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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 -
Translational Cancer Research Apr 2024Hepatocellular carcinoma (HCC) is a major health problem with more than 850,000 cases per year worldwide. This cancer is now the third leading cause of cancer-related...
BACKGROUND
Hepatocellular carcinoma (HCC) is a major health problem with more than 850,000 cases per year worldwide. This cancer is now the third leading cause of cancer-related deaths worldwide, and the number is rising. Cancer cells develop anoikis resistance which is a vital step during cancer progression and metastatic colonization. However, there is not much research that specifically addresses the role of anoikis in HCC, especially in terms of prognosis.
METHODS
This study obtained gene expression data and clinical information from 371 HCC patients through The Cancer Genome Atlas (TCGA) Program and The Gene Expression Omnibus (GEO) databases. A total of 516 anoikis-related genes (ANRGs) were retrieved from GeneCard database and Harmonizome portal. Differential expression analysis identified 219 differentially expressed genes (DEGs), and univariate Cox regression analysis was utilized to select 99 ANRGs associated with the prognosis of HCC patients. A risk scoring model with seven genes was established using the least absolute shrinkage and selection operator (LASSO) regression model, and internal validation of the model was performed.
RESULTS
The identified 99 ANRGs are closely associated with the prognosis of HCC patients. The risk scoring model based on seven characteristic genes demonstrates excellent predictive performance, further validated by receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves. The study reveals significant differences in immune cell infiltration, gene expression, and survival status among different risk groups.
CONCLUSIONS
The prognosis of HCC patients can be predicted using a unique prognostic model built on ANRGs in HCC.
PubMed: 38737687
DOI: 10.21037/tcr-23-2096 -
Free Radical Biology & Medicine Aug 2024A tumour suppressor miRNA, miR-128-3p, is widely involved in various biological processes and has been found to get downregulated in breast cancer patients. We...
A tumour suppressor miRNA, miR-128-3p, is widely involved in various biological processes and has been found to get downregulated in breast cancer patients. We previously published that ectopically expressed miR-128-3p suppressed migration, invasion, cell cycle arrest, and breast cancer stem cells. In the present study, we explored the role of Empagliflozin (EMPA) as a miR-128-3p functionality-mimicking drug in inducing ferroptosis by inhibiting CD98hc. Given that CD98hc is one of the proteins critical in triggering ferroptosis, we confirmed that miR-128-3p and EMPA inhibited SP1, leading to inhibition of CD98hc expression. Further, transfection with siCD98hc, miR-128-3p mimics, and inhibitors was performed to assess their involvement in the ferroptosis of anoikis-resistant cells. We proved that anoikis-resistant cells possess high ROS and iron levels. Further, miR-128-3p and EMPA treatments induced ferroptosis by inhibiting GSH and enzymatic activity of GPX4 and also induced lipid peroxidation. Moreover, EMPA suppressed bioluminescence of 4T1-Red-FLuc induced thoracic cavity, peritoneal tumour burden and lung nodules in an in-vivo metastatic model of breast cancer. Collectively, we revealed that EMPA sensitized the ECM detached cells to ferroptosis by synergically activating miR-128-3p and lowering the levels of SP1 and CD98hc, making it a potential adjunct drug for breast cancer chemotherapy.
Topics: Ferroptosis; MicroRNAs; Humans; Female; Breast Neoplasms; Glucosides; Animals; Anoikis; Mice; Benzhydryl Compounds; Gene Expression Regulation, Neoplastic; Cell Line, Tumor; Reactive Oxygen Species; Sodium-Glucose Transporter 2 Inhibitors; Xenograft Model Antitumor Assays; Lipid Peroxidation; Sodium-Phosphate Cotransporter Proteins, Type IIb
PubMed: 38734268
DOI: 10.1016/j.freeradbiomed.2024.05.018 -
Medicine May 2024Papillary thyroid carcinoma (PTC) prognosis may be deteriorated due to the metastases, and anoikis palys an essential role in the tumor metastasis. However, the...
