Anoikis in cancer: The role of lipid signaling

ANXA5 predicts prognosis and immune response and mediates proliferation and migration in head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy that often develops unnoticed. Typically, these tumors are identified at advanced stages, resulting in a relatively low chance of successful treatment. Anoikis serves as a natural defense against the spread of tumor cells, meaning circumventing anoikis can effectively inhibit tumor metastasis. Nonetheless, studies focusing on anoikis in the context of HNSCC remain scarce.

METHODS

Anoikis-related genes (ARGs) were identified by using the GeneCards and Harmonizome databases. Expression data of these genes and relevant clinical features were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A LASSO regression and a prognostic risk score model were developed to determine their prognostic significance. The analysis included the use of the CIBERSORT algorithm to quantify immune and stromal cell presence. Furthermore, in vitro and in vivo, we confirmed the expression and functional roles of proteins and mRNA of genes independently predictive of prognosis.

RESULTS

The study identified eight genes linked to prognosis (ANXA5, BAK1, CDKN2A, PPARG, CCR7, MAPK11, CRYAB, CRYBA1) and developed a prognostic model that effectively forecasts the survival outcomes for patients with HNSCC. A higher survival likelihood is associated with lower risk scores. In addition, a significant relationship was found between immune and risk score, and ANXA5 deletion promoted the killing of HNSCC cells by activated CD8+ T cells. During the screening process, 65 different chemotherapeutic drugs were found to have significant differences in IC50 values when comparing high- and low-risk categories. ANXA5 emerged as a gene with independent prognostic significance, exhibiting notably elevated protein and mRNA levels in HNSCC tissue compared to non-tumorous tissue. The suppression of ANXA5 gene activity resulted in a substantial decrease in both the growth and mobility of HNSCC cells. Animal model experiments demonstrated that inhibiting ANXA5 suppressed HNSCC growth and migration in vivo.

CONCLUSION

Through bioinformatics, a prognostic risk model of high precision was developed, offering valuable insights into the survival rates and immune responses in patients with HNSCC. ANXA5 is highlighted as a significant prognostic factor among the identified genes, indicating its promise as a potential therapeutic target for those with HNSCC.

Novel anoikis-related diagnostic biomarkers for aortic dissection based on machine learning

Aortic dissection (AD) is one of the most dangerous diseases of the cardiovascular system, which is characterized by acute onset and poor prognosis, while the pathogenesis of AD is still unclear and may affect or even delay the diagnosis of AD. Anchorage-dependent cell death (Anoikis) is a special mode of cell death, which is programmed cell death caused by normal cells after detachment from extracellular matrix (ECM) and has been widely studied in the field of oncology in recent years. In this study, we applied bioinformatics analysis, according to the results of research analysis and Gene Ontology (GO), as well as Kyoto Encyclopedia of Genes and Genomes (KEGG), finally found 3 anoikis-related genes (ARGs) based on machine learning. Among these, TP53 and TUBB3 were further verified by receiver operating characteristic (ROC), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA)and other methods. We hypothesize ARGs may be involved in the pathogenesis of AD through pathways such as oxidative stress, inflammatory response, and ECM. Therefore, we conclude that these ARGs can be potential factors in determining the diagnosis of AD.

Identification and Validation of Molecular Features of the Anoikis Gene-Related Hub Genes in Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma (NPC) is a malignant tumor originating from nasopharyngeal mucosa. Anoikis, a form of programmed cell death induced by detachment from the extracellular matrix, normally prevents metastasis. Resistance to anoikis in cancer cells can enhance their metastatic potential. This study identifies anoikis-related genes (ARGs) associated with NPC to elucidate tumorigenesis mechanisms. Analysis of the GSE12452 dataset from GEO revealed 77 differentially expressed ARGs in NPC tissues. GO and KEGG analyses highlighted significant enrichment in apoptosis-related pathways. A PPI network identified MYC, FN1, BRCA1, and FGF2 as Hub genes. Correlation analysis showed MYC positively correlated with activated dendritic cells (p < 0.01) but negatively with naive CD4 T cells (p < 0.001). FN1 was positively correlated with activated dendritic cells (p < 0.01) and negatively with M1 macrophages (p < 0.05). FGF2 negatively correlated with naive CD4 T cells (p < 0.001), while BRCA1 was positively correlated with eosinophils (p < 0.01). GSVA and GSEA indicated that MYC, FN1, BRCA1, and FGF2 were significantly enriched in cell cycle and DNA replication pathways. Immunohistochemistry and qPCR of 50 NPC samples confirmed the overexpression of these genes. Knockdown of MYC, FN1, BRCA1, and FGF2 led to increased tumor cell malignancy, with statistical significance (p < 0.05). This study identifies MYC, FN1, BRCA1, and FGF2 as anoikis-related genes (ARGs) with significant regulatory roles in nasopharyngeal carcinoma (NPC). These ARGs are found to be involved in the development and progression of NPC, suggesting their potential as therapeutic targets for this cancer.

