TCF7L2 promotes anoikis resistance and metastasis of gastric cancer by transcriptionally activating PLAUR

Prognostic analysis of anoikis-related genes in bladder cancer: An observational study

Anoikis is proved to play a crucial role in the development of cancers. However, the impact of anoikis on the prognosis of bladder cancer (BLCA) is currently unknown. Thus, this study aimed to find potential effect of anoikis in BLCA. The Cancer Genome Atlas (TCGA)-BLCA and GSE13507 cohorts were downloaded from TCGA and the Gene Expression Omnibus (GEO) databases, respectively. Differentially expressed genes (DEGs) were screened between BLCA and normal groups, which intersected with anoikis-related genes to yield anoikis-related DEGs (AR DEGs). Univariate COX, rbsurv, and multivariate COX analyses were adopted in order to build a prognostic risk model. The differences of risk score in the different clinical subgroups and the relevance between survival rate and clinical characteristics were explored as well. Finally, chemotherapy drug sensitivity in different risk groups was analyzed. In total, 78 AR DEGs were acquired and a prognostic signature was build based on the 6 characteristic genes (CALR, FASN, CSPG4, HGF, INHBB, SATB1), where the patients of low-risk group had longer survival time. The survival rate of BLCA patients was significantly differential in different groups of age, stage, smoking history, pathologic-T, and pathologic-N. The IC50 of 56 drugs showed significant differences between 2 risk groups, such as imatinib, docetaxel, and dasatinib. At last, the results of real time quantitative-polymerase chain reaction (RT-qPCR) demonstrated that the expression trend of CALR, HGF, and INHBB was consistent with the result obtained previously based on public databases. Taken together, this study identified 6 anoikis-related characteristic genes (CALR, FASN, CSPG4, HGF, INHBB, SATB1) for the prognosis of BLCA patients, providing a scientific reference for further research on BLCA.

Identifying the Pattern Characteristics of Anoikis-Related Genes in Keloid

Objective: Anoikis is a kind of programmed cell death that is triggered when cells lose contact with each other or with the matrix. However, the potential value of anoikis-related genes (ARGs) in keloid (KD) has not been investigated. Approach: We downloaded three keloid fibroblast (KF) RNA sequencing (RNA-seq) datasets from the Gene Expression Omnibus (GEO) and obtained 338 ARGs from a search of the GeneCards database and PubMed articles. Weighted correlation network analysis was used to construct the coexpression network and obtain the KF-related ARGs. The LASSO-Cox method was used to screen the hub ARGs and construct the best prediction model. Then, GEO single-cell sequencing datasets were used to verify the expression of hub genes. We used whole RNA-seq for gene-level validation and the correlation between KD immune infiltration and anoikis. Results: Our study comprehensively analyzed the role of ARGs in KD for the first time. The least absolute shrinkage and selection operator (LASSO) regression analysis identified six hub ARGs (HIF1A, SEMA7A, SESN1, CASP3, LAMA3, and SIK2). A large number of miRNAs participate in the regulation of hub ARGs. In addition, correlation analysis revealed that ARGs were significantly correlated with the infiltration levels of multiple immune cells in patients with KD. Innovation: We explored the expression characteristics of ARGs in KD, which is extremely important for determining the molecular pathways and mechanisms underlying KD. Conclusions: This study provides a useful reference for revealing the characteristics of ARGs in the pathogenesis of KD. The identified hub genes may provide potential therapeutic targets for patients. This study provides new ideas for individualized therapy and immunotherapy.

Lopinavir enhances anoikis by remodeling autophagy in a circRNA-dependent manner

Macroautophagy/autophagy-mediated anoikis resistance is crucial for tumor metastasis. As a key autophagy-related protein, ATG4B has been demonstrated to be a prospective anti-tumor target. However, the existing ATG4B inhibitors are still far from clinical application, especially for tumor metastasis. In this study, we identified a novel circRNA, circSPECC1, that interacted with ATG4B. CircSPECC1 facilitated liquid-liquid phase separation of ATG4B, which boosted the ubiquitination and degradation of ATG4B in gastric cancer (GC) cells. Thus, pharmacological addition of circSPECC1 may serve as an innovative approach to suppress autophagy by targeting ATG4B. Specifically, the circSPECC1 underwent significant m6A modification in GC cells and was subsequently recognized and suppressed by the m6A reader protein ELAVL1/HuR. The activation of the ELAVL1-circSPECC1-ATG4B pathway was demonstrated to mediate anoikis resistance in GC cells. Moreover, we also verified that the above pathway was closely related to metastasis in tissues from GC patients. Furthermore, we determined that the FDA-approved compound lopinavir efficiently enhanced anoikis and prevented metastasis by eliminating repression of ELAVL1 on circSPECC1. In summary, this study provides novel insights into ATG4B-mediated autophagy and introduces a viable clinical inhibitor of autophagy, which may be beneficial for the treatment of GC with metastasis.

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.

PLAUR facilitates the progression of clear cell renal cell carcinoma by activating the PI3K/AKT/mTOR signaling pathway

Background

PLAUR has been found upregulated in various tumors and closely correlated with the malignant phenotype of tumor cells. The aim of this study was to investigate the relationship between PLAUR and clear cell renal cell carcinoma (ccRCC) and its potential mechanism of promoting tumor progression.

Methods

The expression levels and clinical significance of PLAUR, along with the associated signaling pathways, were extensively investigated in ccRCC samples obtained from The Cancer Genome Atlas (TCGA). PLAUR expression in 20 pairs of ccRCC tumor tissues and the adjacent tissues was assessed using qRT-PCR and IHC staining. Additionally, a series of in vitro experiments were conducted to investigate the impact of PLAUR suppression on cellular proliferation, migration, invasion, cell cycle progression, and apoptosis in ccRCC. The Western blot analysis was employed to investigate the expression levels of pivotal genes associated with the PI3K/AKT/mTOR signaling pathway.

Results

The expression of PLAUR was significantly upregulated in ccRCC compared to normal renal tissues, and higher PLAUR expression in ccRCC was associated with a poorer prognosis than low expression. The in-vitro functional investigations demonstrated that knockdown of PLAUR significantly attenuated the proliferation, migration, and invasion capabilities of ccRCC cells. Concurrently, PLAUR knockdown effectively induced cellular apoptosis, modulated the cell cycle, inhibited the EMT process, and attenuated the activation of the PI3K/AKT/mTOR signaling pathway. PLAUR may represent a key mechanism underlying ccRCC progression.

Conclusions

The involvement of PLAUR in ccRCC progression may be achieved through the activation of the PI3K/AKT/mTOR signaling pathway, making it a reliable biomarker for the identification and prediction of ccRCC.

Integrated analysis of colorectal cancer metastasis identifies characteristics of tumor cell during metastasis

Background

Metastasis is the main cause of death in colorectal cancer (CRC). Metastasis is a sequential and dynamic process, but the development of tumor cells during this process is unclear. In this study, we aimed to reveal characteristics of tumor cell subset during CRC metastasis.

Methods

Single-cell RNA sequence CRC data of normal epithelium, non-metastatic primary tumor, metastatic primary tumor, and liver metastases from gene expression omnibus (GEO) dataset were analyzed to reveal characteristics of CRC metastasis. Primary tumor tissues of three non-metastatic CRC and three metastatic CRC patients from Union Hospital of Tongji Medical College (Wuhan, China) were used to verify the characteristics of CRC metastasis.

Results

We identified a metastasis-related cancer cell subset EP1, which was characterized with a high expression of KRT17, LAMC2, EMP1, and PLAC8. EP1 had an enhanced cell-cell interaction, which interacted with SPP+ macrophages and drove them toward anti-inflammatory and immunosuppressive phenotype. Dynamic changes in genes and TF regulons during the metastasis were also revealed.

Conclusions

This study advanced our understanding of the development of tumor cells during CRC metastasis and further identified metastasis-related subset and potential therapeutic targets for the treatment and prevention of CRC metastasis.

