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Cancer Medicine May 2024Tumors that resist anoikis, a programmed cell death triggered by detachment from the extracellular matrix, promote metastasis; however, the role of anoikis-related genes...
Identification of the molecular subtypes and signatures to predict the prognosis, biological functions, and therapeutic response based on the anoikis-related genes in colorectal cancer.
BACKGROUND
Tumors that resist anoikis, a programmed cell death triggered by detachment from the extracellular matrix, promote metastasis; however, the role of anoikis-related genes (ARGs) in colorectal cancer (CRC) stratification, prognosis, and biological functions remains unclear.
METHODS
We obtained transcriptomic profiles of CRC and 27 ARGs from The Cancer Genome Atlas, the Gene Expression Omnibus, and MSigDB databases, respectively. CRC tissue samples were classified into two clusters based on the expression pattern of ARGs, and their functional differences were explored. Hub genes were screened using weighted gene co-expression network analysis, univariate analysis, and least absolute selection and shrinkage operator analysis, and validated in cell lines, tissues, or the Human Protein Atlas database. We constructed an ARG-risk model and nomogram to predict prognosis in patients with CRC, which was validated using an external cohort. Multifaceted landscapes, including stemness, tumor microenvironment (TME), immune landscape, and drug sensitivity, between high- and low-risk groups were examined.
RESULTS
Patients with CRC were divided into C1 and C2 clusters. Cluster C1 exhibited higher TME scores, whereas cluster C2 had favorable outcomes and a higher stemness index. Eight upregulated hub ARGs (TIMP1, P3H1, SPP1, HAMP, IFI30, ADAM8, ITGAX, and APOC1) were utilized to construct the risk model. The qRT-PCR, Western blotting, and immunohistochemistry results were consistent with those of the bioinformatics analysis. Patients with high risk exhibited worse overall survival (p < 0.01), increased stemness, TME, immune checkpoint expression, immune infiltration, tumor mutation burden, and drug susceptibility compared with the patients with low risk.
CONCLUSION
Our results offer a novel CRC stratification based on ARGs and a risk-scoring system that could predict the prognosis, stemness, TME, immunophenotypes, and drug susceptibility of patients with CRC, thereby improving their prognosis. This stratification may facilitate personalized therapies.
Topics: Humans; Colorectal Neoplasms; Anoikis; Prognosis; Tumor Microenvironment; Gene Expression Regulation, Neoplastic; Male; Biomarkers, Tumor; Nomograms; Female; Transcriptome; Gene Expression Profiling
PubMed: 38785271
DOI: 10.1002/cam4.7315 -
Frontiers in Immunology 2024The effect of anoikis-related genes (ARGs) on clinicopathological characteristics and tumor microenvironment remains unclear. We comprehensively analyzed...
The effect of anoikis-related genes (ARGs) on clinicopathological characteristics and tumor microenvironment remains unclear. We comprehensively analyzed anoikis-associated gene signatures of 1057 colorectal cancer (CRC) samples based on 18 ARGs. Anoikis-related molecular subtypes and gene features were identified through consensus clustering analysis. The biological functions and immune cell infiltration were assessed using the GSVA and ssGSEA algorithms. Prognostic risk score was constructed using multivariate Cox regression analysis. The immunological features of high-risk and low-risk groups were compared. Finally, DAPK2-overexpressing plasmid was transfected to measure its effect on tumor proliferation and metastasis and . We identified 18 prognostic ARGs. Three different subtypes of anoikis were identified and demonstrated to be linked to distinct biological processes and prognosis. Then, a risk score model was constructed and identified as an independent prognostic factor. Compared to the high-risk group, patients in the low-risk group exhibited longer survival, higher enrichment of checkpoint function, increased expression of CTLA4 and PD-L1, higher IPS scores, and a higher proportion of MSI-H. The results of RT-PCR indicated that the expression of DAPK2 mRNA was significantly downregulated in CRC tissues compared to normal tissues. Increased DAPK2 expression significantly suppressed cell proliferation, promoted apoptosis, and inhibited migration and invasion. The nude mice xenograft tumor model confirmed that high expression of DAPK2 inhibited tumor growth. Collectively, we discovered an innovative anoikis-related gene signature associated with prognosis and TME. Besides, our study indicated that DAPK2 can serve as a promising therapeutic target for inhibiting the growth and metastasis of CRC.
