This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
Research Article
Xihu Qin
The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0256-3478
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Hepatocellular carcinoma (HCC) has become the third leading cause of death from cancer worldwide, and PI3K/AKT signaling pathway acts as the most common oncogenic pathway in HCC. But few studies have reported the prognostic value of PI3K/AKT associated genes (PAGs) and their association with immune infiltration. Hence, we downloaded the mRNA sequencing data and clinical information from The Cancer Genome Atlas (TCGA), GSE14520 dataset of Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) database. The 105 PAGs gene sets were from the Gene set enrichment analysis (GSEA) website. The biological processes of differently expressed PAGs were explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then 4 prognostic PAGs (SFN, PRKAA2, PITX2 and CDK1) were identified through univariate and multivariate analyses. A prognostic signature was built base on the four PAGs. Afterward, the high-risk group had shorter survival time in Kaplan-Meier (KM) curves. Receiver operating characteristic (ROC) curves showed a better prognostic value of risk score (ROC=0.736) compared with other clinicopathological characteristics (AUC ≤ 0.511). The consistent results were obtained in the testing groups involving the GSE14520 dataset and ICGC database. The nomogram predicted the 1-year, 3-year, and 5-year overall survival rates in HCC. The correlation among risk score and immune infiltration of monocytes and M0 macrophages were determined, the expression levels of immune checkpoints (PDCD1, CTLA4, TIM3 and TIGIT) were also related to risk score in HCC. The study provided novel insight into the new targets for immunotherapy in HCC.
Keywords
Hepatocellular carcinoma, PI3K/AKT signaling pathway, immune checkpoints, immunotherapy, prognostic signature, nomogram
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
This is a list of supplementary files associated with this preprint. Click to download.
- Supplementarytable1.docx
- FigureS1.tif
Fig. S1. (a-d) Expressions of the four identified PAGs between HCC and normal samples. (e-h) Overall survival rates in K-M curves to verify the prognostic value of four PAGs in HCC.
- FigureS2.tif
Fig. S2. (a) Expressions of four identified PAGs in ONCOMINE database. (b) Immunohistochemistry (IHC) results showed the protein levels of three PAGs in HCC and normal tissues. (c) The genetic alteration of four PAGs in the cBioPortal database.
- FigureS3.tif
Fig. S3. Correlations between the risk score/four PAGs and clinicopathological characteristics of HCC patients. (a) risk score and pathology stage. (b) risk score and pathology grade. (c) risk score and pathology T classification. (d) CDK1 expression level and pathology stage. (e) CDK1 expression level and pathology grade. (f) CDK1 expression level and pathology T classification. (g) PRKAA2 expression level and pathology stage. (h) PRKAA2 expression level and pathology grade. (i) PRKAA2 expression level and pathology T classification. (j) PITX2 expression level and pathology stage. (k) PITX2 expression level and pathology T classification. (l) SFN expression level and pathology grade.
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
The most recent version of this article is available here.
No community comments so far
Version 1
Posted 12 May, 2021
No comments provided
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Learn more about our company.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Xihu Qin
The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0256-3478
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
The most recent version of this article is available here.
No community comments so far
Version 1
Posted 12 May, 2021
No comments provided
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
The most recent version of this article is available here.
Hepatocellular carcinoma (HCC) has become the third leading cause of death from cancer worldwide, and PI3K/AKT signaling pathway acts as the most common oncogenic pathway in HCC. But few studies have reported the prognostic value of PI3K/AKT associated genes (PAGs) and their association with immune infiltration. Hence, we downloaded the mRNA sequencing data and clinical information from The Cancer Genome Atlas (TCGA), GSE14520 dataset of Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) database. The 105 PAGs gene sets were from the Gene set enrichment analysis (GSEA) website. The biological processes of differently expressed PAGs were explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then 4 prognostic PAGs (SFN, PRKAA2, PITX2 and CDK1) were identified through univariate and multivariate analyses. A prognostic signature was built base on the four PAGs. Afterward, the high-risk group had shorter survival time in Kaplan-Meier (KM) curves. Receiver operating characteristic (ROC) curves showed a better prognostic value of risk score (ROC=0.736) compared with other clinicopathological characteristics (AUC ≤ 0.511). The consistent results were obtained in the testing groups involving the GSE14520 dataset and ICGC database. The nomogram predicted the 1-year, 3-year, and 5-year overall survival rates in HCC. The correlation among risk score and immune infiltration of monocytes and M0 macrophages were determined, the expression levels of immune checkpoints (PDCD1, CTLA4, TIM3 and TIGIT) were also related to risk score in HCC. The study provided novel insight into the new targets for immunotherapy in HCC.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
This is a list of supplementary files associated with this preprint. Click to download.
- Supplementarytable1.docx
- FigureS1.tif
Fig. S1. (a-d) Expressions of the four identified PAGs between HCC and normal samples. (e-h) Overall survival rates in K-M curves to verify the prognostic value of four PAGs in HCC.
- FigureS2.tif
Fig. S2. (a) Expressions of four identified PAGs in ONCOMINE database. (b) Immunohistochemistry (IHC) results showed the protein levels of three PAGs in HCC and normal tissues. (c) The genetic alteration of four PAGs in the cBioPortal database.
- FigureS3.tif
Fig. S3. Correlations between the risk score/four PAGs and clinicopathological characteristics of HCC patients. (a) risk score and pathology stage. (b) risk score and pathology grade. (c) risk score and pathology T classification. (d) CDK1 expression level and pathology stage. (e) CDK1 expression level and pathology grade. (f) CDK1 expression level and pathology T classification. (g) PRKAA2 expression level and pathology stage. (h) PRKAA2 expression level and pathology grade. (i) PRKAA2 expression level and pathology T classification. (j) PITX2 expression level and pathology stage. (k) PITX2 expression level and pathology T classification. (l) SFN expression level and pathology grade.
Loading...