A novel anoikis-related signature predicts prognosis risk and treatment responsiveness in diffuse large B-cell lymphoma

BACKGROUND

Although anoikis plays a role in cancer metastasis and aggressiveness, it has rarely been reported in diffuse large B cell lymphoma (DLBCL).

METHODS

We obtained RNA sequencing data and matched clinical data from the GEO database. An anoikis-related genes (ARGs)-based risk signature was developed in GSE10846 training cohort and validated in three other cohorts. Additionally, we predicted half-maximal inhibitory concentration (IC50) of drugs based on bioinformatics method and obtained the actual IC50 to some chemotherapy drugs via cytotoxicity assay.

RESULTS

The high-risk group, as determined by our signature, was associated with worse prognosis and an immunosuppressive environment in DLBCL. Meanwhile, the nomogram based on eight variables had more accurate ability in forecasting the prognosis than the international prognostic index in DLBCL. The prediction of IC50 indicated that DLBCL patients in the high-risk group were more sensitive to doxorubicin, IPA-3, lenalidomide, gemcitabine, and CEP.701, while patients in the low-risk group were sensitive to cisplatin and dasatinib. Consistent with the prediction, cytotoxicity assay suggested the higher sensitivity to doxorubicin and gemcitabine and the lower sensitivity to dasatinib in the high-risk group in DLBCL.

CONCLUSION

The ARG-based signature may provide a promising direction for prognosis prediction and treatment optimization for DLBCL patients.

Clinical value of molecular subtypes identification based on anoikis-related lncRNAs in castration-resistant prostate cancer

BACKGROUND

Anoikis is a distinctive type of apoptosis. It is involved in tumor progression and metastasis. But its function in castration-resistant prostate cancer (CRPC) remains veiled. We aimed to develop a prognostic indicator based on anoikis-related long non-coding RNAs (arlncRNAs) and to investigate their biological function in CRPC.

MATERIAL AND METHOD

Differentially expressed anoikis-related genes were extracted from two CRPC datasets, GSE51873, and GSE78201. Four lncRNAs associated with the anoikis-related genes were selected. A risk model based on these lncRNAs was developed and validated in The Cancer Genome Atlas (TCGA) and the Memorial Sloan-Kettering Cancer Center (MSKCC) prostate cancer cohorts. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, immune infiltration, immune checkpoints expression, and drug susceptibility were performed based on the model. To identify the biofunction of anoikis-related lncRNA, CCK-8 assays, colony formation assays, and flow cytometry were used.

RESULT

Twenty-nine anoikis-related genes were differentially expressed in the CRPC datasets. And 36 prognostic arlncRNAs were selected for the LASSO Cox analysis. Patients were subsequently classified into two subtypes by constructing an anoikis-related lncRNA based prognostic index (ARPI). The accuracy of this index was validated. KEGG enrichment analysis revealed that the high-ARPI group was enriched in cancer-related and immune-related pathways. Immune infiltration analysis has indicated a positive association between high-ARPI groups and increased immune infiltration. Fulvestrant, OSI-027, Lapatinib, Dabrafenib, and Palbociclib were identified as potential sensitive drugs for high-ARPI patients. In vitro experiments exhibited that silencing LINC01138 dampened the proliferation, migration and enzalutamide resistance in CRPC. Furthermore, it stimulated apoptosis and inhibited the eithelial-mesenchymal transition process.

CONCLUSION

Four arlncRNAs were identified and a risk model was established to predict the prognosis of patients with prostate cancer. Immune infiltration and drug susceptibility analysis revealed a potential therapeutic strategy for patients with castration-resistant prostate cancer.

Identification of the molecular subtypes and signatures to predict the prognosis, biological functions, and therapeutic response based on the anoikis-related genes in colorectal cancer

BACKGROUND

Tumors that resist anoikis, a programmed cell death triggered by detachment from the extracellular matrix, promote metastasis; however, the role of anoikis-related genes (ARGs) in colorectal cancer (CRC) stratification, prognosis, and biological functions remains unclear.

METHODS

We obtained transcriptomic profiles of CRC and 27 ARGs from The Cancer Genome Atlas, the Gene Expression Omnibus, and MSigDB databases, respectively. CRC tissue samples were classified into two clusters based on the expression pattern of ARGs, and their functional differences were explored. Hub genes were screened using weighted gene co-expression network analysis, univariate analysis, and least absolute selection and shrinkage operator analysis, and validated in cell lines, tissues, or the Human Protein Atlas database. We constructed an ARG-risk model and nomogram to predict prognosis in patients with CRC, which was validated using an external cohort. Multifaceted landscapes, including stemness, tumor microenvironment (TME), immune landscape, and drug sensitivity, between high- and low-risk groups were examined.

RESULTS

Patients with CRC were divided into C1 and C2 clusters. Cluster C1 exhibited higher TME scores, whereas cluster C2 had favorable outcomes and a higher stemness index. Eight upregulated hub ARGs (TIMP1, P3H1, SPP1, HAMP, IFI30, ADAM8, ITGAX, and APOC1) were utilized to construct the risk model. The qRT-PCR, Western blotting, and immunohistochemistry results were consistent with those of the bioinformatics analysis. Patients with high risk exhibited worse overall survival (p < 0.01), increased stemness, TME, immune checkpoint expression, immune infiltration, tumor mutation burden, and drug susceptibility compared with the patients with low risk.

CONCLUSION

Our results offer a novel CRC stratification based on ARGs and a risk-scoring system that could predict the prognosis, stemness, TME, immunophenotypes, and drug susceptibility of patients with CRC, thereby improving their prognosis. This stratification may facilitate personalized therapies.

Anoikis-related genes as potential prognostic biomarkers in gastric cancer: A multilevel integrative analysis and predictive therapeutic value

BACKGROUND

Gastric cancer (GC) is a frequent malignancy of the gastrointestinal tract. Exploring the potential anoikis mechanisms and pathways might facilitate GC research.

PURPOSE

The authors aim to determine the significance of anoikis-related genes (ARGs) in GC prognosis and explore the regulatory mechanisms in epigenetics.

METHODS

After describing the genetic and transcriptional alterations of ARGs, we searched differentially expressed genes (DEGs) from the cancer genome atlas and gene expression omnibus databases to identify major cancer marker pathways. The non-negative matrix factorisation algorithm, Lasso, and Cox regression analysis were used to construct a risk model, and we validated and assessed the nomogram. Based on multiple levels and online platforms, this research evaluated the regulatory relationship of ARGs with GC.

RESULTS

Overexpression of ARGs is associated with poor prognosis, which modulates immune signalling and promotes anti-anoikis. The consistency of the DEGs clustering with weighted gene co-expression network analysis results and the nomogram containing 10 variable genes improved the clinical applicability of ARGs. In anti-anoikis mode, cytology, histology, and epigenetics could facilitate the analysis of immunophenotypes, tumour immune microenvironment (TIME), and treatment prognosis.

CONCLUSION

A novel anoikis-related prognostic model for GC is constructed, and the significance of anoikis-related prognostic genes in the TIME and the metabolic pathways of tumours is initially explored.

Ubiquitin specific peptidase 38 epigenetically regulates KLF transcription factor 5 to augment malignant progression of lung adenocarcinoma

Protein ubiquitination is a common post-translational modification and a critical mechanism for regulating protein stability. This study aimed to explore the role and potential molecular mechanism of ubiquitin-specific peptidase 38 (USP38) in the progression of lung adenocarcinoma (LUAD). USP38 expression was significantly higher in patients with LUAD than in their counterparts, and higher USP38 expression was closely associated with a worse prognosis. USP38 silencing suppresses the proliferation of LUAD cells in vitro and impedes the tumorigenic activity of cells in xenograft mouse models in vivo. Further, we found that USP38 affected the protein stability of transcription factor Krüppel-like factors 5 (KLF5) by inhibiting its degradation. Subsequent mechanistic investigations showed that the N-terminal of USP38 (residues 1-400aa) interacted with residues 1-200aa of KLF5, thereby stabilizing the KLF5 protein by deubiquitination. Moreover, we found that PIAS1-mediated SUMOylation of USP38 was promoted, whereas SENP2-mediated de-SUMOylation of USP38 suppressed the deubiquitination effects of USP38 on KLF5. Additionally, our results demonstrated that KLF5 overexpression restored the suppression of the malignant properties of LUAD cells by USP38 knockdown. SUMOylation of USP38 enhances the deubiquitination and stability of KLF5, thereby augmenting the malignant progression of LUAD.