Topics: Humans; Colorectal Neoplasms; Tumor Microenvironment; Anoikis; Animals; Prognosis; Mice; Immunotherapy; Female; Male; Gene Expression Regulation, Neoplastic; Death-Associated Protein Kinases; Cell Line, Tumor; Biomarkers, Tumor; Mice, Nude; Transcriptome; Gene Expression Profiling; Xenograft Model Antitumor Assays; Middle Aged; Cell Proliferation; Mice, Inbred BALB C
PubMed: 38779664
DOI: 10.3389/fimmu.2024.1378305 -
Advances in Wound Care May 2024Anoikis, a kind of programmed cell death that is triggered when cells lose contact with each other or with the matrix. However, the potential value of anoikis-related...
OBJECTIVE
Anoikis, a kind of programmed cell death that is triggered when cells lose contact with each other or with the matrix. However, the potential value of anoikis-related genes (ARGs) in keloid (KD) has not been investigated.
APPROACH
We downloaded three keloid fibroblast (KF) RNA-seq datasets from the GEO and obtained 338 ARGs from a search of the GeneCards database and PubMed articles. WGCNA was used to construct the coexpression network, and obtain the KF-related ARGs. The LASSO-Cox method was used to screen the hub ARGs and construct the best prediction model. Then, GEO single cell sequencing datasets were used to verify the expression of hub genes. We used whole RNA sequencing for gene-level validation, and the correlation between KD immune infiltration and anoikis.
RESULTS
Our study comprehensively analyzed the role of ARGs in KD for the first time. LASSO regression analysis identified six hub ARGs (HIF1A, SEMA7A, SESN1, CASP3, LAMA3 and SIK2). A large number of miRNAs participate in the regulation of hub ARGs. In addition, correlation analysis revealed that ARGs were significantly correlated with the infiltration levels of multiple immune cells in KD patients. Innovation We explored the expression characteristics of ARGs in KD, which is extremely important for determining the molecular pathways and mechanisms underlying KD.
CONCLUSIONS
This study provides a useful reference for revealing the characteristics of ARGs in the pathogenesis of KD. The identified hub genes may provide potential therapeutic targets for patients. This study provides new ideas for individualized therapy and immunotherapy.
PubMed: 38775414
DOI: 10.1089/wound.2024.0027 -
Scientific Reports May 2024Colorectal cancer (CRC) is a malignant tumor originating from epithelial cells of the colon or rectum, and its invasion and metastasis could be regulated by anoikis....
Colorectal cancer (CRC) is a malignant tumor originating from epithelial cells of the colon or rectum, and its invasion and metastasis could be regulated by anoikis. However, the key genes and pathways regulating anoikis in CRC are still unclear and require further research. The single cell transcriptome dataset GSE221575 of GEO database was downloaded and applied to cell subpopulation type identification, intercellular communication, pseudo time cell trajectory analysis, and receptor ligand expression analysis of CRC. Meanwhile, the RNA transcriptome dataset of TCGA, the GSE39582, GSE17536, and GSE17537 datasets of GEO were downloaded and merged into one bulk transcriptome dataset. The differentially expressed genes (DEGs) related to anoikis were extracted from these data sets, and key marker genes were obtained after feature selection. A clinical prognosis prediction model was constructed based on the marker genes and the predictive effect was analyzed. Subsequently, gene pathway analysis, immune infiltration analysis, immunosuppressive point analysis, drug sensitivity analysis, and immunotherapy efficacy based on the key marker genes were conducted for the model. In this study, we used single cell datasets to determine the anoikis activity of cells and analyzed the DEGs of cells based on the score to identify the genes involved in anoikis and extracted DEGs related to the disease from the transcriptome dataset. After dimensionality reduction selection, 7 marker genes were obtained, including TIMP1, VEGFA, MYC, MSLN, EPHA2, ABHD2, and CD24. The prognostic risk model scoring system built by these 7 genes, along with patient clinical data (age, tumor stage, grade), were incorporated to create a nomogram, which predicted the 1-, 3-, and 5-years survival of CRC with accuracy of 0.818, 0.821, and 0.824. By using the scoring system, the CRC samples were divided into high/low anoikis-related prognosis risk groups, there are significant differences in immune infiltration, distribution of immune checkpoints, sensitivity to chemotherapy drugs, and efficacy of immunotherapy between these two risk groups. Anoikis genes participate in the differentiation of colorectal cancer tumor cells, promote tumor development, and could predict the prognosis of colorectal cancer.