HIF1A/PCDH7 axis mediates fatty acid synthesis and metabolism to inhibit lung adenocarcinoma anoikis

BACKGROUND

Aberrantly expressed PCDH7 participates in the malignant progression of many cancers. PCDH7 has been newly discovered as a risk factor in lung cancer, but its functional study in lung adenocarcinoma (LUAD) has not been conducted yet. This study aimed to investigate the functional role of PCDH7 in LUAD.

METHODS

Bioinformatics analyzed the expression of PCDH7 and HIF1A in LUAD tissues, predicted the binding sites between the two, analyzed the clinicopathological relevance of PCDH7 and examined the pathway enrichment of PCDH7. Expression of PCDH7 and HIF1A in LUAD cells was analyzed by RT-qPCR. A nude mouse transplantation tumor model was constructed to analyze the effect of PCDH7 on tumor growth in vivo. The binding relationship between PCDH7 and HIF1A was confirmed by chromatin immunoprecipitation experiments and the dual-luciferase assay. Cell viability was detected with Cell Counting Kit-8. Triglyceride content and Caspase3 activity were measured using corresponding reagent kits. FASN and ACC1 expression was determined utilizing western blot.

RESULTS

PCDH7 was highly expressed in LUAD and correlated with patients' overall survival time and N stage. In vitro and in vivo experiments confirmed that PCDH7 could promote LUAD growth and anoikis resistance. Moreover, overexpression of PCDH7 markedly increased the content of triglycerides in cells and promoted the expression of FASN and ACC1 proteins to inhibit LUAD cell anoikis. Cell rescue experiment confirmed that HIF1A activated PCDH7 to suppress LUAD anoikis by promoting fatty acid (FA) synthesis and metabolism.

CONCLUSION

Our findings demonstrated that the HIF1A/PCDH7 axis suppressed LUAD anoikis by promoting FA synthesis and metabolism. The FA synthesis pathway might be a key pathway regulated by PCDH7 in LUAD anoikis.

Anoikis-related genes in breast cancer patients: reliable biomarker of prognosis

BACKGROUND

Breast cancer (BC) is the most common cancer in women, and its progression is closely related to the phenomenon of anoikis. Anoikis, the specific programmed death resulting from a lack of contact between cells and the extracellular matrix, has recently been recognized as playing a critical role in tumor initiation, maintenance, and treatment. The ability of cancer cells to resist anoikis leads to cancer progression and metastatic colonization. However, the impact of anoikis on the prognosis of BC patients remains unclear.

METHOD

This study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect transcriptome and clinical data of BC patients. Anoikis-related genes (ARGs) were classified into subtypes A and B through consensus clustering. Subsequently, survival prognosis analysis, immune cell infiltration analysis, and functional enrichment analysis were performed for both subtypes. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a set of 10 ARGs related to prognosis was identified. Immune cell infiltration and tumor microenvironment analyses were conducted on these 10 ARGs to develop a prognostic model. Furthermore, single-cell data analysis and real-time polymerase chain reaction (RT-PCR) analysis were employed to study the expression of the 10 identified prognostic ARGs in BC cells.

RESULTS

One hundred thirty-five ARGs were identified as differentially expressed genes in the TCGA and GEO databases, with 42 of them associated with the survival prognosis of BC patients. Analyses involving Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) revealed distinct expression patterns of ARGs between types A and B. Patients in type A exhibited worse survival prognosis and lower immune cell infiltration compared to type B. Subsequent analyses identified 10 key ARGs (YAP1, PIK3R1, BAK1, PHLDA2, EDA2R, LAMB3, CD24, SLC2A1, CDC25C, and SLC39A6) relevant to BC prognosis. Kaplan-Meier analysis indicated that high-risk patients based on these ARGs had a poorer BC prognosis. Additionally, Cox regression analysis established gender, age, T (tumor), N (nodes), and risk score as predictive factors in a nomogram model for BC. The model demonstrated diagnostic value for BC patients at 1, 3, and 5 years. Decision curve analysis (DCA) verified the risk score as a reliable predictor of BC patient survival rates. Moreover, RT-PCR results confirmed differential expressions of YAP1, PIK3R1, BAK1, PHLDA2, CD24, SLC2A1, and CDC25C in BC cells, with SLC39A6, EDA2R, and LAMB3 showing low expression levels.

