A prognostic model for anoikis-related genes in pancreatic cancer.

Anoikis, a distinct form of programmed cell death, is crucial for both organismal development and maintaining tissue equilibrium. Its role extends to the proliferation and progression of cancer cells. This study aimed to establish an anoikis-related prognostic model to predict the prognosis of pancreatic cancer (PC) patients. Gene expression data and patient clinical profiles were sourced from The Cancer Genome Atlas (TCGA-PAAD: Pancreatic Adenocarcinoma) and the International Cancer Genome Consortium (ICGC-PACA: Pancreatic Ductal Adenocarcinoma). Non-cancerous pancreatic tissue gene expression data were obtained from the Genotype-Tissue Expression (GTEx) project. The R package was used to construct anoikis-related PC prognostic models, which were later validated with the ICGC-PACA database. Survival analyses demonstrated a poorer prognosis for patients in the high-risk group, consistent across both TCGA-PAAD and ICGC-PACA datasets. A nomogram was designed as a predictive tool to estimate patient mortality. The study also analyzed tumor mutations and immune infiltration across various risk groups, uncovering notable differences in tumor mutation patterns and immune landscapes between high- and low-risk groups. In conclusion, this research successfully developed a prognostic model centered on anoikis-related genes, offering a novel tool for predicting the clinical trajectory of PC patients.

Elucidating prognosis in cervical squamous cell carcinoma and endocervical adenocarcinoma: a novel anoikis-related gene signature model.

Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are among the most prevalent gynecologic malignancies globally. The prognosis is abysmal once cervical cancer progresses to lymphatic metastasis. Anoikis, a specialized form of apoptosis induced by loss of cell adhesion to the extracellular matrix, plays a critical role. The prediction model based on anoikis-related genes (ARGs) expression and clinical data could greatly aid clinical decision-making. However, the relationship between ARGs and CESC remains unclear. Methods: ARGs curated from the GeneCards and Harmonizome portals were instrumental in delineating CESC subtypes and in developing a prognostic framework for patients afflicted with this condition. We further delved into the intricacies of the immune microenvironment and pathway enrichment across the identified subtypes. Finally, our efforts culminated in the creation of an innovative nomogram that integrates ARGs. The utility of this prognostic tool was underscored by Decision Curve Analysis (DCA), which illuminate its prospective benefits in guiding clinical interventions. Results: In our study, We discerned a set of 17 survival-pertinent, anoikis-related differentially expressed genes (DEGs) in CESC, from which nine were meticulously selected for the construction of prognostic models. The derived prognostic risk score was subsequently validated as an autonomous prognostic determinant. Through comprehensive functional analyses, we observed distinct immune profiles and drug response patterns among divergent prognostic stratifications. Further, we integrated the risk scores with the clinicopathological characteristics of CESC to develop a robust nomogram. DCA corroborated the utility of our model, demonstrating its potential to enhance patient outcomes through tailored clinical treatment strategies. Conclusion: The predictive signature, encompassing nine pivotal genes, alongside the meticulously constructed nomogram developed in this research, furnishes clinicians with a sophisticated tool for tailoring treatment strategies to individual patients diagnosed with CESC.

Anoikis resistance regulates immune infiltration and drug sensitivity in clear-cell renal cell carcinoma: insights from multi omics, single cell analysis and in vitro experiment.

Background: Anoikis is a form of programmed cell death essential for preventing cancer metastasis. In some solid cancer, anoikis resistance can facilitate tumor progression. However, this phenomenon is underexplored in clear-cell renal cell carcinoma (ccRCC). Methods: Using SVM machine learning, we identified core anoikis-related genes (ARGs) from ccRCC patient transcriptomic data. A LASSO Cox regression model stratified patients into risk groups, informing a prognostic model. GSVA and ssGSEA assessed immune infiltration, and single-cell analysis examined ARG expression across immune cells. Quantitative PCR and immunohistochemistry validated ARG expression differences between immune therapy responders and non-responders in ccRCC. Results: ARGs such as CCND1, CDKN3, PLK1, and BID were key in predicting ccRCC outcomes, linking higher risk with increased Treg infiltration and reduced M1 macrophage presence, indicating an immunosuppressive environment facilitated by anoikis resistance. Single-cell insights showed ARG enrichment in Tregs and dendritic cells, affecting immune checkpoints. Immunohistochemical analysis reveals that ARGs protein expression is markedly elevated in ccRCC tissues responsive to immunotherapy. Conclusion: This study establishes a novel anoikis resistance gene signature that predicts survival and immunotherapy response in ccRCC, suggesting that manipulating the immune environment through these ARGs could improve therapeutic strategies and prognostication in ccRCC.