Topics: Humans; Colorectal Neoplasms; Anoikis; Prognosis; Gene Expression Regulation, Neoplastic; Cell Differentiation; Transcriptome; Biomarkers, Tumor; Gene Expression Profiling; Female
PubMed: 38773226
DOI: 10.1038/s41598-024-62370-y -
Journal of Korean Medical Science May 2024The process of cancer metastasis is dependent on the cancer cells' capacity to detach from the primary tumor, endure in a suspended state, and establish colonies in... (Review)
Review
The process of cancer metastasis is dependent on the cancer cells' capacity to detach from the primary tumor, endure in a suspended state, and establish colonies in other locations. Anchorage dependence, which refers to the cells' reliance on attachment to the extracellular matrix (ECM), is a critical determinant of cellular shape, dynamics, behavior, and, ultimately, cell fate in nonmalignant and cancer cells. Anchorage-independent growth is a characteristic feature of cells resistant to anoikis, a programmed cell death process triggered by detachment from the ECM. This ability to grow and survive without attachment to a substrate is a crucial stage in the progression of metastasis. The recently discovered phenomenon named "adherent-to-suspension transition (AST)" alters the requirement for anchoring and enhances survival in a suspended state. AST is controlled by four transcription factors (IKAROS family zinc finger 1, nuclear factor erythroid 2, BTG anti-proliferation factor 2, and interferon regulatory factor 8) and can detach cells without undergoing the typical epithelial-mesenchymal transition. Notably, AST factors are highly expressed in circulating tumor cells compared to their attached counterparts, indicating their crucial role in the spread of cancer. Crucially, the suppression of AST substantially reduces metastasis while sparing primary tumors. These findings open up possibilities for developing targeted therapies that inhibit metastasis and emphasize the importance of AST, leading to a fundamental change in our comprehension of how cancer spreads.
Topics: Humans; Neoplasm Metastasis; Neoplasms; Cell Adhesion; Extracellular Matrix; Epithelial-Mesenchymal Transition; Anoikis; Transcription Factors
PubMed: 38769921
DOI: 10.3346/jkms.2024.39.e156 -
Neurological Research May 2024This study aimed to explore the expression, clinical significance, and functional mechanism of FYN in lower-grade gliomas (LGG).
OBJECTIVES
This study aimed to explore the expression, clinical significance, and functional mechanism of FYN in lower-grade gliomas (LGG).
METHODS
The mRNA and protein expression of FYN in LGG tissues were detected using databases including OncoLnc, GEPIA, and Human protein atlas (HPA). The UCSC Xena browser, TIMER, STRING and Metascape databases were used to investigate Kaplan-Meier survival curves, correlations between FYN expression and various types of immune cell infiltration, protein interaction network and possible functional mechanism.
RESULTS
FYN expression in LGG, IDH mutation or 1p19q co-deletion subgroup was significantly higher than in corresponding control groups ( < 0.05). Patients with higher FYN expression had longer overall survival ( < 0.05). Male or no 1p19q co-deletion groups with higher FYN expression also had longer overall survival ( < 0.05). FYN expression had close correlation with infiltrating levels of cell purity, CD4+T cells, macrophages, and CD8+T cells ( < 0.05). Protein interaction network result showed correlation among FYN, SH2D1A, LCK, CAV1, SRC, CBL and PTK2. Functional enrichment analysis revealed that FYN and its related genes mainly participated in bacterial invasion of epithelial cells and natural killer cell mediated cytotoxicity. Peptidyl-tyrosine phosphorylation, negative regulation of anoikis, immune effector process, transmembrane receptor protein tyrosine kinase signaling pathway, epidermal growth factor receptor signaling pathway, and negative regulation of protein modification process may be the critical biological process.
CONCLUSIONS
FYN is up-expressed in LGG and related to its good prognosis. It participated in tumor pathophysiological processes and may be a therapeutic target for LGG.