CONCLUSION

ARGs markers can be used as BC biomarkers for risk stratification and survival prediction in BC patients. Besides, ARGs can be used as stratification factors for individualized and precise treatment of BC patients.

Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC), ranking as the second-leading cause of global mortality among malignancies, poses a substantial burden on public health worldwide. Anoikis, a type of programmed cell death, serves as a barrier against the dissemination of cancer cells to distant organs, thereby constraining the progression of cancer. Nevertheless, the mechanism of genes related to anoikis in HCC is yet to be elucidated.

Methods

This paper's data (TCGA-HCC) were retrieved from the database of the Cancer Genome Atlas (TCGA). Differential gene expression with prognostic implications for anoikis was identified by performing both the univariate Cox and differential expression analyses. Through unsupervised cluster analysis, we clustered the samples according to these DEGs. By employing the least absolute shrinkage and selection operator Cox regression analysis (CRA), a clinical predictive gene signature was generated from the DEGs. The Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to determine the proportions of immune cell types. The external validation data (GSE76427) were procured from Gene Expression Omnibus (GEO) to verify the performance of the clinical prognosis gene signature. Western blotting and immunohistochemistry (IHC) analysis confirmed the expression of risk genes.

Results

In total, 23 prognostic DEGs were identified. Based on these 23 DEGs, the samples were categorized into four distinct subgroups (clusters 1, 2, 3, and 4). In addition, a clinical predictive gene signature was constructed utilizing ETV4, PBK, and SLC2A1. The gene signature efficiently distinguished individuals into two risk groups, specifically low and high, demonstrating markedly higher survival rates in the former group. Significant correlations were observed between the expression of these risk genes and a variety of immune cells. Moreover, the outcomes from the validation cohort analysis aligned consistently with those obtained from the training cohort analysis. The results of Western blotting and IHC showed that ETV4, PBK, and SLC2A1 were upregulated in HCC samples.

Conclusion

The outcomes of this paper underscore the effectiveness of the clinical prognostic gene signature, established utilizing anoikis-related genes, in accurately stratifying patients. This signature holds promise in advancing the development of personalized therapy for HCC.

Comprehensive analysis and experimental verification of the mechanism of anoikis related genes in pancreatic cancer

Background

Pancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear.

Methods

Study data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature.

Results

A total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples.

Conclusion

Our study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.

Revisiting the potential of regulated cell death in glioma treatment: a focus on autophagy-dependent cell death, anoikis, ferroptosis, cuproptosis, pyroptosis, immunogenic cell death, and the crosstalk between them

Gliomas are primary tumors that originate in the central nervous system. The conventional treatment options for gliomas typically encompass surgical resection and temozolomide (TMZ) chemotherapy. However, despite aggressive interventions, the median survival for glioma patients is merely about 14.6 months. Consequently, there is an urgent necessity to explore innovative therapeutic strategies for treating glioma. The foundational study of regulated cell death (RCD) can be traced back to Karl Vogt's seminal observations of cellular demise in toads, which were documented in 1842. In the past decade, the Nomenclature Committee on Cell Death (NCCD) has systematically classified and delineated various forms and mechanisms of cell death, synthesizing morphological, biochemical, and functional characteristics. Cell death primarily manifests in two forms: accidental cell death (ACD), which is caused by external factors such as physical, chemical, or mechanical disruptions; and RCD, a gene-directed intrinsic process that coordinates an orderly cellular demise in response to both physiological and pathological cues. Advancements in our understanding of RCD have shed light on the manipulation of cell death modulation - either through induction or suppression - as a potentially groundbreaking approach in oncology, holding significant promise. However, obstacles persist at the interface of research and clinical application, with significant impediments encountered in translating to therapeutic modalities. It is increasingly apparent that an integrative examination of the molecular underpinnings of cell death is imperative for advancing the field, particularly within the framework of inter-pathway functional synergy. In this review, we provide an overview of various forms of RCD, including autophagy-dependent cell death, anoikis, ferroptosis, cuproptosis, pyroptosis and immunogenic cell death. We summarize the latest advancements in understanding the molecular mechanisms that regulate RCD in glioma and explore the interconnections between different cell death processes. By comprehending these connections and developing targeted strategies, we have the potential to enhance glioma therapy through manipulation of RCD.