Identification of anoikis-related gene signatures and construction of the prognosis model in prostate cancer.

Background: One of the primary reasons for tumor invasion and metastasis is anoikis resistance. Biochemical recurrence (BCR) of prostate cancer (PCa) serves as a harbinger of its distant metastasis. However, the role of anoikis in PCa biochemical recurrence has not been fully elucidated. Methods: Differential expression analysis was used to identify anoikis-related genes based on the TCGA and GeneCards databases. Prognostic models were constructed utilizing LASSO regression, univariate and multivariate Cox regression analyses. Moreover, Gene Expression Omnibus datasets (GSE70770 and GSE46602) were applied as validation cohorts. Gene Ontology, KEGG and GSVA were utilized to explore biological pathways and molecular mechanisms. Further, immune profiles were assessed using CIBERSORT, ssGSEA, and TIDE, while anti-cancer drugs sensitivity was analyzed by GDSC database. In addition, gene expressions in the model were examined using online databases (Human Protein Atlas and Tumor Immune Single-Cell Hub). Results: 113 differentially expressed anoikis-related genes were found. Four genes (EEF1A2, RET, FOSL1, PCA3) were selected for constructing a prognostic model. Using the findings from the Cox regression analysis, we grouped patients into groups of high and low risk. The high-risk group exhibited a poorer prognosis, with a maximum AUC of 0.897. Moreover, larger percentage of immune infiltration of memory B cells, CD8 Tcells, neutrophils, and M1 macrophages were observed in the high-risk group than those in the low-risk group, whereas the percentage of activated mast cells and dendritic cells in the high-risk group were lower. An increased TIDE score was founded in the high-risk group, suggesting reduced effectiveness of ICI therapy. Additionally, the IC50 results for chemotherapy drugs indicated that the low-risk group was more sensitive to most of the drugs. Finally, the genes EEF1A2, RET, and FOSL1 were expressed in PCa cases based on HPA website. The TISCH database suggested that these four ARGs might contribute to the tumor microenvironment of PCa. Conclusion: We created a risk model utilizing four ARGs that effectively predicts the risk of BCR in PCa patients. This study lays the groundwork for risk stratification and predicting survival outcomes in PCa patients with BCR.

An anoikis-related gene signature predicts prognosis, drug sensitivity, and immune microenvironment in cholangiocarcinoma.

Background: Cholangiocarcinoma is a malignant invasive biliary tract carcinoma with a poor prognosis. Anoikis-related genes are prognostic features of a variety of cancers. However, the value of prognostication and therapeutic effect of anoikis-related genes in cholangiocarcinoma have not been reported. The aim of this research was developing an ARGs signature associated with cholangiocarcinoma patients. Methods: We introduced transcriptome data to discover genes that were differentially expressed in cholangiocarcinoma. Subsequently, WGCNA was utilized to screen critical module genes in reference to anoikis. The univariate Cox, Lasso regression and Kaplan-Meier survival were executed to build a prognostic signature. We further performed gene functional enrichment, immune microenvironment and immunotherapy analysis between two risk subgroups. Finally, the pRRophetic algorithm was applied to compare the half inhibitory concentration value of several drugs. Results: A grand total of 1844 genes with differential expression related to the cholangiocarcinoma patients were identified. Furthermore, we obtained 2678 key module genes related to anoikis. Then, a prognostic signature was developed using the 6 prognostic genes (FXYD2, PCBD1, C1RL, GMNN, LAMA4 and HACL1). Independent prognostic analysis showed that risk score and alcohol could function as separate prognostic variables. We found cetain distinction in the immune microenvironment between the two risk subgroups. Moreover, immunotherapy evaluation showed that the anoikis-related gene signature could be applied as a therapy predictor. Finally, Chemotherapeutic drug sensitivity results showed that the low-risk group responded better to bosutinib, gefitinib, gemcitabine, and paclitaxel, while the high-risk group responded better to axitinib, cisplatin, and imatinib. Conclusion: The prognostic signature comprised of FXYD2, PCBD1, C1RL, GMNN, LAMA4 and HACL1 based on anoikis-related genes was established, which provided theoretical basis and reference value for the research and treatment of cholangiocarcinoma.