Transcriptome and single-cell transcriptomics reveal prognostic value and potential mechanism of anoikis in skin cutaneous melanoma

BACKGROUND

Skin cutaneous melanoma (SKCM) is a highly lethal cancer, ranking among the top four deadliest cancers. This underscores the urgent need for novel biomarkers for SKCM diagnosis and prognosis. Anoikis plays a vital role in cancer growth and metastasis, and this study aims to investigate its prognostic value and mechanism of action in SKCM.

METHODS

Utilizing consensus clustering, the SKCM samples were categorized into two distinct clusters A and B based on anoikis-related genes (ANRGs), with the B group exhibiting lower disease-specific survival (DSS). Gene set enrichment between distinct clusters was examined using Gene Set Variation Analysis (GSVA) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.

RESULTS

We created a predictive model based on three anoikis-related differently expressed genes (DEGs), specifically, FASLG, IGF1, and PIK3R2. Moreover, the mechanism of these prognostic genes within the model was investigated at the cellular level using the single-cell sequencing dataset GSE115978. This analysis revealed that the FASLG gene was highly expressed on cluster 1 of Exhausted CD8( +) T (Tex) cells.

CONCLUSIONS

In conclusion, we have established a novel classification system for SKCM based on anoikis, which carries substantial clinical implications for SKCM patients. Notably, the elevated expression of the FASLG gene on cluster 1 of Tex cells could significantly impact SKCM prognosis through anoikis, thus offering a promising target for the development of immunotherapy for SKCM.

Metastatic outgrowth via the two-way interplay of autophagy and metabolism

Metastasis represents one of the most dangerous issue of cancer progression, characterized by intricate interactions between invading tumor cells, various proteins, and other cells on the way towards target sites. Tumor cells, while undergoing metastasis, engage in dynamic dialogues with stromal cells and undertake epithelial-mesenchymal transition (EMT) phenoconversion. To ensure survival, tumor cells employ several strategies such as restructuring their metabolic needs to adapt to the alterations of the microenvironmental resources via different mechanisms including macroautophagy (autophagy) and to circumvent anoikis-a form of cell death induced upon detachment from the extracellular matrix (ECM). This review focuses on the puzzling connections of autophagy and energetic metabolism within the context of cancer metastasis.

Immune-related long noncoding RNA zinc finger protein 710-AS1-201 promotes the metastasis and invasion of gastric cancer cells

BACKGROUND

Gastric cancer (GC) is a prevalent malignant tumor of the gastrointestinal system. ZNF710 is a transcription factor (TF), and zinc finger protein 710 (ZNF710)-AS1-201 is an immune-related long noncoding RNA (lncRNA) that is upregulated in GC cells.

AIM

To assess the correlation between ZNF710-AS1-201 and immune microenvironment features and to investigate the roles of ZNF710-AS1-201 in the invasion and metastasis processes of GC cells.

METHODS

We obtained data from The Cancer Genome Atlas and Wujin Hospital. We assessed cell growth, migration, invasion, and programmed cell death using cell counting kit-8, EdU, scratch, Transwell, and flow cytometry assays. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to identify the potential downstream targets of ZNF710-AS1-201.

RESULTS

In GC tissues with low ZNF710-AS1-201 expression, immunoassays detected significant infiltration of various antitumor immune cells, such as memory CD8 T cells and activated CD4 T cells. In the low-expression group, the half-maximal inhibitory concentrations (IC50s) of 5-fluorouracil, cisplatin, gemcitabine, and trametinib were lower, whereas the IC50s of dasatinib and vorinostat were higher. The malignant degree of GC was higher and the stage was later in the high-expression group. Additionally, patients with high expression of ZNF710-AS1-201 had lower overall survival and disease-free survival rates. In vitro, the overexpression of ZNF710-AS1-201 greatly enhanced growth, metastasis, and infiltration while suppressing cell death in HGC-27 cells. In contrast, the reduced expression of ZNF710-AS1-201 greatly hindered cell growth, enhanced apoptosis, and suppressed the metastasis and invasion of MKN-45 cells. The expression changes in ZNF710 were significant, but the corresponding changes in isocitrate dehydrogenase-2, Semaphorin 4B, ARHGAP10, RGMB, hsa-miR-93-5p, and ZNF710-AS1-202 were not consistent or statistically significant after overexpression or knockdown of ZNF710-AS1-201, as determined by qRT-PCR.

CONCLUSION

Immune-related lncRNA ZNF710-AS1-201 facilitates the metastasis and invasion of GC cells. It appears that ZNF710-AS1-201 and ZNF710 have potential as effective targets for therapeutic intervention in GC. Nevertheless, it is still necessary to determine the specific targets of the ZNF710 TF.

Prognostic value and immune landscapes of anoikis-associated lncRNAs in lung adenocarcinoma

BACKGROUND

Methods for predicting the outcome of lung adenocarcinoma (LUAD) in the clinic are limited. Anoikis is an important route to programmed cell death in LUAD, and the prognostic value of a model constructed with anoikis-related lncRNAs (ARlncRNAs) in LUAD is unclear.

METHODS

Transcriptome and basic information for LUAD patients was obtained from the Cancer Genome Atlas. Coexpression and Cox regression analyses were utilized to identify prognostically significant ARlncRNAs and construct a prognostic signature. Furthermore, the signature was combined with clinical characteristics to create a nomogram. Finally, we performed principal component, enrichment, tumor mutation burden (TMB), tumor microenvironment (TME) and drug sensitivity analyses to evaluate the basic research and clinical merit of the signature.

RESULTS

The prognostic signature developed with eleven ARlncRNAs can accurately predict that high-risk group patients have a worse prognosis, as proven by the receiver operating characteristic (ROC) curve (AUC: 0.718). Independent prognostic analyses indicated that the risk score is a significant independent prognostic element for LUAD (P<0.001). In the high-risk group, enrichment analysis demonstrated that glucose metabolism and DNA replication were the main enrichment pathways. TMB analysis indicated that the high-risk group had a high TMB (P<0.05). Drug sensitivity analyses can recognize drugs that are sensitive to different risk groups. Finally, 11 ARlncRNAs of this signature were verified by RT-qPCR analysis.

CONCLUSIONS

A novel prognostic signature developed with 11 ARlncRNAs can accurately predict the OS of LUAD patients and offer clinical guidance value for immunotherapy and chemotherapy treatment.

ITGBL1 promotes anoikis resistance and metastasis in human gastric cancer via the AKT/FBLN2 axis

The resistance to anoikis plays a critical role in the metastatic progression of various types of malignancies, including gastric cancer (GC). Nevertheless, the precise mechanism behind anoikis resistance is not fully understood. Here, our primary focus was to examine the function and underlying molecular mechanism of Integrin beta-like 1 (ITGBL1) in the modulation of anoikis resistance and metastasis in GC. The findings of our investigation have demonstrated that the overexpression of ITGBL1 significantly augmented the resistance of GC cells to anoikis and promoted their metastatic potential, while knockdown of ITGBL1 had a suppressive effect on both cellular processes in vitro and in vivo. Mechanistically, we proved that ITGBL1 has a role in enhancing the resistance of GC cells to anoikis and promoting metastasis through the AKT/Fibulin-2 (FBLN2) axis. The inhibition of AKT/FBLN2 signalling was able to reverse the impact of ITGBL1 on the resistance of GC cells to anoikis and their metastatic capability. Moreover, the expression levels of ITGBL1 were found to be significantly elevated in the cancerous tissues of patients diagnosed with GC, and there was a strong correlation observed between high expression levels of ITGBL1 and worse prognosis among individuals diagnosed with GC. Significantly, it was revealed that within our cohort of GC patients, individuals exhibiting elevated ITGBL1 expression and diminished FBLN2 expression experienced the worst prognosis. In conclusion, the findings of our study indicate that ITGBL1 may serve as a possible modulator of resistance to anoikis and the metastatic process in GC.

