The Effects of Autophagy-Related Genes and lncRNAs in Therapy and Prognosis of Colorectal Cancer

Yang, Yang; Feng, Mingyang; Bai, Liangliang; Zhang, Mengxi; Zhou, Kaiyu; Liao, Wei‐Ting; Lei, Wenhua; Zhang, Nan; Huang, Jiaxing; Qiu, Li

doi:10.3389/fonc.2021.582040

Cited by 6 publications

(6 citation statements)

References 54 publications

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“…Furthermore, our study showed that the abundance of TIICs, such as T cells, resting memory CD4 T cells, and activated mast cells, was lower in the low-risk group compared with the high-risk group. These results are consistent with the results of Yang et al [ 53 ]. Besides, our study showed that the abundance of TIICs, such as resting memory CD4 T cells and activated mast cells, was lower in the low-risk score group compared with the high-risk group.…”

Section: Discussionsupporting

confidence: 94%

Development and validation of cuproptosis-related lncRNA signatures for prognosis prediction in colorectal cancer

Pang

Wang

et al. 2023

BMC Med Genomics

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Background Cuproptosis, a novel form of programmed cell death, plays an essential role in various cancers. However, studies of the function of cuproptosis lncRNAs (CRLs) in colorectal cancer (CRC) remain limited. Thus, this study aims to identify the cuprotosis-related lncRNAs (CRLs) in CRC and to construct the potential prognostic CRLs signature model in CRC. Methods First, we downloaded RNA-Seq data and clinical information of CRC patients from TCGA database and obtained the prognostic CRLs based on typical expression analysis of cuproptosis-related genes (CRGs) and univariate Cox regression. Then, we constructed a prognostic model using the Least Absolute Shrinkage and Selection Operator algorithm combined with multiple Cox regression methods (Lasso-Cox). Next, we generated Kaplan–Meier survival and receiver operating characteristic curves to estimate the performance of the prognostic model. In addition, we also analysed the relationships between risk signatures and immune infiltration, mutation, and drug sensitivity. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT -PCR) to verify the prognostic model. Result Lasso-Cox analysis revealed that four CRLs, SNHG16, LENG8-AS1, LINC0225, and RPARP-AS1, were related to CRC prognosis. Receiver operating characteristic (ROC) and Kaplan–Meier analysis curves indicated that this model performs well in prognostic predictions of CRC patients. The DCA results also showed that the model included four gene signatures was better than the traditional model. In addition, GO and KEGG analyses revealed that DE-CRLs are enriched in critical signalling pathway, such as chemical carcinogenesis-DNA adducts and basal cell carcinoma. Immune infiltration analysis revealed significant differences in immune infiltration cells between the high-risk and low-risk groups. Furthermore, significant differences in somatic mutations were noted between the high-risk and low-risk groups. Finally, we also validated the expression of four CRLs in FHCs cell lines and CRC cell lines using qRT-PCR. Conclusion The signature composed of SNHG16, LENG8-AS1, LINC0225, and RPARP-AS1, which has better performance in predicting colorectal cancer prognosis and are promising biomarkers for prognosis prediction of CRC.

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Section: Discussionsupporting

confidence: 94%

Development and validation of cuproptosis-related lncRNA signatures for prognosis prediction in colorectal cancer

Pang

Wang

et al. 2023

BMC Med Genomics

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“…In our study, according to the risk score formula, patients were divided into the low-risk group and high-risk group by the median risk score. Similar to the previous studies ( Fan, Tian, 2020 ; Yang et al, 2021 ), the K-M curve showed that low-risk group survival was longer than the high-risk group with significance ( p < 0.05). The AUCs at 1-, 3-, and 5-years were 0.72, 0.814, and 0.83, which indicate that the predictive ability of the risk score signatured by autophagy-related lncRNAs were stable and the risk score can be set as an independent prognostic indicator.…”

Section: Discussionsupporting

confidence: 88%

Identification of Autophagy-Related LncRNA to Predict the Prognosis of Colorectal Cancer

Duan

Xia

et al. 2022

Front. Genet.

