Identification of autophagy related genes in predicting the prognosis and aiding 5- fluorouracil therapy of colorectal cancer

Gao, Tianyi; Yuan, Dan; He, Bangshun; Gao, Yingdong; Liu, Caidong; Sun, Huilin; Nie, Junjie; Wang, Shukui; Nie, Zhenlin

doi:10.1016/j.heliyon.2022.e09033

Cited by 4 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent literature highlights its dual role in cancer, acting either as an oncogene or a tumor suppressor depending on the context. In CRC, it is reported that HSPB8 expression is upregulated and contributes to chemoresistance, highlighting its potential impact on CRC prognosis and treatment response 41 . PTGIS plays a role in the synthesis of prostaglandin I2, a molecule involved in inflammation and tumorigenesis 42 .…”

Section: Discussionmentioning

confidence: 99%

“…In CRC, it is reported that HSPB8 expression is upregulated and contributes to chemoresistance, highlighting its potential impact on CRC prognosis and treatment response. 41 PTGIS plays a role in the synthesis of prostaglandin I2, a molecule involved in inflammation and tumorigenesis. 42 PTGIS expression is significantly reduced in CRC tissues and its low expression is associated with worse survival, suggesting its potential as a prognostic biomarker.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer

Xu,

Qiang,

Huang

et al. 2024

Environmental Toxicology

View full text Add to dashboard Cite

BackgroundColorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD‐related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction.MethodWe retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome‐based CRC prognostic models.ResultOur integrated model successfully identified differentially expressed PCD‐related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high‐risk and low‐risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis.ConclusionThe current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer

Xu,

Qiang,

Huang

et al. 2024

Environmental Toxicology

View full text Add to dashboard Cite

show abstract

“…According to our early study, 232 ARGs were obtained from the HADb (Human Autophagy Database, http://www.autophagy.lu/) [13]. Then we downloaded the transcriptome pro ling data and clinic-pathological annotation of COAD from TCGA Data Portal (https://tcga-data.nci.nih.gov/tcga/) which was composed of a total of 447 cases of colon transcripts, including 39 normal and 398 tumor samples.…”

Section: Identi Cation Of Differently Expressed Args In Coadmentioning

confidence: 99%

“…Then we downloaded the transcriptome pro ling data and clinic-pathological annotation of COAD from TCGA Data Portal (https://tcga-data.nci.nih.gov/tcga/) which was composed of a total of 447 cases of colon transcripts, including 39 normal and 398 tumor samples. At last, ARGs between COAD and normal samples were analyzed basing on a t-test of linear models for microarray analysis package in R [13]. In addition, the fold-change (FC) of the gene expression was also detected in the study.…”

mentioning

confidence: 99%

Identification of autophagy-related genes for regulating cancer stem cell characteristics in colon adenocarcinoma by the analysis of transcriptome data stemness indices

Guo

Hou

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The emergence of cancer stem cells (CSCs) is the barrier to effective clinical outcomes for Colon adenocarcinoma (COAD) patients. Autophagy was found to play an important role on CSCs stemness regulation. However, the specific role of autophagy-related genes in COAD stemness remains unclear. In this study, by processing on two independent stemness indices, mRNAsi and mDNAsi, TP53INP2 among 29 differentially expressed autophagy-related genes(ARGs) in COAD was identified to be the hub ARGs in COAD stemness elimination. COAD patients with high stemness indices scores usually showed a down-regulated TP53INP2 expression which was correlated to a higher chemotherapy resistance and poorer RFS than the others. Two TFs, KLF9 and SETBP1 were involved in CSCs TP53INP2 expression promotion. Additionally, the decreased expression level of TP53INP2 was found to be significantly correlated to the COAD immune subtypes of C4 which contributed to the immunoresistance with low density infiltration of TH2, Treg cells, macrophages, monocyte and dendritic cells. In conclusion, TP53INP2 was found to be a valid indicator for poor prognosis of COAD patients with high stemness. All these results would provide a new strategy in seeking potential COAD therapeutic targets.

show abstract

Identification of a seven autophagy-related gene pairs signature for the diagnosis of colorectal cancer using the RankComp algorithm

Qin

et al. 2023

J. Bioinform. Comput. Biol.

View full text Add to dashboard Cite

Based on the colorectal cancer microarray sets gene expression data series (GSE) GSE10972 and GSE74602 in colon cancer and 222 autophagy-related genes, the differential signature in colorectal cancer and paracancerous tissues was analyzed by RankComp algorithm, and a signature consisting of seven autophagy-related reversal gene pairs with stable relative expression orderings (REOs) was obtained. Scoring based on these gene pairs could significantly distinguish colorectal cancer samples from adjacent noncancerous samples, with an average accuracy of 97.5% in two training sets and 90.25% in four independent validation GSE21510, GSE37182, GSE33126, and GSE18105. Scoring based on these gene pairs also accurately identifies 99.85% of colorectal cancer samples in seven other independent datasets containing a total of 1406 colorectal cancer samples.

show abstract

Identification of autophagy related genes in predicting the prognosis and aiding 5- fluorouracil therapy of colorectal cancer

Cited by 4 publications

References 37 publications

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer

Identification of autophagy-related genes for regulating cancer stem cell characteristics in colon adenocarcinoma by the analysis of transcriptome data stemness indices

Identification of a seven autophagy-related gene pairs signature for the diagnosis of colorectal cancer using the RankComp algorithm

Contact Info

Product

Resources

About