Identification of potential crucial genes associated with the pathogenesis and prognosis of pancreatic adenocarcinoma

Shi, Laner; Shang, Xin; Nie, Kechao; Xu, Qiang; Chen, Na‐βei; Zhu, Zhangzhi

doi:10.3892/ol.2020.11921

Cited by 7 publications

(10 citation statements)

References 61 publications

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“…Therefore, identification of prognostic-related biomarkers and the development of a prognostic model is of critical importance for HCC patients. Currently, bioinformatics analyses have been shown to play an increasingly significant role in identifying therapeutic targets for the diagnosis, treatment, and prognosis of numerous tumors [ 22 ]. Apoptosis plays a crucial role in liver tumor development and regeneration [ 12 , 23 ].…”

Section: Discussionmentioning

confidence: 99%

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A prognostic model for hepatocellular carcinoma based on apoptosis-related genes

et al. 2021

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Background Dysregulation of the balance between proliferation and apoptosis is the basis for human hepatocarcinogenesis. In many malignant tumors, such as hepatocellular carcinoma (HCC), there is a correlation between apoptotic dysregulation and poor prognosis. However, the prognostic values of apoptosis-related genes (ARGs) in HCC have not been elucidated. Methods To screen for differentially expressed ARGs, the expression levels of 161 ARGs from The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) were analyzed. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to evaluate the underlying molecular mechanisms of differentially expressed ARGs in HCC. The prognostic values of ARGs were established using Cox regression, and subsequently, a prognostic risk model for scoring patients was developed. Kaplan–Meier (K-M) and receiver operating characteristic (ROC) curves were plotted to determine the prognostic value of the model. Results Compared with normal tissues, 43 highly upregulated and 8 downregulated ARGs in HCC tissues were screened. GO analysis results revealed that these 51 genes are indeed related to the apoptosis function. KEGG analysis revealed that these 51 genes were correlated with MAPK, P53, TNF, and PI3K-AKT signaling pathways, while Cox regression revealed that 5 ARGs (PPP2R5B, SQSTM1, TOP2A, BMF, and LGALS3) were associated with prognosis and were, therefore, obtained to develop the prognostic model. Based on the median risk scores, patients were categorized into high-risk and low-risk groups. Patients in the low-risk groups exhibited significantly elevated 2-year or 5-year survival probabilities (p < 0.0001). The risk model had a better clinical potency than the other clinical characteristics, with the area under the ROC curve (AUC = 0.741). The prognosis of HCC patients was established from a plotted nomogram. Conclusion Based on the differential expression of ARGs, we established a novel risk model for predicting HCC prognosis. This model can also be used to inform the individualized treatment of HCC patients.

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Section: Discussionmentioning

confidence: 99%

“…The potential impact of these 5 hub ARGs on the progression of HCC were established. For example, TOP2A was identified as a potential biomarker for cancer therapy in ovarian cancer, colon cancer, pancreatic adenocarcinoma, and HCC [ 9 , 22 , 28 , 29 ]. However, TOP2A was upregulated in HCC [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

A prognostic model for hepatocellular carcinoma based on apoptosis-related genes

et al. 2021

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“…Therefore, identi cation of prognostic-related biomarkers and the development of a prognostic model is of critical importance for HCC patients. Currently, bioinformatics analyses have been shown to play an increasingly signi cant role in identifying therapeutic targets for the diagnosis, treatment, and prognosis of numerous tumors [22]. Apoptosis plays a crucial role in liver tumor development and regeneration [12,23].…”

Section: Discussionmentioning

confidence: 99%

“…The potential impact of these 5 hub ARGs on the progression of HCC were established. For example, TOP2A was identi ed as a potential biomarker for cancer therapy in ovarian cancer, colon cancer, pancreatic adenocarcinoma, and HCC [9,22,28,29]. However, TOP2A was up-regulated in HCC [30].…”