PubMed: 38752708
DOI: 10.1080/01616412.2024.2354620 -
Anti-cancer Drugs May 2024The study aims to discuss the challenges associated with treating prostate cancer (PCa), which is known for its complexity and drug resistance. It attempts to find...
The study aims to discuss the challenges associated with treating prostate cancer (PCa), which is known for its complexity and drug resistance. It attempts to find differentially expressed genes (DEGs), such as those linked to anoikis resistance and circulating tumor cells, in PCa samples. This study involves analyzing the functional roles of these DEGs using gene enrichment analysis, and then screening of 102 bioactive compounds to identify a combination that can control the expression of the identified DEGs. In this study, 53 DEGs were identified from PCa samples including anoikis-resistant PCa cells and circulating tumor cells in PCa. Gene enrichment analysis with regards to functional enrichment of DEGs was performed. An inclusive screening process was carried out among 102 bioactive compounds to identify a combination capable of affecting and regulating the expression of selected DEGs. Eventually, gastrodin, nitidine chloride, chenodeoxycholic acid, and bilobalide were selected, as their combination demonstrated ability to modulate expression of 50 out of the 53 genes targeted. The subsequent analysis focused on investigating the biological pathways and processes influenced by this combination. The findings revealed a multifaceted and multidimensional approach to tumor regression. The combination of bioactive compounds exhibited effects on various genes including those related to production of inflammatory cytokines, cell proliferation, autophagy, apoptosis, angiogenesis, and metastasis. The current study has made a valuable contribution to the development of a combination of bioactive natural compounds that can significantly impede the development of treatment resistance in prostate tumor while countering the tumors' evasion of the immune system. The implications of this study are highly significant as it suggests the creation of an enhanced immunotherapeutic, natural therapeutic concoction with combinatorial potential.
PubMed: 38743565
DOI: 10.1097/CAD.0000000000001616 -
Scientific Reports May 2024In addition to presenting significant diagnostic and treatment challenges, lung adenocarcinoma (LUAD) is the most common form of lung cancer. Using scRNA-Seq and bulk...
Integrated single-cell and bulk RNA-Seq analysis enhances prognostic accuracy of PD-1/PD-L1 immunotherapy response in lung adenocarcinoma through necroptotic anoikis gene signatures.
In addition to presenting significant diagnostic and treatment challenges, lung adenocarcinoma (LUAD) is the most common form of lung cancer. Using scRNA-Seq and bulk RNA-Seq data, we identify three genes referred to as HMR, FAM83A, and KRT6A these genes are related to necroptotic anoikis-related gene expression. Initial validation, conducted on the GSE50081 dataset, demonstrated the model's ability to categorize LUAD patients into high-risk and low-risk groups with significant survival differences. This model was further applied to predict responses to PD-1/PD-L1 blockade therapies, utilizing the IMvigor210 and GSE78220 cohorts, and showed strong correlation with patient outcomes, highlighting its potential in personalized immunotherapy. Further, LUAD cell lines were analyzed using quantitative PCR (qPCR) and Western blot analysis to confirm their expression levels, further corroborating the model's relevance in LUAD pathophysiology. The mutation landscape of these genes was also explored, revealing their broad implication in various cancer types through a pan-cancer analysis. The study also delved into molecular subclustering, revealing distinct expression profiles and associations with different survival outcomes, emphasizing the model's utility in precision oncology. Moreover, the diversity of immune cell infiltration, analyzed in relation to the necroptotic anoikis signature, suggested significant implications for immune evasion mechanisms in LUAD. While the findings present a promising stride towards personalized LUAD treatment, especially in immunotherapy, limitations such as the retrospective nature of the datasets and the need for larger sample sizes are acknowledged. Prospective clinical trials and further experimental research are essential to validate these findings and enhance the clinical applicability of our prognostic model.
Topics: Humans; Adenocarcinoma of Lung; Anoikis; Lung Neoplasms; Prognosis; Immunotherapy; Programmed Cell Death 1 Receptor; RNA-Seq; B7-H1 Antigen; Single-Cell Analysis; Gene Expression Regulation, Neoplastic; Cell Line, Tumor; Immune Checkpoint Inhibitors; Biomarkers, Tumor
PubMed: 38740918
DOI: 10.1038/s41598-024-61629-8 -
Translational Cancer Research Apr 2024Colorectal cancer (CRC) is characterized by a high metastasis rate, leading to poor prognosis and increased mortality. Anoikis, a physiological process, serves as a...
