ArticleFrontiers in oncology2021
The Effects of Autophagy-Related Genes and lncRNAs in Therapy and Prognosis of Colorectal Cancer.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- The role of long non-coding RNAs in NK cell biology and diseases.Genes & diseases · 2026Review
- Experimental prognostic model integrating N6-methyladenosine-related programmed cell death genes in colorectal cancer.iScience · 2024Article
- Development and validation of cuproptosis-related lncRNA signatures for prognosis prediction in colorectal cancer.BMC medical genomics · 2023Article
- MALAT1-related signaling pathways in colorectal cancer.Cancer cell international · 2022Review
- Identification of Autophagy-Related LncRNA to Predict the Prognosis of Colorectal Cancer.Frontiers in genetics · 2022Article
- Identification and validation of an inflammation-related lncRNAs signature for improving outcomes of patients in colorectal cancer.Frontiers in genetics · 2022Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cellular autophagy plays an important role in the occurrence and development of colorectal cancer (CRC). Whether autophagy-related genes and lncRNAs can be used as ideal markers in CRC is still controversial. The purpose of this study is to identify novel treatment and prognosis markers of CRC. We downloaded transcription and clinical data of CRC from the GEO (GSE40967, GSE12954, GSE17536) and TCGA database, screened for differentially autophagy-related genes (DEAGs) and lncRNAs, constructed prognostic model, and analyzed its relationship with immune infiltration. TCGA and GEO datasets (GSE12954 and GSE17536) were used to validate the effect of the model. Oncomine database and Human Protein Atlas verified the expression of DEAGs. We obtained a total of 151 DEAGs in three verification sets collaboratively. Then we constructed a risk prognostic model through Lasso regression to obtain 15 prognostic DEAGs from the training set and verified the risk prognostic model in three verification sets. The low-risk group survived longer than the high-risk group. Age, gender, pathological stage, and TNM stage were related to the prognostic risk of CRC. On the other hand, BRAF status, RFS event, and tumor location are considered as most significant risk factors of CRC in the training set. Furthermore, we found that the immune score of the low-risk group was higher. The content of CD8 + T cells, active NK cells, macrophages M0, macrophages M1, and active dendritic cells was noted more in the high-risk group. The content of plasma cells, resting memory CD4 + T cells, resting NK cells, resting mast cells, and neutrophil cells was higher in the low-risk group. After all, the Oncomine database and immunohistochemistry verified that the expression level of most key autophagy-related genes was consistent with the results that we found. In addition, we obtained six lncRNAs co-expressed with DEAGs from the training set and found that the survival time was longer in the low-risk group. This finding was verified in the verification set and showed same trend to the results mentioned above. In the final analysis, these results indicate that autophagy-related genes and lncRNAs can be used as prognostic and therapeutic markers for CRC.
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