Section: Discussionmentioning

confidence: 99%

A prognostic model for hepatocellular carcinoma based on apoptosis-related genes

Liu

Wang

Zhang

et al. 2021

Preprint

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Background: Dysregulation of the balance between proliferation and apoptosis is the basis for human hepatocarcinogenesis. In many malignant tumors, such as hepatocellular carcinoma (HCC), there is a correlation between apoptotic dysregulation and poor prognosis. However, the prognostic values of apoptosis-related genes (ARGs) in HCC have not been elucidated. Methods: To screen for differentially expressed ARGs, the expression levels of 161 ARGs from The Cancer Genome Atlas (TCGA) database(https://cancergenome.nih.gov/) were analyzed. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to evaluate the underlying molecular mechanisms of differentially expressed ARGs in HCC. The prognostic values of ARGs were established using Cox regression, and subsequently, a prognostic risk model for scoring patients was developed. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were plotted to determine the prognostic value of the model. Results: Compared to normal tissues, 43 highly up-regulated and 8 down-regulated ARGs in HCC tissues were screened. GO analysis results revealed that these 51 genes are indeed related to the apoptosis function. KEGG analysis revealed that these 51 genes were correlated with MAPK, P53, TNF, and PI3K-AKT signaling pathways, while Cox regression revealed that 5 ARGs (PPP2R5B, SQSTM1, TOP2A, BMF, and LGALS3) were associated with prognosis and were, therefore, obtained to develop the prognostic model. Based on the median risk scores, patients were categorized into high-risk and low-risk groups. Patients in the low-risk groups exhibited significantly elevated two-year or five-year survival probabilities (p < 0.0001). The risk model had a better clinical potency than the other clinical characteristics, with the area under the ROC curve (AUC = 0.741). The prognosis of HCC patients was established from a plotted nomogram. Conclusion: Based on the differential expression of ARGs, we established a novel risk model for predicting HCC prognosis. This model can also be used to inform the individualized treatment of HCC patients.

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“…Therefore, the identi cation of a prognosis-related biomarker and the development of a prognostic model is of critical importance for HCC patients. Currently, bioinformatics analysis plays an increasingly signi cant role in identifying therapeutic targets for diagnosis, treatment, and prognosis of numerous tumors [19]. The process of apoptosis plays a crucial role in liver tumor development and regeneration [12,20].…”

Section: Discussionmentioning

confidence: 99%

Identification of hepatocellular carcinoma risk using a novel prognostic model based on apoptosis-related genes

Liu

Wang

Zhang

et al. 2021

Preprint

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Background Dysregulation of the balance between proliferation and apoptosis is the basis in human hepatocarcinogenesis. There is a possible association of apoptosis dysregulation with poor prognosis in many malignant tumors, such as hepatocellular carcinoma (HCC). However, the prognostic effect of Apoptosis-related genes (ARGs) on HCC is still unclear. Methods A total of 161 ARGs expression levels were analyzed based on The Cancer Genome Atlas (TCGA) database(https://cancergenome.nih.gov/) to screen for differentially expressed ARGs. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to determine the underlying molecular mechanisms of screened ARGs in HCC. ARGs prognostic values were identified using Cox regression to subsequently establish a prognostic risk model and scoring in patients. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were plotted to determine the prognostic value in the model. Results Compared to normal specimens, 43 highly upregulated and 8 downregulated ARGs respectively and their normal counterparts in HCC specimens were screened. KEGG analysis demonstrated pathways correlated with these 51 genes which included MAPK, P53, TNF, PI3K-Akt signaling pathways. With Cox regression, 5 prognostic correlated with ARGs (PPP2R5B, SQSTM1, TOP2A, BMF, and LGALS3) were obtained to develop the prognosis model. According to the median of risk scores, patients were categorized into high-risk and low-risk groups. Patients in low-risk groups had a significantly higher two-year or five-year survival probability (p < 0.0001). The risk model had better potency than other clinical characteristics, with the area under the ROC curve (AUC = 0.741). Prognosis of HCC patients was established from a plotted nomogram. Conclusion This present study established a novel prognostic risk model for predicting HCC according to the expression of ARGs. The present advancement can potentially contribute to prediction prognosis and individualized treatment of HCC patients.

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Identification of potential crucial genes associated with the pathogenesis and prognosis of pancreatic adenocarcinoma

Cited by 7 publications

References 61 publications

A prognostic model for hepatocellular carcinoma based on apoptosis-related genes

A prognostic model for hepatocellular carcinoma based on apoptosis-related genes

A prognostic model for hepatocellular carcinoma based on apoptosis-related genes

Identification of hepatocellular carcinoma risk using a novel prognostic model based on apoptosis-related genes

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