Integrative analysis of Anoikis-related genes reveals that FASN is a novel prognostic biomarker and promotes the malignancy of bladder cancer via Wnt/β-catenin pathway

Bladder cancer (BC) exhibits diversity in clinical outcomes and is characterized by heterogeneity. Anoikis, a form of programmed cell death, plays a crucial role in facilitating tumor invasion and metastasis. This study comprehensively investigated the genetic landscape of BC progression, identifying 300 differentially expressed Anoikis-related genes (DE-ARGs) through in-depth analysis of the GSE13507 datasets. Functional enrichment analysis revealed associations with diverse diseases and biological processes. Employing machine learning algorithms, a logistic regression model based on nine marker genes demonstrated superior accuracy in distinguishing BC from normal samples. Validation in TCGA datasets highlighted the prognostic significance of LRP1, FASN, and SIRT6, suggesting their potential as cancer biomarkers. Particularly, FASN emerged as an independent prognostic indicator, regulating BC cell proliferation and metastasis through the Wnt/β-catenin pathway. The study provides crucial insights into altered genetic landscapes and potential therapeutic strategies for BC, emphasizing the significance of FASN in BC prognosis and progression.

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.

Anoikis-related gene signatures in colorectal cancer: implications for cell differentiation, immune infiltration, and prognostic prediction

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.

Development of an anoikis-related gene signature and prognostic model for predicting the tumor microenvironment and response to immunotherapy in colorectal cancer

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 in vitro and in vivo. 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.

Therapeutic vulnerabilities in triple negative breast cancer: Stem-like traits explored within molecular classification

Triple Negative Breast Cancer (TNBC) is the most aggressive type of breast cancer (BC). Despite advances in the clinical management of TNBC, recurrence-related mortality remains a challenge. The stem-like phenotype of TNBC plays a significant role in the persistence of minimal disease residue after therapy. Individuals exhibiting stem-like characteristics are particularly prone to inducing malignant relapse accompanied by strong resistance. Therefore, stem-like traits have been broadly proposed as therapeutic vulnerabilities to treat TNBC and reduce recurrence. However, heterogeneity within TNBC often generally restricts the stability of the therapeutic efficacy. To understand the heterogeneity and manage TNBC more precisely, multiple TNBC subtyping categories have been reported, providing the basis for profile-according therapeutic regimens. To provide more insight into targeting stem-like traits to ablate TNBC and reduce recurrence in the context of heterogeneity, this paper reviewed the molecular subtyping of TNBC, identified the consensus subtypes with distinct stem-like phenotypes, characterized the stemness hierarchy of TNBC, outlined the biological models for stem-like TNBC subtypes, summarized the therapeutic vulnerabilities in stem-like traits of the subtypes, and proposed potential therapeutic regimens targeting stem-like characteristics to improve TNBC prognosis.

Phenotypic Transitions the Processes Involved in Regulation of Growth and Proangiogenic Properties of Stem Cells, Cancer Stem Cells and Circulating Tumor Cells

Epithelial-mesenchymal transition (EMT) is a crucial process with significance in the metastasis of malignant tumors. It is through the acquisition of plasticity that cancer cells become more mobile and gain the ability to metastasize to other tissues. The mesenchymal-epithelial transition (MET) is the return to an epithelial state, which allows for the formation of secondary tumors. Both processes, EMT and MET, are regulated by different pathways and different mediators, which affects the sophistication of the overall tumorigenesis process. Not insignificant are also cancer stem cells and their participation in the angiogenesis, which occur very intensively within tumors. Difficulties in effectively treating cancer are primarily dependent on the potential of cancer cells to rapidly expand and occupy secondarily vital organs. Due to the ability of these cells to spread, the concept of the circulating tumor cell (CTC) has emerged. Interestingly, CTCs exhibit molecular diversity and stem-like and mesenchymal features, even when derived from primary tumor tissue from a single patient. While EMT is necessary for metastasis, MET is required for CTCs to establish a secondary site. A thorough understanding of the processes that govern the balance between EMT and MET in malignancy is crucial.

