Construction of an anoikis‒related prognostic signature to predict immunotherapeutic response and prognosis in hepatocellular carcinoma.

Anoikis resistance is an important inducer of tumor metastasis. The role of anoikis-related genes (ARGs) in hepatocellular carcinoma (HCC) remains unclear. A list of ARGs was obtained and regression analysis was employed to assemble an anoikis-related prognostic signature and risk score formula from mRNA data and clinical prognostic data downloaded from The Cancer Genome Atlas database. Quantitative real-time PCR (qRT-PCR) was performed on clinical samples to validate the selected ARGs expressions. Kaplan‒Meier curves, ROC curves, and Cox regression analyses were used to demonstrated the prognostic value of this signature. Biological functional enrichment analysis and immune infiltration analysis were utilized to analyze the differences in potential biological functions, immune cell infiltration, immune functions, and immunotherapeutic responses. A prognostic signature based on 6 ARGs and corresponding prognostic nomogram were established. Our qRT-PCR results showed a higher expression of 6 ARGs in HCC tissues (p value < 0.05). Kaplan‒Meier curves, ROC curves, and Cox regression analyses demonstrated good prognostic value of the signature in HCC (p value < 0.05). Significant differences between the enriched biological functions and immune landscapes of patients in different risk groups were discovered (p value < 0.05). In addition, patients with higher risk scores possibly had poor therapeutic response to transhepatic arterial chemotherapy and embolization or sorafenib, but their responses to immunotherapy might be more effective. A successful anoikis-related prognostic signature and corresponding clinical nomogram were established, which might facilitate better predictions of prognosis and therapeutic responses for HCC patients.

Xiong C ，Pan G ，Wang H ，Meng G ，Yan L ，Li R ，Yan Y ，Yang Y ，Zhang X ，Yang C ，Dong Z ，Li T ... -  《-》

Anoikis-related lncRNA signature predicts prognosis and is associated with immune infiltration in hepatocellular carcinoma.

Anoikis is a programmed cell death process triggered when cells are dislodged from the extracellular matrix. Numerous long noncoding RNAs (lncRNAs) have been identified as significant factors associated with anoikis resistance in various tumor types, including glioma, breast cancer, and bladder cancer. However, the relationship between lncRNAs and the prognosis of hepatocellular carcinoma (HCC) has received limited research attention. Further research is needed to investigate this potential link and understand the role of lncRNAs in the progression of HCC. We developed a prognostic signature based on the differential expression of lncRNAs implicated in anoikis in HCC. A co-expression network of anoikis-related mRNAs and lncRNAs was established using data obtained from The Cancer Genome Atlas (TCGA) for HCC. Cox regression analyses were conducted to formulate an anoikis-related lncRNA signature (ARlncSig) in a training cohort, which was subsequently validated in both a testing cohort and a combined dataset comprising the two cohorts. Receiver operating characteristic curves, nomograms, and decision curve analyses based on the ARlncSig score and clinical characteristics demonstrated robust predictive ability. Moreover, gene set enrichment analysis revealed significant enrichment of several immune processes in the high-risk group compared to the low-risk group. Furthermore, significant differences were observed in immune cell subpopulations, expression of immune checkpoint genes, and response to chemotherapy and immunotherapy between the high- and low-risk groups. Lastly, we validated the expression levels of the five lncRNAs included in the signature using quantitative real-time PCR. In conclusion, our ARlncSig model holds substantial predictive value regarding the prognosis of HCC patients and has the potential to provide clinical guidance for individualized immunotherapy. In this study, we obtained 36 genes associated with anoikis from the Gene Ontology and Gene Set Enrichment Analysis databases. We also identified 22 differentially expressed lncRNAs that were correlated with these genes using data from TCGA. Using Cox regression analyses, we developed an ARlncSig in a training cohort, which was then validated in both a testing cohort and a combined cohort comprising data from both cohorts. Additionally, we collected eight pairs of liver cancer tissues and adjacent tissues from the Affiliated Tumor Hospital of Nantong University for further analysis. The aim of this study was to investigate the potential of ARlncSig as a biomarker for liver cancer prognosis. The study developed a risk stratification system called ARlncSig, which uses five lncRNAs to categorize liver cancer patients into low- and high-risk groups. Patients in the high-risk group exhibited significantly lower overall survival rates compared to those in the low-risk group. The model's predictive performance was supported by various analyses including the receiver operating characteristic curve, nomogram calibration, clinical correlation analysis, and clinical decision curve. Additionally, differential analysis of immune function, immune checkpoint, response to chemotherapy, and immune cell subpopulations revealed significant differences between the high- and low-risk groups. Finally, quantitative real-time PCR validated the expression levels of the five lncRNAs. In conclusion, the ARlncSig model demonstrates critical predictive value in the prognosis of HCC patients and may provide clinical guidance for personalized immunotherapy.

Zhu J ，Zhao W ，Yang J ，Liu C ，Wang Y ，Zhao H ... -  《-》

Development of a prognostic model based on anoikis-related genes for predicting clinical prognosis and immunotherapy of hepatocellular carcinoma.

Pang M ，Sun X ，He T ，Liang H ，Yang H ，Chen J ... -  《Aging-US》

Development and validation of multi-omic prognostic signature of anoikis-related genes in liver hepatocellular carcinoma.

Liver hepatocellular carcinoma (LIHC) is characterized by high morbidity, rapid progression and early metastasis. Although many efforts have been made to improve the prognosis of LIHC, the situation is still dismal. Inability to initiate anoikis process is closely associated with cancer proliferation and metastasis, affecting patients' prognosis. In this study, a corresponding gene signature was constructed to comprehensively assess the prognostic value of anoikis-related genes (ARGs) in LIHC. Using TCGA-LIHC dataset, the mRNA levels of the differentially expressed ARGs in LIHC and normal tissues were compared by Student t test. And prognostic ARGs were identified through Cox regression analysis. Prognostic signature was established and then externally verified by ICGC-LIRI-JP dataset and GES14520 dataset via LASSO Cox regression model. Potential functions and mechanisms of ARGs in LIHC were evaluated by functional enrichment analyses. And the immune infiltration status in prognostic signature was analyzed by ESTIMATE algorithm and ssGSEA algorithm. Furthermore, ARGs expression in LIHC tissues was validated via qRT-PCR and IHC staining from the HPA website. A total of 97 differentially expressed ARGs were detected in LIHC tissues. Functional enrichment analysis revealed these genes were mainly involved in MAP kinase activity, apoptotic signaling pathway, anoikis and PI3K-Akt signaling pathway. Afterward, the prognostic signature consisting of BSG, ETV4, EZH2, NQO1, PLK1, PBK, and SPP1 had a moderate to high predictive accuracy and served as an independent prognostic indicator for LIHC. The prognostic signature was also applicable to patients with distinct clinical parameters in subgroup survival analysis. And it could reflect the specific immune microenvironment in LIHC, which indicated high-risk group tended to profit from ICI treatment. Moreover, qRT-PCR and IHC staining showed increasing expression of BSG, ETV4, EZH2, NQO1, PLK1, PBK and SPP1in LIHC tissues, which were consistent to the results from TCGA database. The current study developed a novel prognostic signature comprising of 7 ARGs, which could stratify the risk and effectively predict the prognosis of LIHC patients. Furthermore, it also offered a potential indicator for immunotherapy of LIHC.

Ding D ，Wang D ，Qin Y 《-》

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.

Yu Z ，Shi FE ，Mao Y ，Song A ，He L ，Gao M ，Wei H ，Xiao F ，Wei H ... -  《-》