Arsenic Nanoparticles Trigger Apoptosis via Anoikis Induction in OECM-1 Cells.

Arsenic compounds have been used as therapeutic alternatives for several diseases including cancer. In the following work, we obtained arsenic nanoparticles (AsNPs) produced by an anaerobic bacterium from the Salar de Ascotán, in northern Chile, and evaluated their effects on the human oral squamous carcinoma cell line OECM-1. Resazurin reduction assays were carried out on these cells using 1-100 µM of AsNPs, finding a concentration-dependent reduction in cell viability that was not observed for the non-tumoral gastric mucosa-derived cell line GES-1. To establish if these effects were associated with apoptosis induction, markers like Bcl2, Bax, and cleaved caspase 3 were analyzed via Western blot, executor caspases 3/7 via luminometry, and DNA fragmentation was analyzed by TUNEL assay, using 100 µM cisplatin as a positive control. OECM-1 cells treated with AsNPs showed an induction of both extrinsic and intrinsic apoptotic pathways, which can be explained by a significant decrease in P-Akt/Akt and P-ERK/ERK relative protein ratios, and an increase in both PTEN and p53 mRNA levels and Bit-1 relative protein levels. These results suggest a prospective mechanism of action for AsNPs that involves a potential interaction with extracellular matrix (ECM) components that reduces cell attachment and subsequently triggers anoikis, an anchorage-dependent type of apoptosis.

SIRT6 Inhibits Anoikis of Colorectal Cancer Cells by Down-Regulating NDRG1.

Anoikis, a form of apoptosis resulting from the loss of cell-extracellular matrix interaction, is a significant barrier to cancer cell metastasis. However, the epigenetic regulation of this process remains to be explored. Here, we demonstrate that the histone deacetylase sirtuin 6 (SIRT6) plays a pivotal role in conferring anoikis resistance to colorectal cancer (CRC) cells. The protein level of SIRT6 is negatively correlated with anoikis in CRC cells. The overexpression of SIRT6 decreases while the knockdown of SIRT6 increases detachment-induced anoikis. Mechanistically, SIRT6 inhibits the transcription of N-myc downstream-regulated gene 1 (NDRG1), a negative regulator of the AKT signaling pathway. We observed the up-regulation of SIRT6 in advanced-stage CRC samples. Together, our findings unveil a novel epigenetic program regulating the anoikis of CRC cells.

Construction and validation of a prognostic signature based on anoikis-related lncRNAs in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and is characterized by a high death rate and a poor prospect for survival. Anoikis, which is a kind of programmed cell apoptosis, is an important factor in the advancement of tumors. Nonetheless, the function of anoikis-related lncRNAs (ARLRs) in LUAD is still not well understood. The TCGA database was queried for genomic and clinical information. A prognostic signature for ARLRs was established via the use of coexpression analysis and Cox regression. Validation of the model's accuracy was conducted utilizing K-M curves and receiver operating characteristic (ROC) curves, and the signature was utilized to develop a nomogram. LncRNAs were implicated in the progression of tumors, as determined by functional enrichment analysis. There was an improvement in prognosis, increased immune cell infiltration, and higher immune scores among the low-risk patients. Additionally, we found that the two groups had varied anticancer drug sensitivities, which could help guide treatment. The impact of one ARLR, AC026355.2, on migration and invasion was validated by in vitro experiments in LUAD cells. Herein, a new lncRNA signature associated with anoikis was identified and estimated, potentially serving as a prognostic indicator for LUAD patients.

ZEB family is a prognostic biomarker and correlates with anoikis and immune infiltration in kidney renal clear cell carcinoma.