Anoikis-related signature predicts prognosis and characterizes immune landscape of ovarian cancer

Ovarian cancer (OV) is the most lethal gynecological malignancy worldwide, with high recurrence rates. Anoikis, a newly-acknowledged form of programmed cell death, plays an essential role in cancer progression, though studies focused on prognostic patterns of anoikis in OV are still lacking. We filtered 32 potential anoikis-related genes (ARGs) among the 6406 differentially expressed genes (DEGs) between the 180 normal controls and 376 TCGA-OV samples. Through the LASSO-Cox analysis, a 2-gene prognostic signature, namely AKT2, and DAPK1, was finally distinguished. We then demonstrated the promising prognostic value of the signature through the K-M survival analysis and time-dependent ROC curves (p-value < 0.05). Moreover, based on the signature and clinical features, we constructed and validated a nomogram model for 1-year, 3-year, and 5-year overall survival, with reliable prognostic values in both TCGA-OV training cohort (p-value < 0.001) and ICGC-OV validation cohort (p-value = 0.030). We evaluated the tumor immune landscape through the CIBERSORT algorithm, which indicated the upregulation of resting Myeloid Dendritic Cells (DCs), memory B cells, and naïve B cells and high expression of key immune checkpoint molecules (CD274 and PDCD1LG2) in the high-risk group. Interestingly, the high-risk group exhibited better sensitivity toward immunotherapy and less sensitivity toward chemotherapies, including Cisplatin and Bleomycin. Especially, based on the IHC of tissue microarrays among 125 OV patients at our institution, we reported that aberrant upregulation of DAPK1 was related to poor prognosis. Conclusively, the anoikis-related signature was a promising tool to evaluate prognosis and predict therapy responses, thus assisting decision-making in the realm of OV precision medicine.

Death-associated protein 3 in cancer-discrepant roles of DAP3 in tumours and molecular mechanisms

Cancer, ranks as the secondary cause of death, is a group of diseases that are characterized by uncontrolled tumor growth and distant metastasis, leading to increased mortality year-on-year. To date, targeted therapy to intercept the aberrant proliferation and invasion is crucial for clinical anticancer treatment, however, mutant expression of target genes often leads to drug resistance. Therefore, it is essential to identify more molecules that can be targeted to facilitate combined therapy. Previous studies showed that death associated protein 3 (DAP3) exerts a pivotal role in regulating apoptosis signaling of tumors, meanwhile, aberrant DAP3 expression is associated with the tumorigenesis and disease progression of various cancers. This review provides an overview of the molecule structure of DAP3 and the discrepant roles played by DAP3 in various types of tumors. Considering the molecular mechanism of DAP3-regulated cancer development, new potential treatment strategies might be developed in the future.

Lipid alterations play a role in the integration of PD-1/PD-L1 inhibitors and anlotinib for the treatment of advanced non-small-cell lung cancer

BACKGROUND

Studies have shown that integrating anlotinib with programmed death 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors enhances survival rates among progressive non-small-cell lung cancer (NSCLC) patients lacking driver mutations. However, not all individuals experience clinical benefits from this therapy. As a result, it is critical to investigate the factors that contribute to the inconsistent response of patients. Recent investigations have emphasized the importance of lipid metabolic reprogramming in the development and progression of NSCLC.

METHODS

The objective of this investigation was to examine the correlation between lipid variations and observed treatment outcomes in advanced NSCLC patients who were administered PD-1/PD-L1 inhibitors alongside anlotinib. A cohort composed of 30 individuals diagnosed with advanced NSCLC without any driver mutations was divided into three distinct groups based on the clinical response to the combination treatment, namely, a group exhibiting partial responses, a group manifesting progressive disease, and a group demonstrating stable disease. The lipid composition of patients in these groups was assessed both before and after treatment.

RESULTS

Significant differences in lipid composition among the three groups were observed. Further analysis revealed 19 differential lipids, including 2 phosphatidylglycerols and 17 phosphoinositides.

CONCLUSION

This preliminary study aimed to explore the specific impact of anlotinib in combination with PD-1/PD-L1 inhibitors on lipid metabolism in patients with advanced NSCLC. By investigating the effects of using both anlotinib and PD-1/PD-L1 inhibitors, this study enhances our understanding of lipid metabolism in lung cancer treatment. The findings from this research provide valuable insights into potential therapeutic approaches and the identification of new therapeutic biomarkers.

Deciphering the significance of anoikis in bladder cancer and systematic analysis of S100A7 as a potential therapeutic target

BACKGROUND

Bladder cancer is an epidemic and life-threating urologic carcinoma. Anoikis is a unusual type of programmed cell death which plays a vital role in tumor survival, invasion and metastasis. Nevertheless, the relationship between anoikis and bladder cancer has not been understood thoroughly.

