Development of an Aging-Related Gene Signature for Predicting Prognosis, Immunotherapy, and Chemotherapy Benefits in Rectal Cancer

Wang, Yangyang; Liu, Yan; Zhu, Chunchao; Zhang, Xinyu; Li, Guodong

doi:10.3389/fmolb.2021.775700

Cited by 5 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the present analysis, we identified three aging-associated clusters, and the aging gene-based C1 cluster exhibited the shortest overall survival time, highest immune score, and maximum immune infiltration compared with other subtypes. Li and co-workers figured out that an aging-related model had a good risk prediction efficiency in rectal cancer prognosis [ 22 ]. However, the links between aging-associated changes and cancer initiation and progression have not been fully explored [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Classification of Signature-Based Phenotypes of Aging-Related Genes to Identify Prognostic and Immune Characteristics in HCC

Zhao

et al. 2023

Analytical Cellular Pathology

View full text Add to dashboard Cite

Hepatocellular carcinoma (HCC), which has become one of the most significant malignancies causing cancer-related mortality, presents genetic and phenotypic heterogeneity that makes predicting prognosis challenging. Aging-related genes have been increasingly reported as significant risk factors for many kinds of malignancies, including HCC. In this study, we comprehensively dissected the features of transcriptional aging-relevant genes in HCC from multiple perspectives. We applied public databases and self-consistent clustering analysis to classify patients into C1, C2, and C3 clusters. The C1 cluster had the shortest overall survival time and advanced pathological features. Least absolute shrinkage and selection operator (LASSO) regression analysis was adopted to build the prognostic prediction model based on six aging-related genes (HMMR, S100A9, SPP1, CYP2C9, CFHR3, and RAMP3). These genes were differently expressed in HepG2 cell lines compared with LO2 cell lines measured by the mRNA expression level. The high-risk score group had significantly more immune checkpoint genes, higher tumor immune dysfunction and exclusion score, and stronger chemotherapy response. The results indicated that the age-related genes have a close correlation with HCC prognosis and immune characteristics. Overall, the model based on six aging-associated genes demonstrated great prognostic prediction ability.

show abstract

Section: Discussionmentioning

confidence: 99%

Classification of Signature-Based Phenotypes of Aging-Related Genes to Identify Prognostic and Immune Characteristics in HCC

Zhao

et al. 2023

Analytical Cellular Pathology

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

“…18 Similarly, the high-risk cohort in rectal cancer shows a worse prognosis than the low-risk group. 19 In hepatocellular carcinoma, the risk score model based on the aging-related genes suggests that high-risk scores show a poor prognosis compared with the low-risk score subgroup. 20 Specifically, these risk score model-associated genes play various roles in cancer initiation, progression, and microenvironment formation.…”

Section: Discussionmentioning

confidence: 99%

Construction and evaluation of an aging-associated genes-based model for pancreatic adenocarcinoma prognosis and therapies

Zhao

Guan

Xing

2023

Int J Immunopathol Pharmacol

View full text Add to dashboard Cite

Objectives: Pancreatic adenocarcinoma (PAAD) is a highly malignant tumor. Despite extensive research, the precise role of aging-related genes in the initiation, microenvironment regulation, and progression of PAAD remains unclear. Methods: Patients with PAAD were selected from the International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA) cohorts and the cell senescence-associated genes were obtained from CellAge. ConsensusClusterPlus was utilized for cluster identification. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct a prognosis prediction model. Results: We identified three clusters (C1, C2, and C3) based on aging-associated gene profiles. The C1 cluster had a shorter overall survival time, advanced clinical grades, lower immune ESTIMATE score, and tumor immune dysfunction and exclusion (TIDE) score than the C3 subgroup. Moreover, signaling pathways for cell cycle activation were enriched in the C1 cluster. We also identified eight hub genes and constructed a risk model. The high cellular senescence-related signature (CSRS) score subtype exhibited poor prognosis, advanced clinical grades, M2 macrophage infiltration, higher immune checkpoint gene expression, and lower immunotherapeutic benefits. Conclusion: Our risk score model shows high prediction accuracy and survival prediction ability in individual clinical prognosis and pre-immunotherapy evaluation.

show abstract

“…The construction of an accurate and efficient model for cancer monitoring has become a research hotspot due to advances in RNA-sequencing and bioinformatics tools. Research has shown a considerable correlation of several ARG signatures with the prognosis of cancers such as Zhang et al [ 41 ], Wang et al [ 42 ], and others who developed gene signatures. In both cases, gene signatures that were aging-related were designed to evaluate the potential prognosis prediction efficiency of a biomarker for malignancy and study the effect of chemo- and immunotherapies.…”

Section: Discussionmentioning

confidence: 99%

Identification and Development of an Age-Related Classification and Signature to Predict Prognosis and Immune Landscape in Osteosarcoma

Hong

et al. 2022

Journal of Oncology

View full text Add to dashboard Cite

Background. In childhood and adolescence, the prevailing bone tumor is osteosarcoma associated with frequent recurrence and lung metastasis. This research focused on predicting the survival and immune landscape of osteosarcoma by developing a prognostic signature and establishing aging-related genes (ARGs) subtypes. Methods. The training group comprised of the transcriptomic and associated clinical data of 84 patients with osteosarcoma accessed at the TARGET database and the validation group consisted of 53 patients from GSE21257. The aging-related subtypes were identified using unsupervised consensus clustering analysis. The ARG signature was developed utilizing multivariate Cox analysis and LASSO regression. The prognostic value was assessed using the univariate and multivariate Cox analyses, Kaplan-Meier plotter, time-dependent ROC curve, and nomogram. The functional enrichment analyses were performed by GSEA, GO, and KEGG analysis, while the ssGSEA, ESTIMATE, and CIBERSORT analyses were conducted to reveal the immune landscape in osteosarcoma. Results. The two clusters of osteosarcoma patients formed based on 543 ARGs, depicted a considerable difference in the tumor microenvironment, and the overall survival and immune cell infiltration rate varied as well. Among these, the selected 23 ARGs were utilized for the construction of an efficient predictive prognostic signature for the overall survival prediction. The testing in the validation group of osteosarcoma patients confirmed the status of the high-risk score as an independent indicator for poor prognosis, which was already identified as such using the univariate and multivariate Cox analyses. Furthermore, the ARG signature could distinguish different immune-related functions, infiltration status of immune cells, and tumor microenvironment, as well as predict the immunotherapy response of osteosarcoma patients. Conclusion. The aging-related subtypes were identified and a prognostic signature was developed in this research, which determined different prognoses and allowed for treatment of osteosarcoma patients to be tailored. Additionally, the immunotherapeutic response of individuals with osteosarcoma could also be predicted by the ARG signature.

show abstract

Development of an Aging-Related Gene Signature for Predicting Prognosis, Immunotherapy, and Chemotherapy Benefits in Rectal Cancer

Cited by 5 publications

References 37 publications

Classification of Signature-Based Phenotypes of Aging-Related Genes to Identify Prognostic and Immune Characteristics in HCC

Classification of Signature-Based Phenotypes of Aging-Related Genes to Identify Prognostic and Immune Characteristics in HCC

Construction and evaluation of an aging-associated genes-based model for pancreatic adenocarcinoma prognosis and therapies

Identification and Development of an Age-Related Classification and Signature to Predict Prognosis and Immune Landscape in Osteosarcoma

Contact Info

Product

Resources

About