BACKGROUND: Zinc finger E-box binding homEeobox 1 (ZEB1) and ZEB2 are two anoikis-related transcription factors. The mRNA expressions of these two genes are significantly increased in kidney renal clear cell carcinoma (KIRC), which are associated with poor survival. Meanwhile, the mechanisms and clinical significance of ZEB1 and ZEB2 upregulation in KIRC remain unknown. METHODS: Through the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, expression profiles, prognostic value and receiver operating characteristic curves (ROCs) of ZEB1 and ZEB2 were evaluated. The correlations of ZEB1 and ZEB2 with anoikis were further assessed in TCGA-KIRC database. Next, miRTarBase, miRDB, and TargetScan were used to predict microRNAs targeting ZEB1 and ZEB2, and TCGA-KIRC database was utilized to discern differences in microRNAs and establish the association between microRNAs and ZEBs. TCGA, TIMER, TISIDB, and TISCH were used to analyze tumor immune infiltration. RESULTS: It was found that ZEB1 and ZEB2 expression were related with histologic grade in KIRC patient. Kaplan-Meier survival analyses showed that KIRC patients with low ZEB1 or ZEB2 levels had a significantly lower survival rate. Meanwhile, ZEB1 and ZEB2 are closely related to anoikis and are regulated by microRNAs. We constructed a risk model using univariate Cox and LASSO regression analyses to identify two microRNAs (hsa-miR-130b-3p and hsa-miR-138-5p). Furthermore, ZEB1 and ZEB2 regulate immune cell invasion in KIRC tumor microenvironments. CONCLUSIONS: Anoikis, cytotoxic immune cell infiltration, and patient survival outcomes were correlated with ZEB1 and ZEB2 mRNA upregulation in KIRC. ZEB1 and ZEB2 are regulated by microRNAs.

Pharmacologically inducing anoikis offers novel therapeutic opportunities in hepatocellular carcinoma.

Tumor metastasis occurs in hepatocellular carcinoma (HCC), leading to tumor progression and therapeutic failure. Anoikis is a matrix detachment-induced apoptosis, also known as detachment-induced cell death, and mechanistically prevents tumor cells from escaping their native extracellular matrix to metastasize to new organs. Deciphering the regulators and mechanisms of anoikis in cancer metastasis is urgently needed to treat HCC. Several natural and synthetic products induce anoikis in HCC cells and in vivo models. Here, we first briefly summarize the current understanding of the molecular mechanisms of anoikis regulation and relevant regulators involved in HCC metastasis. Then we discuss the therapeutic potential of pharmacological induction of anoikis as a potential treatment against HCC. Finally, we discuss the key limitations of this therapeutic paradigm and propose possible strategies to overcome them. Cumulatively this review suggests that the pharmacological induction of anoikis can be used a promising therapeutic modality against HCC.

Vimentin-mediated buffering of internal integrin ß1 pool increases survival of cells from anoikis.

BACKGROUND: The intermediate filament protein vimentin is widely recognized as a molecular marker of epithelial-to-mesenchymal transition. Although vimentin expression is strongly associated with cancer metastatic potential, the exact role of vimentin in cancer metastasis and the underlying mechanism of its pro-metastatic functions remain unclear. RESULTS: This study revealed that vimentin can enhance integrin ß1 surface expression and induce integrin-dependent clustering of cells, shielding them against anoikis cell death. The increased integrin ß1 surface expression in suspended cells was caused by vimentin-mediated protection of the internal integrin ß1 pool against lysosomal degradation. Additionally, cell detachment was found to induce vimentin Ser38 phosphorylation, allowing the translocation of internal integrin ß1 to the plasma membrane. Furthermore, the use of an inhibitor of p21-activated kinase PAK1, one of the kinases responsible for vimentin Ser38 phosphorylation, significantly reduced cancer metastasis in animal models. CONCLUSIONS: These findings suggest that vimentin can act as an integrin buffer, storing internalized integrin ß1 and releasing it when needed. Overall, this study provides insights regarding the strong correlation between vimentin expression and cancer metastasis and a basis for blocking metastasis using this novel therapeutic mechanism.

Protein Structure Inspired Drug Discovery.