METHODS

We downloaded the transcriptome and clinical information of BLCA patients from TCGA and GEO databases. Then, we analyzed different expression of anoikis-related genes and established a prognostic model based on TCGA database by univariate Cox regression, lasso regression, and multivariate Cox regression. Then the Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves were performed. GEO database was used for external validation. BLCA patients in TCGA database were divided into two subgroups by non-negative matrix factorization (NMF) classification. Survival analysis, different gene expression, immune cell infiltration and drug sensitivity were calculated. Finally, we verified the function of S100A7 in two BLCA cell lines.

RESULTS

We developed a prognostic risk model based on three anoikis-related genes including TPM1, RAC3 and S100A7. The overall survival of BLCA patients in low-risk groups was significantly better than high-risk groups in training sets, test sets and external validation sets. Subsequently, the checkpoint and immune cell infiltration had significant difference between two groups. Then we identified two subtypes (CA and CB) through NMF analysis and found CA had better OS and PFS than CB. Besides, the accuracy of risk model was verified by ROC analysis. Finally, we identified that knocking down S100A7 gene expression restrained the proliferation and invasion of bladder cancer cells.

CONCLUSION

We established and validated a bladder cancer prognostic model consisting of three genes, which can effectively evaluate the prognosis of bladder cancer patients. Additionally, through cellular experiments, we demonstrated the significant role of S100A7 in the metastasis and invasion of bladder cancer, suggesting its potential as a novel target for future treatments.

The Prognosis of Cancer Depends on the Interplay of Autophagy, Apoptosis, and Anoikis within the Tumor Microenvironment

Within the tumor microenvironment, the fight between the immune system and cancer influences tumor transformation. Metastasis formation is an important stage in the progression of cancer. This process is aided by cellular detachment and resistance to anoikis, which are achieved by altering intercellular signaling. Autophagy, specifically pro-survival autophagy, aids cancer cells in developing treatment resistance. Numerous studies have shown that autophagy promotes tumor growth and resistance to anoikis. To regulate protective autophagy, cancer-related genes phosphorylate both pro- and anti-apoptotic proteins. Apoptosis, a type of controlled cell death, eliminates damaged or unwanted cells. Anoikis is a type of programmed cell death in which cells lose contact with the extracellular matrix. The dysregulation of these cellular pathways promotes tumor growth and spread. Apoptosis, anoikis, and autophagy interact meticulously and differently depending on the cellular circumstances. For instance, autophagy can protect cancer cells from apoptosis by removing cellular components that are damaged and might otherwise trigger apoptotic pathways. Similarly, anoikis dysregulation can trigger autophagy by causing cellular harm and metabolic stress. In order to prevent or treat metastatic disease, specifically, targeting these cellular mechanisms may present a promising prospect for cancer therapy. This review discourses the state of our understanding of the molecular and cellular mechanisms underlying tumor transformation and the establishment of metastatic tumors. To enhance the prognosis for cancer, we highlight and discuss potential therapeutic approaches that target these processes and genes involved in them.

Predicting the prognosis, immune response, and immunotherapy in head and neck squamous cell carcinoma using a novel risk model based on anoikis-related lncRNAs

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is an extremely heterogeneous and metastatic disease. Anoikis, which is a specific type of programmed apoptosis, is involved in tumor metastasis, tissue homeostasis, and development. Herein, we constructed an anoikis-related long non-coding RNA (lncRNA) signature to predict the prognosis, immune responses, and therapeutic effects in HNSCC patients.

METHODS

A total of 501 HNSCC samples were acquired from the TCGA database and randomly classified into the training and validation groups (1:1 ratio). Thereafter, the results derived from the training set were analyzed with the LASSO regression analysis, and a novel anoikis-related lncRNA risk model was constructed. Time-dependent ROC curves and Kaplan-Meier analysis were carried out to assess the diagnostic value and survival outcomes. A nomogram was utilized to predict the prognostic accuracy. Furthermore, we studied the tumor microenvironment, tumor mutation burden, enrichment pathways, and the response to chemotherapy and immunotherapy.

RESULTS

Seven anoikis-related lncRNAs (AC015878.1, CYTOR, EMSLR, LINC01503, LINC02084, RAB11B-AS1, Z97200.1) were screened to design a novel risk model, which was recognized as the independent prognostic factor for HNSCC patients. The findings implied that low-risk patients showed significantly longer OS, PFS, and DSS compared to those high-risk patients. The two groups that were classified using the risk model showed significant differences in their immune landscape. The risk model also predicted that low-risk HNSCC patients could attain a better response to immunotherapy, while high-risk patients would be more sensitive to gemcitabine, docetaxel, and cisplatin.