BACKGROUND
Colorectal cancer (CRC) is characterized by a high metastasis rate, leading to poor prognosis and increased mortality. Anoikis, a physiological process, serves as a crucial barrier against metastasis. The objective of this research is to construct a prognostic model for CRC based on genes associated with anoikis.
METHODS
The study involved differential analysis and univariate Cox analysis of anoikis-related genes (ARGs), resulting in the selection of 47 genes closely associated with prognosis. Subsequently, unsupervised k-means clustering analysis was conducted on all patients to identify distinct clusters. Survival analysis, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE) analysis were performed on the different clusters to investigate associations within the clusters. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were utilized to assess metabolic pathway enrichment between the identified clusters. Furthermore, single-sample GSEA (ssGSEA) was applied to explore variations in immune infiltration. Multivariable Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were conducted to construct a risk model based on ten signatures, which enabled the grouping of all samples according to their risk scores. The prognostic value of the model was validated using receiver operating characteristic (ROC) curves, area under the curve (AUC) calculations, and survival curves. Additionally, the expression of candidate genes was validated using quantitative real-time polymerase chain reaction (qRT-PCR).
RESULTS
Forty-seven survival-related ARGs were screened out. Somatic mutation analysis showed that these genes revealed a high mutation rate. Based on their expression, two clusters were identified. Cluster B patients exhibited a shortened overall survival and higher immune infiltration. A risk scoring model including ten genes was subsequently developed, which exhibited excellent prognostic predictive ability for CRC, as evidenced by the survival curve, ROC curve, and AUC curve. In addition, a nomogram was developed for predicting 3- and 5-year survival probabilities. The qRT-PCR results indicated the dissimilarities among the ten signatures in the tumor tissues and adjacent tissues of patients with CRC were fundamentally consistent with the analytical findings.
CONCLUSIONS
This study comprehensively evaluated the prognostic significance of ARGs in CRC. It identified two distinct anoikis-related clusters and examined their respective immune microenvironments. Furthermore, an ARGs signature was developed to effectively predict the prognosis of CRC, thereby establishing a solid foundation for investigating the clinical prognostic role of anoikis in CRC.
PubMed: 38737694
DOI: 10.21037/tcr-23-1221 -
Translational Cancer Research Apr 2024Lung adenocarcinoma (LUAD), a type of lung cancer, is one of the most aggressive and deadly malignancies worldwide. Malignant tumor cells exhibit strong anti-anoikis...
BACKGROUND
Lung adenocarcinoma (LUAD), a type of lung cancer, is one of the most aggressive and deadly malignancies worldwide. Malignant tumor cells exhibit strong anti-anoikis properties to achieve distant metastasis through the circulatory system. However, more research is needed to understand how anoikis is involved in the progression, metastasis and especially the prognosis of LUAD.
METHODS
We obtained anoikis-related genes (ARGs) from two websites, Harmonizome and Genecards, and integrated them to select and model the genes associated with LUAD prognosis. In addition, we investigated differences in the immune cell microenvironment and pathways of enrichment analysis between subtypes. We finally constructed a nomogram based on ARGs and used decision curve analysis (DCA) to demonstrate that this model could help clinicians make clinical decisions.
RESULTS
Sixty-four differentially expressed genes (DEGs) were found to be associated with survival, and of these, six were chosen to build a prognostic model. The time-dependent receiver operating characteristic (ROC) curves showed that the model had a satisfactory predictive ability. Enrichment analysis and immune microenvironment analysis revealed that the immune status and drug sensitivity of populations at high and low risk were different. We integrated the clinicopathological features of LUAD with the risk score to build the nomogram. The nomogram was shown to be a good predictor of short- and long-term survival in LUAD patients through DCA analysis.
CONCLUSIONS
This new model based on six ARGs and nomograms in our study could help patients with LUAD develop personalized treatment plans.
PubMed: 38737691
DOI: 10.21037/tcr-23-2185