A novel anoikis-related gene signature predicts prognosis in patients with head and neck squamous cell carcinoma and reveals immune infiltration

Chi, Hao; Jiang, Puyu; Xu, Ke; Zhao, Yue; Song, Bingyu; Peng, Gaoge; He, Bingsheng; Liu, Xin; Xia, Zhijia; Tian, Gang

doi:10.3389/fgene.2022.984273

Cited by 87 publications

(88 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 10 In addition, ARGs have been proven to be associated with prognosis in several tumors. 11 , 12 , 13 However, the comprehensive analyses of the implications of anoikis in LUAD are still scarce. Therefore, identification of the predominant genes related to anoikis with prognostic significance in LUAD is vital.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of a novel anoikis‐related gene signature to predict prognosis and tumor microenvironment in lung adenocarcinoma

Diao

Guo

2022

Thoracic Cancer

View full text Add to dashboard Cite

Background Lung adenocarcinoma (LUAD) is the most prevalent histotype of non‐small cell lung cancer. Anoikis, an alternative form of programmed cell death, plays a pivotal role in cancer invasion and metastasis, preventing the detached cancer cells from readhering to other substrates for abnormal proliferation. The aim of this study was to conduct a comprehensive analyses of the prognostic implications of anoikis‐related genes (ARGs) in LUAD. Methods ARGs were selected from The Cancer Genome Atlas (TCGA) database and Genecards dataset using differential expression analysis. The signature incorporating ARGs was identified using univariate Cox regression analysis and LASSO regression analysis. Furthermore, a nomogram containing the signature and clinical information was developed through univariate and multivariate Cox regression analysis. Kaplan–Meier survival analysis and receiver operating characteristic (ROC) curves were applied to evaluate the predictive validity of these risk models. Finally, functional analysis of the selected ARGs in signature and analysis of immune landscape were also conducted. Results A 16‐gene signature was integrated to stratify LUAD patients into different survival risk groups. The prognostic risk score generated from the signature and TNM stage were identified as independent prognostic factors and utilized to develop a nomogram. Both the signature and the nomogram showed satisfactory prediction performance in predicting overall survival (OS) of LUAD patients. The ARGs were enriched in several biological functions and signaling pathways. Finally, differences of immune landscape were investigated among the high‐ and low‐risk groups stratified by the signature. Conclusions This study revealed potential relationships between ARGs and prognosis of LUAD. The prognostic predictors identified in present study could be utilized as potential biomarkers for clinical applications.

show abstract

Section: Introductionmentioning

confidence: 99%

“…In another study by D'Amato et al, TDO2 was found as an attractive factor to facilitate anoikis resistance in triple‐negative breast cancer 10 . In addition, ARGs have been proven to be associated with prognosis in several tumors 11–13 . However, the comprehensive analyses of the implications of anoikis in LUAD are still scarce.…”

Section: Introductionmentioning

confidence: 99%

Identification of a novel anoikis‐related gene signature to predict prognosis and tumor microenvironment in lung adenocarcinoma

Diao

Guo

2022

Thoracic Cancer

View full text Add to dashboard Cite

show abstract

“…ICBhe diameter of the basal tumor, ciliary involvement and scleral expansion, non-random chromosomal aberrations and genetic mutations (e.g., BAP1 and SF3B1 mutations) are closely related to the prognosis of UVM (7)(8)(9) and are the main basis and foundation for prognostic grading, immunotherapy, radiotherapy and other treatment options. However, patients with the same clinical stage may have different clinicopathological features, suggesting that the prognosis of tumor patients based on traditional clinicopathological staging is not completely accurate (10,11). Therefore, to improve the quality of life of UVM patients, new prognostic biomarkers and molecular targets are needed to predict the prognosis of UVM patients and guide individualized treatment.…”

Section: Introductionmentioning

confidence: 99%

Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

Chi

Peng²,

Yang³

et al. 2022

Front. Endocrinol.