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Objective: To establish a prediction model based on autophagy-related lncRNAs and investigate the functional enrichment of autophagy-related lncRNAs in colorectal cancer.Methods: TCGA database was used to extract the transcriptome data and clinical features of colorectal cancer patients. HADb was used to obtain autophagy-related genes. Pearson correlation analysis was performed to identify autophagy-related lncRNAs. The autophagy-related lncRNAs with prognostic values were selected. Based on the selected lncRNAs, the risk score model and nomogram were constructed, respectively. Calibration curve, concordance index, and ROC curve were performed to evaluate the predictive efficacy of the prediction model. GSEA was performed to figure out the functional enrichment of autophagy-related lncRNAs.Results: A total of 13413 lncRNAs and 938 autophagy-related genes were obtained. A total of 709 autophagy-related genes were identified in colon cancer tissues, and 11 autophagy-related lncRNAs (AL138756.1, LINC01063, CD27-AS1, LINC00957, EIF3J-DT, LINC02474, SNHG16, AC105219.1, AC068580.3, LINC02381, and LINC01011) were finally selected and set as prognosis-related lncRNAs. According to the risk score, patients were divided into the high-risk and low-risk groups, respectively. The survival K–M (Kaplan–Meier) curve showed the low-risk group exhibits better overall survival than the high-risk group. The AUCs under the ROC curves were 0.72, 0.814, and 0.83 at 1, 3, and 5 years, respectively. The C-index (concordance index) of the model was 0.814. The calibration curves at 1, 3, and 5 years showed the predicting values were consistent with the actual values. Functional enrichment analysis showed that autophagy-related lncRNAs were enriched in several pathways.Conclusions: A total of 11 specific autophagy-related lncRNAs were identified to own prognostic value in colon cancer. The predicting model based on the lncRNAs and clinical features can effectively predict the OS. Furthermore, functional enrichment analysis showed that autophagy-related genes were enriched in various biological pathways.

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“…In detail, the IRLncSig and age were treated as continuous variables, while the pathologic stage was coded as binary, "Stage I/II" and "Stage III/IV". We also compared the IRLncSig with four published prognostic signatures in CRC: a 15gene signature derived from Yang's study (Yang et al, 2021), a 6-lncRNA signature derived from Li's study (Li et al, 2020), a 6gene signature derived from Dai's study (Dai et al, 2020), and a 7-gene signature derived from Lu's study (Lu et al, 2021).…”

Section: Performance Comparison Of the Irlncsig With Other Clinical V...mentioning

confidence: 99%

“…We compared the IRLncSig with four recently published prognostic signatures in CRC: a 15-gene signature derived from Yang's study (Yang et al, 2021), a 6-lncRNA signature derived from Li's study (Li et al, 2020), a 6-gene signature derived from Dai's study (Dai et al, 2020), and a 7-gene signature derived from Lu's study (Lu et al, 2021). The results showed that the AUC values at 1-year, 3-years and 5-years were 0.78, 0.78 and 0.79, respectively, which were all higher than those of the other four signatures (Figure 9).…”

Section: Performance Comparison Of the Irlncsig With Other Clinical V...mentioning

confidence: 99%

Identification and validation of an inflammation-related lncRNAs signature for improving outcomes of patients in colorectal cancer

Huang

Ye²,

Chen³

et al. 2022

Front. Genet.

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Background: Colorectal cancer is the fourth most deadly cancer worldwide. Although current treatment regimens have prolonged the survival of patients, the prognosis is still unsatisfactory. Inflammation and lncRNAs are closely related to tumor occurrence and development in CRC. Therefore, it is necessary to establish a new prognostic signature based on inflammation-related lncRNAs to improve the prognosis of patients with CRC.Methods: LASSO-penalized Cox analysis was performed to construct a prognostic signature. Kaplan-Meier curves were used for survival analysis and ROC curves were used to measure the performance of the signature. Functional enrichment analysis was conducted to reveal the biological significance of the signature. The R package “maftool” and GISTIC2.0 algorithm were performed for analysis and visualization of genomic variations. The R package “pRRophetic”, CMap analysis and submap analysis were performed to predict response to chemotherapy and immunotherapy.Results: An effective and independent prognostic signature, IRLncSig, was constructed based on sixteen inflammation-related lncRNAs. The IRLncSig was proved to be an independent prognostic indicator in CRC and was superior to clinical variables and the other four published signatures. The nomograms were constructed based on inflammation-related lncRNAs and detected by calibration curves. All samples were classified into two groups according to the median value, and we found frequent mutations of the TP53 gene in the high-risk group. We also found some significantly amplificated regions in the high-risk group, 8q24.3, 20q12, 8q22.3, and 20q13.2, which may regulate the inflammatory activity of cancer cells in CRC. Finally, we identified chemotherapeutic agents for high-risk patients and found that these patients were more likely to respond to immunotherapy, especially anti-CTLA4 therapy.Conclusion: In short, we constructed a new signature based on sixteen inflammation-related lncRNAs to improve the outcomes of patients in CRC. Our findings have proved that the IRLncSig can be used as an effective and independent marker for predicting the survival of patients with CRC.

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The Effects of Autophagy-Related Genes and lncRNAs in Therapy and Prognosis of Colorectal Cancer

Cited by 6 publications

References 54 publications

Development and validation of cuproptosis-related lncRNA signatures for prognosis prediction in colorectal cancer

Development and validation of cuproptosis-related lncRNA signatures for prognosis prediction in colorectal cancer

Identification of Autophagy-Related LncRNA to Predict the Prognosis of Colorectal Cancer

Identification and validation of an inflammation-related lncRNAs signature for improving outcomes of patients in colorectal cancer

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