypes (A, B, and C) with significant differences in the immune infiltration and survival outcomes were determined. Nine prognostic ARGs were identified to construct prognostic models, and the predictive capacity was verified in the GSE73403 and GSE30219 datasets. Patients with a high-risk score had worse outcome, and were associated with Tregs, resting memory CD4 + T cells, neutrophils, immune escape phenotypes, inflammatory response, and epithelial–mesenchymal transition. A nomogram with excellent clinical diagnostic accuracy was established for LUSC patients. The 9 hub genes had lower expression in LUSC cell lines, except for CHEK2, FADD, and SPINK1. Finally, we identified several drugs for patients in different risk groups. Conclusions ARGs play a key role in LUSC, and the ARG score devised in this study may be a valuable tool for determining clinical prognosis and the immunotherapy response in patients with LUSC.

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Anoikis in cancer: The role of lipid signaling

Cited by 40 publications

References 144 publications

A novel anoikis-related prognostic signature associated with prognosis and immune infiltration landscape in clear cell renal cell carcinoma

A novel anoikis-related prognostic signature associated with prognosis and immune infiltration landscape in clear cell renal cell carcinoma

Development of a prognostic signature based on anoikis-related genes in hepatocellular carcinoma with the utilization of LASSO-cox method

Development of an anoikis-related gene signature for lung squamous cell carcinoma to predict prognosis, immune landscape, and immunotherapy response

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