Drug discovery starts with known function, either of a compound or a protein, in-turn prompting investigations to probe 3D structure of the compound-protein interface. As protein structure determines function, we hypothesized that unique 3D structural motifs represent primary information denoting unique function that can drive discovery of novel agents. Using a physics-based protein structure analysis platform developed by us, designed to conduct computationally intensive analysis at supercomputing speeds, we probed a high-resolution protein x-ray crystallographic library developed by us. We selected 3D structural motifs whose function was not otherwise established, that offered environments supporting binding of drug-like chemicals and were present on proteins that were not established therapeutic targets. For each of eight potential binding pockets on six different proteins we accessed a 60 million compound library and used our analysis platform to evaluate binding. Using eight-day colony formation assays acquired compounds were screened for efficacy against human breast, prostate, colon and lung cancer cells and toxicity against human bone marrow stem cells. Compounds selectively inhibiting cancer growth segregated to two pockets on separate proteins. The compound, Dxr2-017, exhibited selective activity against human melanoma cells in the NCI-60 cell line screen, had an IC50 of 19 nM against human melanoma M14 cells in our eight-day assay, while over 2100-fold higher concentrations inhibited stem cells by less than 30%. We show that Dxr2-017 induces anoikis, a unique form of programmed cell death in need of targeted therapeutics. The predicted target protein for Dxr2-017 is expressed in bacteria, not in humans. This supports our strategy of focusing on unique 3D structural motifs. It is known that functionally important 3D structures are evolutionarily conserved. Here we demonstrate proof-of-concept that protein structure represents high value primary data to support discovery of novel therapeutics. This approach is widely applicable.

Targeting anoikis resistance as a strategy for cancer therapy.

Anoikis, known as matrix detachment-induced apoptosis or detachment-induced cell death, is crucial for tissue development and homeostasis. Cancer cells develop means to evade anoikis, e.g. anoikis resistance, thereby allowing for cells to survive under anchorage-independent conditions. Uncovering the mechanisms of anoikis resistance will provide details about cancer metastasis, and potential strategies against cancer cell dissemination and metastasis. Here, we summarize the principal elements and core molecular mechanisms of anoikis and anoikis resistance. We discuss the latest progress of how anoikis and anoikis resistance are regulated in cancers. Furthermore, we summarize emerging data on selective compounds and nanomedicines, explaining how inhibiting anoikis resistance can serve as a meaningful treatment modality against cancers. Finally, we discuss the key limitations of this therapeutic paradigm and possible strategies to overcome them. In this review, we suggest that pharmacological modulation of anoikis and anoikis resistance by bioactive compounds could surmount anoikis resistance, highlighting a promising therapeutic regimen that could be used to overcome anoikis resistance in cancers.

System analysis based on Anoikis-related genes identifies MAPK1 as a novel therapy target for osteosarcoma with neoadjuvant chemotherapy.

BACKGROUND: Osteosarcoma (OS) is the most common bone malignant tumor in children, and its prognosis is often poor. Anoikis is a unique mode of cell death.However, the effects of Anoikis in OS remain unexplored. METHOD: Differential analysis of Anoikis-related genes was performed based on the metastatic and non-metastatic groups. Then LASSO logistic regression and SVM-RFE algorithms were applied to screen out the characteristic genes. Later, Univariate and multivariate Cox regression was conducted to identify prognostic genes and further develop the Anoikis-based risk score. In addition, correlation analysis was performed to analyze the relationship between tumor microenvironment, drug sensitivity, and prognostic models. RESULTS: We established novel Anoikis-related subgroups and developed a prognostic model based on three Anoikis-related genes (MAPK1, MYC, and EDIL3). The survival and ROC analysis results showed that the prognostic model was reliable. Besides, the results of single-cell sequencing analysis suggested that the three prognostic genes were closely related to immune cell infiltration. Subsequently, aberrant expression of two prognostic genes was identified in osteosarcoma cells. Nilotinib can promote the apoptosis of osteosarcoma cells and down-regulate the expression of MAPK1. CONCLUSIONS: We developed a novel Anoikis-related risk score model, which can assist clinicians in evaluating the prognosis of osteosarcoma patients in clinical practice. Analysis of the tumor immune microenvironment and chemotherapeutic drug sensitivity can provide necessary insights into subsequent mechanisms. MAPK1 may be a valuable therapeutic target for neoadjuvant chemotherapy in osteosarcoma.

ASS1 Enhances Anoikis Resistance via AMPK/CPT1A-mediated Fatty Acid Metabolism in Ovarian Cancer.