Extracellular vesicles from Fusobacterium nucleatum: roles in the malignant phenotypes of gastric cancer

The increase of the Fusobacterium nucleatum level has been previously identified in various cancers including gastric cancer (GC), but how the F. nucleatum exerts its carcinogenic role in GC remains unclear. Several studies revealed that F. nucleatum contributes to cancer progression via its secretion of extracellular vehicles (EVs). Hence, it's designed to reveal the influence of F. nucleatum-derived EVs (Fn-EVs) in GC progression. The tumor and adjacent tissues were collected from 30 GC patients, and the abundance of F. nucleatum was found to be highly expressed in tumor samples. The ultracentrifugation was employed to isolate EVs from F. nucleatum and Escherischia coli (E. coli), which were labeled Fn-EVs and E. coli-EVs, respectively. After treating GC cells with Fn-EVs and E. coli-EVs, cell counting kit 8, colony formation, wound healing as well as transwell assay were performed, which revealed that Fn-EVs effectively enhanced oxaliplatin resistance, and facilitated cell proliferation, migration, invasion, and stemness in GC cells while E. coli-EVs exert no significant effect on GC cells. Besides, the stemness and DNA repair of GC cells were also enhanced by Fn-EVs, as revealed by the sphere-forming assay and the detection of stemness- and DNA repair-associated proteins by western blotting. In vivo analyses demonstrated that Fn-EVs administration not only promoted GC tumor growth and liver metastasis but also conferred GC tumor resistance to oxaliplatin resistance. This study first revealed the contributive role of F. nucleatum in GC development via Fn-EVs, which provided a better perspective for manipulating F. nucleatum in treating GC patients with malignant phenotypes.

Multiple databases analyzed the prognosis prediction of renin secretion pathway-related genes in renal clear cell carcinoma and immunotherapy

Background

Clear cell renal cell carcinoma (ccRCC) is a malignant kidney tumour and its progression is associated with the renin secretion pathway, so this study aimed to develop a prognostic model based on renin secretion pathway-related genes.

Methods

First, 453 renin secretion pathway-related genes were acquired [|log fold change (FC)| >1.5, false discovery rate (FDR) <0.05] from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The data were combined and further screened for 188 genes associated with ccRCC prognosis (P<0.05) by univariate independent prognostic analysis. These genes were subjected to least absolute shrinkage and selection operator regression to identify potential prognostic genes to construct the prognostic model. The stability of the model was externally validated. Combined risk scores and clinical information were used to create nomograms to accurately reflect patient survival. The model-related genes were further mined for subsequent analysis.

Results

A prognostic model of six renin secretion pathway genes (IGFBP3, PLAUR, CHKB-CPT1B, HOXA13, CDH13, and CDC20) was developed. Its reliability in predicting disease prognosis was confirmed by survival analysis, receiver operating characteristic (ROC) curve analysis and a risk curve. The nomogram and calibration curve showed good accuracy. The immune-related analyses revealed that the low-risk group would benefit more from immunotherapy.

Conclusions

The prognostic model of ccRCC based on six renin secretion pathway-related genes can be used to guide the precise treatment of ccRCC patients.

Integration analysis of PLAUR as a sunitinib resistance and macrophage related biomarker in ccRCC, an in silicon and experimental study

Sunitinib remains the preferred systemic treatment option for specific patients with advanced RCC who are ineligible for immune therapy. However, it's essential to recognize that Sunitinib fails to elicit a favourable response in all patients. Moreover, most patients eventually develop resistance to Sunitinib. Therefore, identifying new targets associated with Sunitinib resistance is crucial. Utilizing multiple datasets from public cohorts, we conducted an exhaustive analysis and identified a total of 8 microRNAs and 112 mRNAs displaying significant expression differences between Sunitinib responsive and resistant groups. A particular set of six genes, specifically NIPSNAP1, STK40, SDC4, NEU1, TBC1D9, and PLAUR, were identified as highly significant via WGCNA. To delve deeper into the resistance mechanisms, we performed additional investigations using cell, molecular, and flow cytometry tests. These studies confirmed PLAUR's pivotal role in fostering Sunitinib resistance, both in vitro and in vivo. Our findings suggest that PLAUR could be a promising therapeutic target across various cancer types. In conclusion, this investigation not only uncovers vital genes and microRNAs associated with Sunitinib resistance in RCC but also introduces PLAUR as a prospective therapeutic target for diverse cancers. The outcomes contribute to advancing personalized healthcare and developing superior therapeutic strategies.Communicated by Ramaswamy H. Sarma.

Development of adaptive anoikis resistance promotes metastasis that can be overcome by CDK8/19 Mediator kinase inhibition

Anoikis resistance or evasion of cell death triggered by cell detachment into suspension is a hallmark of cancer that is concurrent with cell survival and metastasis. The effects of frequent matrix detachment encounters on the development of anoikis resistance in cancer remains poorly defined. Here we show using a panel of ovarian cancer models, that repeated exposure to suspension stress in vitro followed by attached recovery growth leads to the development of anoikis resistance paralleling in vivo development of anoikis resistance in ovarian cancer ascites. This resistance is concurrent with enhanced invasion, chemoresistance and the ability of anoikis adapted cells to metastasize to distant sites. Adapted anoikis resistant cells show a heightened dependency on oxidative phosphorylation and can also evade immune surveillance. We find that such acquired anoikis resistance is not genetic, as acquired resistance persists for a finite duration in the absence of suspension stress. Transcriptional reprogramming is however essential to this process, as acquisition of adaptive anoikis resistance in vitro and in vivo is exquisitely sensitive to inhibition of CDK8/19 Mediator kinase, a pleiotropic regulator of transcriptional reprogramming. Our data demonstrate that growth after recovery from repeated exposure to suspension stress is a direct contributor to metastasis and that inhibition of CDK8/19 Mediator kinase during such adaptation provides a therapeutic opportunity to prevent both local and distant metastasis in cancer.

A Prediction Model for Deciphering Intratumoral Heterogeneity Derived from the Microglia/Macrophages of Glioma Using Non-Invasive Radiogenomics

Microglia and macrophages play a major role in glioma immune responses within the glioma microenvironment. We aimed to construct a prognostic prediction model for glioma based on microglia/macrophage-correlated genes. Additionally, we sought to develop a non-invasive radiogenomics approach for risk stratification evaluation. Microglia/macrophage-correlated genes were identified from four single-cell datasets. Hub genes were selected via lasso-Cox regression, and risk scores were calculated. The immunological characteristics of different risk stratifications were assessed, and radiomics models were constructed using corresponding MRI imaging to predict risk stratification. We identified eight hub genes and developed a relevant risk score formula. The risk score emerged as a significant prognostic predictor correlated with immune checkpoints, and a relevant nomogram was drawn. High-risk groups displayed an active microenvironment associated with microglia/macrophages. Furthermore, differences in somatic mutation rates, such as IDH1 missense variant and TP53 missense variant, were observed between high- and low-risk groups. Lastly, a radiogenomics model utilizing five features from magnetic resonance imaging (MRI) T2 fluid-attenuated inversion recovery (Flair) effectively predicted the risk groups under a random forest model. Our findings demonstrate that risk stratification based on microglia/macrophages can effectively predict prognosis and immune functions in glioma. Moreover, we have shown that risk stratification can be non-invasively predicted using an MRI-T2 Flair-based radiogenomics model.