Papillary thyroid carcinoma (PTC) prognosis may be deteriorated due to the metastases, and anoikis palys an essential role in the tumor metastasis. However, the potential effect of anoikis-related genes on the prognosis of PTC was unclear. The mRNA and clinical information were obtained from the cancer genome atlas database. Hub genes were identified and risk model was constructed using Cox regression analysis. Kaplan-Meier (K-M) curve was applied for the survival analysis. Immune infiltration and immune therapy response were calculated using CIBERSORT and TIDE. The identification of cell types and cell interaction was performed by Seurat, SingleR and CellChat packages. GO, KEGG, and GSVA were applied for the enrichment analysis. Protein-protein interaction network was constructed in STRING and Cytoscape. Drug sensitivity was assessed in GSCA. Based on bulk RNA data, we identified 4 anoikis-related risk signatures, which were oncogenes, and constructed a risk model. The enrichment analysis found high risk group was enriched in some immune-related pathways. High risk group had higher infiltration of Tregs, higher TIDE score and lower levels of monocytes and CD8 T cells. Based on scRNA data, we found that 4 hub genes were mainly expressed in monocytes and macrophages, and they interacted with T cells. Hub genes were significantly related to immune escape-related genes. Drug sensitivity analysis suggested that cyclin dependent kinase inhibitor 2A may be a better chemotherapy target. We constructed a risk model which could effectively and steadily predict the prognosis of PTC. We inferred that the immune escape may be involved in the development of PTC.
Topics: Humans; Thyroid Cancer, Papillary; Anoikis; Thyroid Neoplasms; Prognosis; Single-Cell Analysis; Sequence Analysis, RNA; Protein Interaction Maps; Female; Male; Kaplan-Meier Estimate; Gene Expression Regulation, Neoplastic; Gene Expression Profiling
PubMed: 38728457
DOI: 10.1097/MD.0000000000038144 -
Applied Biochemistry and Biotechnology May 2024Colon cancer (CC) is a malignant tumor in the colon. Despite some progress in the early detection and treatment of CC in recent years, some patients still experience...
Colon cancer (CC) is a malignant tumor in the colon. Despite some progress in the early detection and treatment of CC in recent years, some patients still experience recurrence and metastasis. Therefore, it is urgent to better predict the prognosis of CC patients and identify new biomarkers. Recent studies have shown that anoikis-related genes (ARGs) play a significant role in the progression of many tumors. Hence, it is essential to confirm the role of ARGs in the development and treatment of CC by integrating scRNA-seq and transcriptome data. This study integrated transcriptome and single-cell sequencing (scRNA-seq) data from CC samples to evaluate patient stratification, prognosis, and ARG expression in different cell types. Specifically, differential expression of ARGs was identified through consensus clustering to classify CC subtypes. Subsequently, a CC risk model composed of CDKN2A, NOX4, INHBB, CRYAB, TWIST1, CD36, SERPINE1, and MMP3 was constructed using prognosis-related ARGs. Finally, using scRNA-seq data of CC, the expression landscape of prognostic genes in different cell types and the relationship between important immune cells and other cells were explored. Through the above analysis, two CC subtypes were identified, showing significant differences in prognosis and clinical factors. Subsequently, a risk model comprising aforementioned genes successfully categorized all CC samples into two risk groups, which also exhibited significant differences in prognosis, clinical factors, involved pathways, immune landscape, and drug sensitivity. Multiple pathways (cell adhesion molecules (CAMs), and extracellular matrix (ECM) receptor interaction) and immune cells/immune functions (B cell naive, dendritic cell activate, plasma cells, and T cells CD4 memory activated) related to CC were identified. Furthermore, it was found that prognostic genes were highly expressed in various immune cells, and B cells exhibited more and stronger interaction pathways with other cells. The results of this study may provide references for personalized treatment and potential biomarker identification in CC.
PubMed: 38727936
DOI: 10.1007/s12010-024-04957-9