Metastasis is the leading cause of death in ovarian cancer (OC), with anoikis resistance being a crucial step for detached OC cells survival. Despite extensive research, targeting anoikis resistance remians a challenge. Here, we identify argininosuccinate synthase 1 (ASS1), a key enzyme in urea cycle, is markedly upregulated in OC cells in detached culture and is associated with increased anoikis resistance and metastasis. Disruption of the AMP/ATP balance by elevated ASS1 activates AMPK and its downstream factor, CPT1A. Then, ASS1 enhances FAO, leading to higher ATP generation and lipid utilization. Inhibition of CPT1A reverses ASS1-induced FAO. Our study gives some new functional insights into OC metabolism and represents a shift from traditional views, expanding ASS1's relevance beyond nitrogen metabolism to fatty acid metabolism. It uncovers how ASS1-induced FAO disrupts the AMP/ATP balance, leading to AMPK activation. By identifying the ASS1/AMPK/CPT1A axis as crucial for OC anoikis resistance and metastasis, our study opens up new avenues for therapeutic interventions.

A novel anoikis-related gene signature can predict the prognosis of hepatocarcinoma patients.

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.

Construction of a gene signature associated with anoikis to evaluate the prognosis and immune infiltration in patients with colorectal cancer.

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.

Anoikis-related gene signatures predict prognosis of lung adenocarcinoma patients and reveal immune infiltration.

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.

Clinicopathological differences between EGFR mutated and EGFR wild-type lung adenocarcinoma with papillary predominant pattern.

OBJECTIVES: We aimed to reveal the clinicopathological differences between epidermal growth factor receptor (EGFR)-mutated and wild-type (WT) lung adenocarcinoma (LUAD) focusing on the predominant subtype. METHODS: This study included 352 with EGFR mutation and 370 with WT patients in consecutive stage I LUAD classified by the predominant subtype, and their clinicopathological characteristics and prognosis were analyzed. Using the Cancer Genome Atlas Program (TCGA) cohort, we analyzed differences in gene expression between EGFR mutation and WT groups. Furthermore, we performed immunohistochemical evaluations for 46 with EGFR mutation and 47 with WT patients in consecutive stage I papillary predominant adenocarcinoma (PPA). RESULTS: Compared to the PPA with WT [n = 115], those with EGFR mutation [n = 99] exhibited smaller invasive size (p = 0.03) and less frequent vessel invasion (p < 0.01). However, PPA with EGFR mutation showed significantly worse 5-ys recurrence-free survival (RFS) rates compared to those with WT (70.6 % versus 83.3 %, p = 0.03). Contrarily, no significant differences were observed in other predominant subtypes. In the TCGA cohort, PPA with EGFR mutation tended to show higher expression of galectin-3, which is associated with tumor metastasis and resistance to anoikis, compared to those with WT (p = 0.06). Immunohistochemical evaluation revealed that galectin-3 expression was significantly higher in PPA with EGFR mutation than in those with WT (p < 0.01). CONCLUSIONS: The prognosis of PPA with EGFR mutation proved to be less favorable compared to that with WT, and galectin-3 is highly expressed in EGFR-mutated PPA.

CircRNA regulates the liquid-liquid phase separation of ATG4B, a novel strategy to inhibit cancer metastasis?

Anoikis is a common programmed death for most of detached cells, but cancer cells can obtain anoikis resistance to facilitate their distant metastasis through the circulation system. Researches have indicated that enhanced autophagic flux accounts for the survival of many cancer cells under detached conditions. Targeting ATG4B, the key factor of autophagy progress, can inhibit cancer metastasis in vitro, but ATG4B-deficient mice are susceptible to many serious diseases, which indicates the potential uncontrolled side effects of direct targeting of ATG4B. In our recent research, we confirmed that ATG4B is a novel RNA binding protein in the gastric cancer (GC) cell. It interacts with circSPECC1 which consequently facilitates the liquid-liquid phase separation and ubiquitination of ATG4B. Additionally, the m6A reader ELAVL1 inhibits the expression of circSPECC1 to enhance the expression of ATG4B and anoikis resistance of GC cells. Further, we screened out an FDA-approved compound, lopinavir, to restore circSPECC1 abundance and suppress GC metastasis. In conclusion, our research identified a novel signal pathway (ELAVL1-circSPECC1-ATG4B-autophagy) to facilitate anoikis resistance and metastasis of GC cells and screened out a compound with clinical application potential to block this pathway, providing a novel strategy for the prevention of GC metastasis.