Inhibition of PI3K/Akt/mTOR Signaling Pathway Suppresses 5-Fluorouracil Resistance in Gastric Cancer

BACKGROUND

At present, 5-Fluorouracil (5-FU) is a crucial anti-cancer drug and is widely used for the treatment of various carcinomas, including gastric cancer (GC). The resistance of GC cells to 5-FU is still a matter of great concern.

OBJECTIVE

To illustrate the role of PI3K/Akt/mTOR signaling in regulating the cell cycle progression and migration of 5-FU-resistant GC cells.

MATERIAL AND METHODS

After the establishment of drug-resistant GC cell lines, the effects of 5-FU and/or BEZ235 (the dual inhibitor of PI3K and mTOR) on the activity of parental or drug-resistant GC cells were explored. The viability and localization of GC cells (MKN-45 and MKN-74) and their drug-resistant cells (MKN-45/R and MKN-74/R) were assessed using MTT assays and immunofluorescence staining. The impacts of 5-FU and/or BEZ235 on GC cell cycle progression and cell migration were assessed via flow cytometry analyses and wound healing assays, respectively. GC tissues were collected from patients with GC sensitive or refractory to 5-FU chemotherapy. RT-qPCR and western blot were conducted to measure PI3K, AKT, and mTOR levels in GC cells or tissues.

RESULTS

After 5-FU treatment, GC cells displayed 5-FU resistance and the viability of drug-resistant cells (MKN-45/R and MKN-74/R) was higher than that of parental cells (MKN-45 and MKN-74). The IC50 values for MKN-45 and MKN-45/R were 8.93 ug/ml and 140 ug/ml, and the values for MKN-74 and MKN-74/R were 3.93 ug/ml and 114.29 ug/ml. Additionally, the PI3K/Akt/mTOR signaling pathway was activated in drug-resistant GC cells and tumor tissues of patients refractory to 5-FU chemotherapy, as evidenced by high PI3K, Akt, and mTOR levels in MKN-45/R, MKN-74/R, and GC tissues resistant to 5-FU. BEZ235 promoted cell cycle arrest and suppressed the migration of GC cells. Moreover, the combination of BEZ235 and 5-FU led to more effective suppressive influence on cell cycle progression and cell migration relative to the single 5-FU or BEZ235 treatment.

CONCLUSIONS

Silencing of the PI3K/Akt/mTOR signaling pathway suppressed the 5-FU resistance of GC cells.

WIPF1 promotes gastric cancer progression by regulating PI3K/Akt signaling in a myocardin-dependent manner

Wiskott-Aldrich syndrome protein-interacting protein family member 1 (WIPF1) is associated with malignant tumor progression. However, molecular links between WIPF1 and gastric cancer (GC) remain elusive. The expression of WIPF1 was detected in GC tissues and cells. WIPF1 was overexpressed in GC tissues and cells and high expression of WIPF1 was an independent risk factor for a poor prognosis in patients with GC. Further experiments indicated that WIPF1 promoted the proliferation, invasion, and migration of GC cells in vivo and in vitro. WIPF1-regulated genes were closely related to cell proliferation and migration in GC, and silencing WIPF1 significantly repressed PI3K/AKT signaling pathway activation. WIPF1 was activated by myocardin (MYOCD) translation. Rescue experiments confirmed that MYOCD promotes the proliferation, invasion, and migration of GC cells in a WIPF1-dependent manner and activates the PI3K/AKT signaling pathway. MYOCD may transactivate WIPF1 and facilitate GC cell growth and metastasis by activating the PI3K/AKT signaling pathway.

A novel risk model for predicting peritoneal metastasis in colorectal cancer based on the SEER database

BACKGROUND

Early detection and intervention could significantly improve the prognosis of patients with peritoneal metastasis (PM). Our main purpose was to develop a model to predict the risk of PM in patients with colorectal cancer (CRC).

METHODS

Patients from the Surveillance, Epidemiology, and End Results (SEER) database with CRC classified according to the AJCC 8th TNM staging system were selected for the study. After data pre-processing, the dataset was divided into a training set and a validation set. In the training set, univariate logistic analysis and stepwise multivariate logistic regression analysis were utilized to screen clinical features and construct a risk prediction model. Then, we validated the model using the confusion matrix, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves to examine its performance.

RESULTS

The model constructed using stepwise multivariate logistic regression analysis incorporated the following eight clinical features: age, tumor location, histological type, T stage, carcinoembryonic antigen (CEA) level, tumor deposits (TDs), log odds (LODDS) of metastatic lymph nodes, and extraperitoneal metastasis (EM). The areas under the curve (AUCs) of the model in the training and validation sets were 0.924 and 0.912, respectively. The accuracy and the recall ratio were higher than 0.8 in both cohorts. DCA and the calibration curves also confirmed its excellent predictive power.

CONCLUSIONS

Our model can effectively predict the risk of PM in CRC patients, which is of great significance for the timely identification of patients at high risk of PM and further clinical decision-making.

STAT1 suppresses the transcriptional activity of TRIM21 in gastric cancer

PURPOSE

Tripartite motif-containing protein 21 (TRIM21) has E3 ubiquitin ligase activity and is involved in the regulation of various biological processes in vivo. TRIM21 has been found to have strong associations with various cancers. However, its role in gastric cancer is unclear.

METHODS

The TCGA database was screened to obtain TRIM21 using WGCNA and PPI analyses. The TCGA database was used to evaluate the correlation of TRIM21 expression with patients' clinical characteristics, prognosis, functional enrichment and immune cell infiltration. The role of TRIM21 in cell proliferation, apoptosis and invasion was verified by in vivo and in vitro assays. The UCSC and JASPAR databases were used to evaluate the regulatory role of STAT1 on TRIM21 transcription. Finally, dual-luciferase reporter assay was used to confirm the regulation of TRIM21 transcriptional activity by STAT1.

RESULTS

As a key gene, high expression of TRIM21 inhibited the gastric cancer growth and was significantly enriched in apoptosis, cell proliferation, and JAK/STAT signaling pathways. TRIM21 expression was positively correlated with a variety of TICs, including T cells, NK cells, and DCs. In vivo assays, TRIM21 inhibited functions in gastric cancer cell lines, including inhibition of proliferation and migration, and promotion of apoptosis. Database analysis and dual-luciferase reporter assay showed that STAT1 inhibited the transcriptional activity of TRIM21. In vivo assays confirmed that TRIM21 inhibited tumor growth, and STAT1 expression was negatively correlated with STAT1.

CONCLUSION

TRIM21 is a tumor-suppressive gene in gastric cancer, and its transcriptional activity is inhibited by STAT1.

Development of a Signature Based on Eight Metastatic-Related Genes for Prognosis of GC Patients

Gastric cancer (GC) has been a common tumor type with high mortality. Distal metastasis is one of the main causes of death in GC patients, which is also related to poor prognosis. The mRNA profiles and clinical information of GC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Univariate Cox and LASSO Cox analyses were used to screen the optimal metastasis-related genes (MRGs) to establish a prognostic Risk Score model for GC patients. The nomogram was used to visualize the Risk Score and predict the 1-, 3-, 5-year survival rate. The immune cell infiltration was analyzed by CIBERSORT and the ratio of immune-stromal component was calculated by the ESTIMATE algorithm. A total of 142 differentially expressed genes were identified between metastatic and non-metastatic GC samples. The optimal 8 genes, comprising GAMT (guanidinoacetate N-methyltransferase), ABCB5 (ATP-binding cassette subfamily B member 5), ITIH3 (inter-alpha-trypsin inhibitor heavy chain 3), GDF3 (growth differentiation factor 3), VSTM2L (V-set and transmembrane domain-containing 2 like), CIDEA (cell death inducing DFFA like effector a), NPTX1 (neuronal pentraxin-1), and UMOD (uromodulin), were further screened to establish a prognostic Risk Score, which proved to be an independent prognostic factor. Patients in high-risk group had a poor prognosis. There were significant differences in the proportion of 11 tumor-infiltrating immune cells between high-risk and low-risk subgroups. In addition, the StromalScore, ImmuneScore, and ESTIMATEScore in high-risk group were higher than those in low-risk group, indicating that the tumor microenvironment of the high-risk group was more complex. A Risk Score model based on eight metastasis-related genes could clearly distinguish the prognosis of GC patients. The poor prognosis of patients with high-Risk Score might be associated with the complex tumor microenvironments.