CONCLUSIONS

We constructed a novel risk model that could be employed for effectively predicting patient prognosis with a good independent prognostic value for HNSCC patients. Furthermore, this model could be used for designing new immunotherapeutic and chemotherapeutic strategies, and it helps clinicians establish personalized and detailed strategies for HNSCC patients.

Anoikis-associated signatures predict prognosis and immune response in bladder cancer

Objective: Anoikis is a type of programmed cell death that occurs in normal epithelial and endothelial cells. However, the specific role of anoikis regulators (ANRs) in bladder cancer (BLCA) remains unknown. Therefore, the objective of this study was to find subgroups that could identify different levels of anoikis resistance in BLCA and construct an anoikis scoring system to assess prognosis. Method: By obtaining ANRs from public datasets, subgroups of BLCA with varying degrees of anoikis resistance were identified, and risk was determined. Result: ANRs affects the occurrence and prognosis of BLCA and can be predicted by establishing risk models. Conclusion: The anoikis scoring system and anoikis-associated risk profiles may help develop more effective and personalized treatment strategies for BLCA patients.

Regulation of anoikis by extrinsic death receptor pathways

Metastatic cancer cells can develop anoikis resistance in the absence of substrate attachment and survive to fight tumors. Anoikis is mediated by endogenous mitochondria-dependent and exogenous death receptor pathways, and studies have shown that caspase-8-dependent external pathways appear to be more important than the activity of the intrinsic pathways. This paper reviews the regulation of anoikis by external pathways mediated by death receptors. Different death receptors bind to different ligands to activate downstream caspases. The possible mechanisms of Fas-associated death domain (FADD) recruitment by Fas and TNF receptor 1 associated-death domain (TRADD) recruitment by tumor necrosis factor receptor 1 (TNFR1), and DR4- and DR5-associated FADD to induce downstream caspase activation and regulate anoikis were reviewed. This review highlights the possible mechanism of the death receptor pathway mediation of anoikis and provides new insights and research directions for studying tumor metastasis mechanisms. Video Abstract.

Development of the prognostic value in lung adenocarcinoma based on anoikis-related genes and initial experimental validation

BACKGROUND

Lung adenocarcinoma (LUAD) is a highly aggressive cancer in advanced stages and has the highest cancer-related death across the world. Anoikis has emerged as a specific form of apoptotic cell death that may play a vital role in the formation and development of tumors.

METHODS

Based on The Cancer Genome Atlas dataset, we developed a novel anoikis-related genes (ARGs) signature in LUAD and evaluated the differences between low and high-risk groups in clinical characteristics, expression patterns, immune cell infiltration, and drug sensitivity, etc. According to multivariate Cox regression analysis, the risk score was identified as a significant independent prognostic factor. The possible biological pathways of ARGs' were assessed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. The immune infiltration landscape and risk score of ARGs were analyzed by ESTIMATE and CIBERSORT analysis. A nomogram grounded on six key ARGs and clinicopathological features was provided. Moreover, experiment validation of the expression patterns of six hub ARGs in lung cancer cell lines was conducted.

RESULTS

We identified 53 survival-related LUAD anoikis-related differentially expressed genes and finally six hub anoikis genes (LDHA, SLC2A1, SERPINB5, ITGB4, BRCA2, and PIK3R1) were selected to construct an ARG model. The risk model could efficiently cluster the patients into low- and high-risk groups which could accurately predict clinical outcomes for LUAD patients. There is evidence that the prognostic risk score is a remarkable prognostic factor in determining overall survival. Different immune statuses and drug sensitivity between low- and high-risk groups were explored according to functional analysis. On the basis of risk scores and LUAD clinicopathological features, a novel nomogram was developed. Ultimately, all six key genes except for PIK3R1 were proved to be upregulated in LUAD tissues and cell lines by bioinformatics analysis and experimental validation.

CONCLUSIONS

The result of the present study suggest that ARGs could be carcinogenic to LUAD and could be used as an effective stratification factor to customize therapies and forecast the survival rate in LUAD patients.