Self Cite

View full text Add to dashboard Cite

BackgroundUveal melanoma (UVM) is the most common primary intraocular malignancy in adults and is highly metastatic, resulting in a poor patient prognosis. Sphingolipid metabolism plays an important role in tumor development, diagnosis, and prognosis. This study aimed to establish a reliable signature based on sphingolipid metabolism genes (SMGs), thus providing a new perspective for assessing immunotherapy response and prognosis in patients with UVM.MethodsIn this study, SMGs were used to classify UVM from the TCGA-UVM and GEO cohorts. Genes significantly associated with prognosis in UVM patients were screened using univariate cox regression analysis. The most significantly characterized genes were obtained by machine learning, and 4-SMGs prognosis signature was constructed by stepwise multifactorial cox. External validation was performed in the GSE84976 cohort. The level of immune infiltration of 4-SMGs in high- and low-risk patients was analyzed by platforms such as CIBERSORT. The prediction of 4-SMGs on immunotherapy and immune checkpoint blockade (ICB) response in UVM patients was assessed by ImmuCellAI and TIP portals.Results4-SMGs were considered to be strongly associated with the prognosis of UVM and were good predictors of UVM prognosis. Multivariate analysis found that the model was an independent predictor of UVM, with patients in the low-risk group having higher overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores had good prognostic power. The high-risk group showed better results when receiving immunotherapy.Conclusions4-SMGs signature and nomogram showed excellent predictive performance and provided a new perspective for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology studies.

show abstract

“…org/gsea/msigdb/index.jsp ). Then, the activity of each gene set in each sample was assessed using the GSEABase package (24). The link between model genes and 51 immune genes (25) was then further examined, and the results were shown using a circular heat map.…”

Section: Enrichment Analysismentioning

confidence: 99%

By integrating single-cell RNA-seq and bulk RNA-seq in sphingolipid metabolism, CACYBP was identified as a potential therapeutic target in lung adenocarcinoma

et al. 2023

View full text Add to dashboard Cite

BackgroundLung adenocarcinoma (LUAD) is a heterogeneous disease with a dismal prognosis for advanced tumors. Immune-associated cells in the microenvironment substantially impact LUAD formation and progression, which has gained increased attention in recent decades. Sphingolipids have a profound impact on tumor formation and immune infiltration. However, few researchers have focused on the utilization of sphingolipid variables in the prediction of LUAD prognosis. The goal of this work was to identify the major sphingolipid-related genes (SRGs) in LUAD and develop a valid prognostic model based on SRGs.MethodsThe most significant genes for sphingolipid metabolism (SM) were identified using the AUCell and WGCNA algorithms in conjunction with single-cell and bulk RNA-seq. LASSO and COX regression analysis was used to develop risk models, and patients were divided into high-and low-risk categories. External nine provided cohorts evaluated the correctness of the models. Differences in immune infiltration, mutation landscape, pathway enrichment, immune checkpoint expression, and immunotherapy were also further investigated in distinct subgroups. Finally, cell function assay was used to verify the role of CACYBP in LUAD cells.ResultsA total of 334 genes were selected as being most linked with SM activity for further investigation, and a risk model consisting of 11 genes was established using lasso and cox regression. According to the median risk value, patients were split into high- and low-risk groups, and the high-risk group had a worse prognosis. The low-risk group had more immune cell infiltration and higher expression of immune checkpoints, which illustrated that the low-risk group was more likely to benefit from immunotherapy. It was verified that CACYBP could increase the ability of LUAD cells to proliferate, invade, and migrate.ConclusionThe eleven-gene signature identified in this research may help physicians create individualized care plans for LUAD patients. CACYBP may be a new therapeutic target for patients with advanced LUAD.

show abstract

A novel anoikis-related gene signature predicts prognosis in patients with head and neck squamous cell carcinoma and reveals immune infiltration

Cited by 87 publications

References 45 publications

Identification of a novel anoikis‐related gene signature to predict prognosis and tumor microenvironment in lung adenocarcinoma

Identification of a novel anoikis‐related gene signature to predict prognosis and tumor microenvironment in lung adenocarcinoma

Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

By integrating single-cell RNA-seq and bulk RNA-seq in sphingolipid metabolism, CACYBP was identified as a potential therapeutic target in lung adenocarcinoma

Contact Info

Product

Resources

About