A novel anoikis-related gene signature predicts prognosis in patients with breast cancer and reveals immune infiltration

Breast cancer (BRCA) is a common malignancy worldwide that is associated with a high mortality rate. Despite recent improvements in diagnosis and treatment, there is an urgent need to investigate the processes underlying cancer progression and identify novel prognostic indicators. Anoikis, which plays a role in the development of human malignant tumors, has been gaining increasing interest from researchers. However, the potential role of anoikis-related genes (ANRGs) in the advancement of BRCA remains unknown. In this study, we aimed to assess the predictive value of ANRGs in BRCA, construct a prognostic model based on ANRGs, and explore the tumor microenvironment in different prognostic score groups. This study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect clinical information and RNA sequencing data from patients with BRCA. Information on ANRGs was gathered from GeneCards and Harmonizome portals. A risk score model based on ANRGs was created using least absolute shrinkage and selection operator Cox (LASSO) regression analysis. Additionally, the study explored the tumor microenvironment and enriched pathways in different risk groups. Finally, a novel ANRG-based nomogram is developed. A total of 142 differentially expressed genes associated with survival were identified, of which 5 genes were selected to create the ANRG signature. The risk score based on this signature proved to be an independent prognostic factor. Further analysis revealed that different risk subgroups exhibited variations in the tumor microenvironment and drug sensitivities. Subsequently, a nomogram was developed using risk scores and clinicopathological factors. The decision curve analysis results suggest that patients with BRCA might derive clinical treatment benefits from utilizing this prognostic model. Based on the results of this study, the ANRG signature and nomograph established can be used for clinical decision-making in patients with BRCA.

Development and validation of a novel anoikis-related gene signature in clear cell renal cell carcinoma

Background

Despite numerous treatments available, clear cell renal cell carcinoma (ccRCC) remains a deadly and invasive cancer. Anoikis-related genes (ARGs) are essential regulators of tumor metastasis and development. However, the potential roles of ARGs in ccRCC remain unclear.

Methods

Based on the TCGA-KIRC cohort and GeneCards database, we identified differentially expressed ARGs in ccRCC. Then a 4 ARGs risk model was created by Cox regression and LASSO. The Kaplan-Meier and receiver operating characteristic (ROC) curves were utilized to verify the predictive efficacy of the prognostic signature. Subsequently, the possible molecular mechanism of ARGs was investigated by functional enrichment analysis. To assess the immune infiltration, immune checkpoint genes, and immune function in various risk groups, single sample gene set enrichment (ssGSEA) algorithm was employed. Furthermore, the low-risk and high-risk groups were compared in terms of tumor mutation burden (TMB). Ultimately, we analyzed the protein expression of these four ARGs utilizing the western blot test.

Results

Four genes were utilized to create a risk signature that may predict prognosis, enabling the classification of KIRC patients into groups with low or high risk. The reliability of the signature was examined utilizing survival analysis and ROC analysis. According to the multivariate Cox regression result, the risk score was a reliable independent prognostic predictor for KIRC patients. The novel risk model could differentiate between KIRC patients with various clinical outcomes and represent KIRC's specific immune status. An analysis of the correlation of TMB and risk score indicated a positive correlation between them, with high TMB being potentially linked to worse outcomes.

Conclusion

Based on our findings, the prognostic signature of ARGs may be employed as an independent prognostic factor for ccRCC patients. It may introduce alternative perspectives on prognosis evaluation and serve as a prominent reference for personalized and precise therapy in KIRC.

Construction and validation of a novel prognostic model of neutrophil‑related genes signature of lung adenocarcinoma

Lung adenocarcinoma (LUAD) remains an incurable disease with a poor prognosis. This study aimed to explore neutrophil‑related genes (NRGs) and develop a prognostic signature for predicting the prognosis of LUAD. NRGs were obtained by intersecting modular genes identified by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data and the marker genes of neutrophils identified from single-cell RNA-sequencing(scRNA-seq) data. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were run to construct a prognostic signature, follow by delineation of risk groups, and external validation. Analyses of ESTIMAT, immune function, Tumor Immune Dysfunction and Exclusion (TIDE) scores, Immune cell Proportion Score (IPS), and immune checkpoint genes between high- and low-risk groups were performed, and then analyses of drug sensitivity to screen for sensitive anticancer drugs in high-risk groups. A total of 45 candidate NRGs were identified, of which PLTP, EREG, CD68, CD69, PLAUR, and CYP27A1 were considered to be significantly associated with prognosis in LUAD and were used to construct a prognostic signature. Correlation analysis showed significant differences in the immune landscape between high- and low-risk groups. In addition, our prognostic signature was important for predicting drug sensitivity in the high-risk group. Our study screened for NRGs in LUAD and constructed a novel and effective signature, revealing the immune landscape and providing more appropriate guidance protocols in LUAD treatment.

YY1 regulates the proliferation and invasion of triple-negative breast cancer via activating PLAUR

It is well-established that breast cancer is a highly prevalent malignancy among women, emphasizing the need to investigate mechanisms underlying its pathogenesis and metastasis. In this study, the Gene Expression Omnibus (GEO) database was utilized to conduct differential expression analysis in breast cancer and adjacent tissues. Upregulated genes were selected for prognostic analysis of breast cancer. The expression of urokinase plasminogen activator receptor (uPAR), also known as PLAUR, was assessed using RT-qPCR and western blot. Immunofluorescence staining was employed to determine PLAUR localization. Various cellular processes were analyzed, including proliferation, migration, invasion, apoptosis, and cell cycle. Bioinformatics analysis was used to predict transcription factors of PLAUR, which were subsequently validated in a double luciferase reporter gene experiment. Rescue experiments confirmed the impact of PLAUR on the proliferation, apoptosis, and migration of MDA-MB-231 cells. Furthermore, the effects of PLAUR were evaluated in an orthotopic tumor transplantation and lung metastasis nude mouse model. Our findings substantiated the critical involvement of PLAUR in the progression of triple-negative breast cancer (TNBC) in vitro and among TNBC patients with a poor prognosis. Additionally, we demonstrated Yin Yang-1 (YY1) as a notable transcriptional regulator of PLAUR, whose activation could transcriptionally enhance the proliferation and invasion capabilities of TNBC cells. We also identified the downstream mechanism of PLAUR associated with PLAU, focal adhesion kinase (FAK), and AKT. Overall, these findings offer a novel perspective on PLAUR as a potential therapeutic target for TNBC.

A novel stratification framework based on anoikis-related genes for predicting the prognosis in patients with osteosarcoma

Background

Anoikis resistance is a prerequisite for the successful development of osteosarcoma (OS) metastases, whether the expression of anoikis-related genes (ARGs) correlates with OS prognosis remains unclear. This study aimed to investigate the feasibility of using ARGs as prognostic tools for the risk stratification of OS.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided transcriptome information relevant to OS. The GeneCards database was used to identify ARGs. Differentially expressed ARGs (DEARGs) were identified by overlapping ARGs with common differentially expressed genes (DEGs) between OS and normal samples from the GSE16088, GSE19276, and GSE99671 datasets. Anoikis-related clusters of patients were obtained by consistent clustering, and gene set variation analysis (GSVA) of the different clusters was completed. Next, a risk model was created using Cox regression analyses. Risk scores and clinical features were assessed for independent prognostic values, and a nomogram model was constructed. Subsequently, a functional enrichment analysis of the high- and low-risk groups was performed. In addition, the immunological characteristics of OS samples were compared between the high- and low-risk groups, and their sensitivity to therapeutic agents was explored.