Development of a novel, clinically relevant anoikis-related gene signature to forecast prognosis in patients with prostate cancer

Introduction: Anoikis is a specific form of programmed cell death and is related to prostate cancer (PC) metastasis. This study aimed to develop a reliable anoikis-related gene signature to accurately forecast PC prognosis. Methods: Based on anoikis-related genes and The Cancer Genome Atlas (TCGA) data, anoikis-related molecular subtypes were identified, and their differences in disease-free survival (DFS), stemness, clinical features, and immune infiltration patterns were compared. Differential expression analysis of the two subtypes and weighted gene co-expression network analysis (WGCNA) were employed to identify clinically relevant anoikis-related differentially expressed genes (DEGs) between subtypes, which were then selected to construct a prognostic signature. The clinical utility of the signature was verified using the validation datasets GSE116918 and GSE46602. A nomogram was established to predict patient survival. Finally, differentially enriched hallmark gene sets were revealed between the different risk groups. Results: Two anoikis-related molecular subtypes were identified, and cluster 1 had poor prognosis, higher stemness, advanced clinical features, and differential immune cell infiltration. Next, 13 clinically relevant anoikis-related DEGs were identified, and five of them (CKS2, CDC20, FMOD, CD38, and MSMB) were selected to build a prognostic signature. This gene signature had a high prognostic value. A nomogram that combined Gleason score, T stage, and risk score could accurately predict patient survival. Furthermore, gene sets closely related with DNA repair were differentially expressed in the different risk groups. Conclusion: A novel, clinically relevant five-anoikis-related gene signature was a powerful prognostic biomarker for PC.

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

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.

A novel anoikis-related gene prognostic signature and its correlation with the immune microenvironment in colorectal cancer

Background: Anoikis is a type of apoptosis associated with cell detachment. Resistance to anoikis is a focal point of tumor metastasis. This study aimed to explore the relationship among anoikis-related genes (ARGs), immune infiltration, and prognosis in colorectal cancer (CRC). Methods: The transcriptome profile and clinical data on patients with CRC were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. Patients were divided into two clusters based on the expression of ARGs. Differences between the two ARG molecular subtypes were analyzed in terms of prognosis, functional enrichment, gene mutation frequency, and immune cell infiltration. An ARG-related prognostic signature for predicting overall survival in patients with CRC was developed and validated using absolute value convergence and selection operator (LASSO) regression analysis. The correlation between the signature risk score and clinicopathological features, immune cell infiltration, immune typing, and immunotherapy response was analyzed. The risk score combined with clinicopathological characteristics was used to construct a nomogram to assess CRC patients' prognosis. Results: Overall, 151 ARGs were differentially expressed in CRC. Two ARG subtypes, namely, ARG-high and ARG-low groups, were identified and correlated with CRC prognosis. The gene mutation frequency and immune, stromal, and ESTIMATE scores of the ARG-high group were higher than those of the ARG-low group. Moreover, CD8, natural killer cells, M1 macrophages, human leukocyte antigen (HLA), and immune checkpoint-related genes were significantly increased in the ARG-high group. An optimized 25-gene CRC prognostic signature was successfully constructed, and its prognostic predictive ability was validated. The high-risk score was correlated with T, N, M, and TNM stages. Risk scores were negatively correlated with dendritic cells, eosinophils, and CD4 cells, and significantly positively correlated with regulatory T cells. Patients in the high-risk group were more likely to exhibit immune unresponsiveness. Finally, the nomogram model was constructed and showed good prognostic predictive power. Conclusion: ARGs are associated with clinicopathological features and the prognosis of CRC, and play important roles in the immune microenvironment. Herein, we underpinned the usefulness of ARGs in CRC to develop more effective immunotherapy techniques.

Identification of Anoikis-Related Subgroups and Prognosis Model in Liver Hepatocellular Carcinoma

Resistance to anoikis is a key characteristic of many cancer cells, promoting cell survival. However, the mechanism of anoikis in hepatocellular carcinoma (HCC) remains unknown. In this study, we applied differentially expressed overlapping anoikis-related genes to classify The Cancer Genome Atlas (TCGA) samples using an unsupervised cluster algorithm. Then, we employed weighted gene coexpression network analysis (WGCNA) to identify highly correlated genes and constructed a prognostic risk model based on univariate Cox proportional hazards regression. This model was validated using external datasets from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). Finally, we used a CIBERSORT algorithm to investigate the correlation between risk score and immune infiltration. Our results showed that the TCGA cohorts could be divided into two subgroups, with subgroup A having a lower survival probability. Five genes (BAK1, SPP1, BSG, PBK and DAP3) were identified as anoikis-related prognostic genes. Moreover, the prognostic risk model effectively predicted overall survival, which was validated using ICGC and GEO datasets. In addition, there was a strong correlation between infiltrating immune cells and prognostic genes and risk score. In conclusion, we identified anoikis-related subgroups and prognostic genes in HCC, which could be significant for understanding the molecular mechanisms and treatment of HCC.

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

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.