Results

Seven DEARGs between OS and normal samples were obtained by intersecting 501 ARGs with 68 common DEGs. BNIP3 and CXCL12 were significantly differentially expressed between both clusters (P<0.05) and were identified as prognosis-related genes. The risk model showed that the risk score and tumor metastasis were independent prognostic factors of patients with OS. A nomogram combining risk score and tumor metastasis effectively predicted the prognosis. In addition, patients in the high-risk group had low immune scores and high tumor purity. The levels of immune cell infiltration, expression of human leukocyte antigen (HLA) genes, immune response gene sets, and immune checkpoints were lower in the high-risk group than those in the low-risk group. The low-risk group was sensitive to the immune checkpoint PD-1 inhibitor, and the high-risk group exhibited lower inhibitory concentration values by 50% for 24 drugs, including AG.014699, AMG.706, and AZD6482.

Conclusion

The prognostic stratification framework of patients with OS based on ARGs, such as BNIP3 and CXCL12, may lead to more efficient clinical management.

Prognostic model for hepatocellular carcinoma based on anoikis-related genes: immune landscape analysis and prediction of drug sensitivity

Background

Hepatocellular carcinoma (HCC) represents a complex ailment characterized by an unfavorable prognosis in advanced stages. The involvement of immune cells in HCC progression is of significant importance. Moreover, metastasis poses a substantial impediment to enhanced prognostication for HCC patients, with anoikis playing an indispensable role in facilitating the distant metastasis of tumor cells. Nevertheless, limited investigations have been conducted regarding the utilization of anoikis factors for predicting HCC prognosis and assessing immune infiltration. This present study aims to identify hepatocellular carcinoma-associated anoikis-related genes (ANRGs), establish a robust prognostic model for HCC, and delineate distinct immune characteristics based on the anoikis signature. Cell migration and cytotoxicity experiments were performed to validate the accuracy of the ANRGs model.

Methods

Consensus clustering based on ANRGs was employed in this investigation to categorize HCC samples obtained from both TCGA and Gene Expression Omnibus (GEO) cohorts. To assess the differentially expressed genes, Cox regression analysis was conducted, and subsequently, prognostic gene signatures were constructed using LASSO-Cox methodology. External validation was performed at the International Cancer Genome Conference. The tumor microenvironment (TME) was characterized utilizing ESTIMATE and CIBERSORT algorithms, while machine learning techniques facilitated the identification of potential target drugs. The wound healing assay and CCK-8 assay were employed to evaluate the migratory capacity and drug sensitivity of HCC cell lines, respectively.

Results

Utilizing the TCGA-LIHC dataset, we devised a nomogram integrating a ten-gene signature with diverse clinicopathological features. Furthermore, the discriminative potential and clinical utility of the ten-gene signature and nomogram were substantiated through ROC analysis and DCA. Subsequently, we devised a prognostic framework leveraging gene expression data from distinct risk cohorts to predict the drug responsiveness of HCC subtypes.

Conclusion

In this study, we have established a promising HCC prognostic ANRGs model, which can serve as a valuable tool for clinicians in selecting targeted therapeutic drugs, thereby improving overall patient survival rates. Additionally, this model has also revealed a strong connection between anoikis and immune cells, providing a potential avenue for elucidating the mechanisms underlying immune cell infiltration regulated by anoikis.

Natural products targeting signaling pathways associated with regulated cell death in gastric cancer: Recent advances and perspectives

Gastric cancer (GC) is one of the most serious gastrointestinal malignancies with high morbidity and mortality. The complexity of GC process lies in the multi-phenotypic linkage regulation, in which regulatory cell death (RCD) is the core link, which largely dominates the fate of GC cells and becomes a key determinant of GC development and prognosis. In recent years, increasing evidence has been reported that natural products can prevent and inhibit the development of GC by regulating RCDs, showing great therapeutic potential. In order to further clarify its key regulatory characteristics, this review focused on specific expressions of RCDs, combined with a variety of signaling pathways and their crosstalk characteristics, sorted out the key targets and action rules of natural products targeting RCD. It is highlighted that a variety of core biological pathways and core targets are involved in the decision of GC cell fate, including the PI3K/Akt signaling pathway, MAPK-related signaling pathways, p53 signaling pathway, ER stress, Caspase-8, gasdermin D (GSDMD), and so on. Moreover, natural products target the crosstalk of different RCDs by modulating above signaling pathways. Taken together, these findings suggest that targeting various RCDs in GC with natural products is a promising strategy, providing a reference for further clarifying the molecular mechanism of natural products treating GC, which warrants further investigations in this area.

Identification of anoikis-related subtypes and development of risk stratification system in skin cutaneous melanoma

Background

Anoikis, a form of apoptosis induced by cell detachment, plays a key role in cancer metastasis. However, the potential roles of anoikis-related genes (ARGs) in assessing the prognosis of skin cutaneous melanoma (SKCM) and the tumor microenvironment (TME) remain unclear.

Methods

The data from TCGA corresponding to transcriptomic expression patterns for patients with SKCM were downloaded and utilized to screen distinct molecular subtypes by a non-negative matrix factorization algorithm. The prognostic signature was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression and was validated in SKCM patients from the GEO cohort. Moreover, the relationship of the ARG_score with prognosis, tumor-infiltrating immune cells, gene mutation, microsatellite instability (MSI), and immunotherapy efficacy.

Results

We screened 100 anoikis-related differentially expressed genes between SKCM tissues and normal skin tissues, which could divide all patients into three different subtypes with significantly distinct prognosis and immune cell infiltration. Then, an anoikis-related signature was developed based on subtype-related DEGs, which could classify all SKCM patients into low and high ARG_score groups with differing overall survival (OS) rates. ARG_score was confirmed to be a strong independent prognostic indicator for SKCM patients. By combining ARG_score with clinicopathological features, a nomogram was constructed, which could accurately predict the individual OS of patients with SKCM. Moreover, low ARG_score patients presented with higher levels of immune cell infiltration, TME score, higher tumor mutation burden, and better immunotherapy responses.

Conclusions

Our comprehensive analysis of ARGs in SKCM provides important insights into the immunological microenvironment within the tumor of SKCM patients and helps to forecast prognosis and the response to immunotherapy in SKCM patients, thereby making it easier to tailor more effective treatment strategies to individual patients.

Low expression of the intestinal metabolite butyric acid and the corresponding memory pattern regulate HDAC4 to promote apoptosis in rat hippocampal neurons

After intensive research on the gut-brain axis, intestinal dysbiosis is considered to be one of the important pathways of cognitive decline. Microbiota transplantation has long been thought to reverse the behavioral changes in the brain caused by colony dysregulation, but in our study, microbiota transplantation seemed to improve only behavioral brain function, and there was no reasonable explanation for the high level of hippocampal neuron apoptosis that remained. Butyric acid is one of the short-chain fatty acids of intestinal metabolites and is mainly used as an edible flavoring. It is commonly used in butter, cheese and fruit flavorings, and is a natural product of bacterial fermentation of dietary fiber and resistant starch in the colon, acting similarly to the small-molecule HDAC inhibitor TSA. The effect of butyric acid on HDAC levels in hippocampal neurons in the brain remains unclear. Therefore, this study used rats with low bacterial abundance, conditional knockout mice, microbiota transplantation, 16S rDNA amplicon sequencing, and behavioral assays to demonstrate the regulatory mechanism of short-chain fatty acids on the acetylation of hippocampal histones. The results showed that disturbance of short-chain fatty acid metabolism led to high HDAC4 expression in the hippocampus and regulated H4K8ac, H4K12ac, and H4K16ac to promote increased neuronal apoptosis. However, microbiota transplantation did not change the pattern of low butyric acid expression, resulting in maintained high HDAC4 expression in hippocampal neurons with continued neuronal apoptosis. Overall, our study shows that low levels of butyric acid in vivo can promote HDAC4 expression through the gut-brain axis pathway, leading to hippocampal neuronal apoptosis, and demonstrates that butyric acid has great potential value for neuroprotection in the brain. In this regard, we suggest that patients with chronic dysbiosis should pay attention to changes in the levels of SCFAs in their bodies, and if deficiencies occur, they should be promptly supplemented through diet and other means to avoid affecting brain health.

Construction of an original anoikis-related prognostic model closely related to immune infiltration in gastric cancer

Background: Anoikis is considered as a particular type of programmed cell death, the weakness or resistance of which contributes greatly to the development and progression of most malignant solid tumors. However, the latent impact of anoikis-related genes (ARGs) on gastric cancer (GC) is still ambiguous. Based on these, this study established an anoikis-related prognostic model of GC to identify the prognosis of patients and provide more effective treatment in clinical practice. Methods: First, we extracted four public datasets containing the gene expression and clinicopathological information of GC, which were worked as the training and validating sets, separately. Then, an anoikis-related survival-predicted model of GC was developed via Lasso and COX regression analyses and verified by using the Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) curve analyses. Next, we assigned GC patients to two groups characterized by the risk score calculated and analyzed somatic mutation, functional pathways, and immune infiltration between the different two groups. Finally, a unique nomogram was offered to clinicians to forecast the personal survival probability of GC patients. Results: Based on seven anoikis-related markers screened and identified, a carcinogenic model of risk score was produced. Patients placed in the high-score group suffered significantly worse overall survival (OS) in four cohorts. Additionally, the model revealed a high sensitivity and specificity to prognosticate the prognoses of GC patients [area under the ROC curve (AUC) at 5-year = 0.713; GSE84437, AUC at 5-year = 0.639; GSE15459, AUC at 5-year = 0.672; GSE62254, AUC at 5-year = 0.616]. Apart from the excellent predictive performance, the model was also identified as an independent prediction factor from other clinicopathological characteristics. Combining anoikis-related prognostic model with GC clinical features, we built a more comprehensive nomogram to foresee the likelihood of survival of GC patients in a given year, showing a well-accurate prediction performance. Conclusion: In summary, this study created a new anoikis-related signature for GC, which has potentially provided new critical insights into survival prediction and individualized therapy development.

Construction of a novel anoikis-related prognostic model and analysis of its correlation with infiltration of immune cells in neuroblastoma

Background

Anoikis resistance (AR) plays an important role in the process of metastasis, which is an important factor affecting the risk stage of neuroblastoma (NB). This study aims to construct an anoikis-related prognostic model and analyze the characteristics of hub genes, important pathways and tumor microenvironment of anoikis-related subtypes of NB, so as to provide help for the clinical diagnosis, treatment and research of NB.

Methods

We combined transcriptome data of GSE49710 and E-MTAB-8248, screened anoikis-related genes (Args) closely related to the prognosis of NB by univariate cox regression analysis, and divided the samples into anoikis-related subtypes by consistent cluster analysis. WGCNA was used to screen hub genes, GSVA and GSEA were used to analyze the differentially enriched pathways between anoikis-related subtypes. We analyzed the infiltration levels of immune cells between different groups by SsGSEA and CIBERSORT. Lasso and multivariate regression analyses were used to construct a prognostic model. Finally, we analyzed drug sensitivity through the GDSC database.

Results

721 cases and 283 Args were included in this study. All samples were grouped into two subtypes with different prognoses. The analyses of WGCNA, GSVA and GSEA suggested the existence of differentially expressed hub genes and important pathways in the two subtypes. We further constructed an anoikis-related prognostic model, in which 15 Args participated. This model had more advantages in evaluating the prognoses of NB than other commonly used clinical indicators. The infiltration levels of 9 immune cells were significantly different between different risk groups, and 13 Args involved in the model construction were correlated with the infiltration levels of immune cells. There was a relationship between the infiltration levels of 6 immune cells and riskscores. Finally, we screened 15 drugs with more obvious effects on NB in high-risk group.

Conclusion

There are two anoikis-related subtypes with different prognoses in the population of NB. The anoikis-related prognostic model constructed in this study can accurately predict the prognoses of children with NB, and has a good guiding significance for clinical diagnosis, treatment and research of NB.

Construction of anoikis-related lncRNAs risk model: Predicts prognosis and immunotherapy response for gastric adenocarcinoma patients

Background: Anoikis acts as a programmed cell death that is activated during carcinogenesis to remove undetected cells isolated from ECM. Further anoikis based risk stratification is expected to provide a deeper understanding of stomach adenocarcinoma (STAD) carcinogenesis. Methods: The information of STAD patients were acquired from TCGA dataset. Anoikis-related genes were obtained from the Molecular Signatures Database and Pearson correlation analysis was performed to identify the anoikis-related lncRNAs (ARLs). We performed machine learning algorithms, including Univariate Cox regression and Least Absolute Shrinkage and Selection Operator (Lasso) analyses on the ARLs to build the OS-score and OS-signature. Clinical subgroup analysis, tumor mutation burden (TMB) detection, drug susceptibility analysis, immune infiltration and pathway enrichment analysis were further performed to comprehensive explore the clinical significance. Results: We established a STAD prognostic model based on five ARLs and its prognostic value was verified. Survival analysis showed that the overall survival of high-risk score patients was significantly shorter than that of low-risk score patients. The column diagrams show satisfactory discrimination and calibration. The calibration curve verifies the good agreement between the prediction of the line graph and the actual observation. TIDE analysis and drug sensitivity analysis showed significant differences between different risk groups. Conclusion: The novel prognostic model based on anoikis-related lncRNAs we identified could be used for prognosis prediction and precise therapy in gastric adenocarcinoma.

Identification of anoikis-related molecular patterns to define tumor microenvironment and predict immunotherapy response and prognosis in soft-tissue sarcoma

Background: Soft-tissue sarcoma (STS) is a massive threat to human health due to its high morbidity and malignancy. STS also represents more than 100 histologic and molecular subtypes, with different prognosis. There is growing evidence that anoikis play a key role in the proliferation and invasion of tumors. However, the effects of anoikis in the immune landscape and the prognosis of STS remain unclear. Methods: We analyzed the genomic and transcriptomic profiling of 34 anoikis-related genes (ARGs) in patient cohort of pan-cancer and STS from The Cancer Genome Atlas (TCGA) database. Single-cell transcriptome was used to disclose the expression patterns of ARGs in specific cell types. Gene expression was further validated by real-time PCR and our own sequencing data. We established the Anoikis cluster and Anoikis subtypes by using unsupervised consensus clustering analysis. An anoikis scoring system was further built based on the differentially expressed genes (DEGs) between Anoikis clusters. The clinical and biological characteristics of different groups were evaluated. Results: The expressions of most ARGs were significantly different between STS and normal tissues. We found some common ARGs profiles across the pan-cancers. Network of 34 ARGs demonstrated the regulatory pattern and the association with immune cell infiltration. Patients from different Anoikis clusters or Anoikis subtypes displayed distinct clinical and biological characteristics. The scoring system was efficient in prediction of prognosis and immune cell infiltration. In addition, the scoring system could be used to predict immunotherapy response. Conclusion: Overall, our study thoroughly depicted the anoikis-related molecular and biological profiling and interactions of ARGs in STS. The Anoikis score model could guide the individualized management.

Development of the Peritoneal Metastasis: A Review of Back-Grounds, Mechanisms, Treatments and Prospects

Peritoneal metastasis is a malignant disease which originated from several gastrointestinal and gynecological carcinomas and has been leading to a suffering condition in patients for decades. Currently, as people have gradually become more aware of the severity of peritoneal carcinomatosis, new molecular mechanisms for targeting and new treatments have been proposed. However, due to the uncertainty of influencing factors involved and a lack of a standardized procedure for this treatment, as well as a need for more clinical data for specific evaluation, more research is needed, both for preventing and treating. We aim to summarize backgrounds, mechanisms and treatments in this area and conclude limitations or new aspects for treatments.