Evidence map›Paper›PMID 37124502›Full record

ArticleFrontiers in oncology2023

Clustering analysis and prognostic model based on PI3K/AKT-related genes in pancreatic cancer.

Xiangying Deng, Xu He, Zehua Yang, Jing Huang, Lin Zhao, Min Wen, Xiyuan Hu, Zizheng Zou

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.2field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Xiangying DengYiyang Key Laboratory of Chemical Small Molecule Anti-Tumor Targeted Therapy, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Yiyang Medical College, Yiyang, China.
Xu HeYiyang Key Laboratory of Chemical Small Molecule Anti-Tumor Targeted Therapy, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Yiyang Medical College, Yiyang, China.
Zehua YangYiyang Key Laboratory of Chemical Small Molecule Anti-Tumor Targeted Therapy, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Yiyang Medical College, Yiyang, China.
Jing HuangYiyang Key Laboratory of Chemical Small Molecule Anti-Tumor Targeted Therapy, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Yiyang Medical College, Yiyang, China.
Lin ZhaoDepartment of Pathology, The Second Xiangya Hospital, Central South University, Changsha, China.
Min WenYiyang Key Laboratory of Chemical Small Molecule Anti-Tumor Targeted Therapy, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Yiyang Medical College, Yiyang, China.
Xiyuan HuDepartment of Biochemistry and Molecular Biology, Hunan Province Key Laboratory of Basic and Applied Hematology, Hunan Key Laboratory of Animal Models for Human Diseases, School of Life Sciences, Xiangya School of Medicine, Central South University, Changsha, China.
Zizheng ZouYiyang Key Laboratory of Chemical Small Molecule Anti-Tumor Targeted Therapy, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Yiyang Medical College, Yiyang, China.
Nanyang Medical College · CNCentral South University · CNXiangya Hospital Central South University · CNSecond Xiangya Hospital of Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Pancreatic cancer is one of most aggressive malignancies with a dismal prognosis. Activation of PI3K/AKT signaling is instrumental in pancreatic cancer tumorigenesis. The aims of this study were to identify the molecular clustering, prognostic value, relationship with tumor immunity and targeting of PI3K/AKT-related genes (PARGs) in pancreatic cancer using bioinformatics. Methods: The GSEA website was searched for PARGs, and pancreatic cancer-related mRNA data and clinical profiles were obtained through TCGA downloads. Prognosis-related genes were identified by univariate Cox regression analysis, and samples were further clustered by unsupervised methods to identify significant differences in survival, clinical information and immune infiltration between categories. Next, a prognostic model was constructed using Lasso regression analysis. The model was well validated by univariate and multivariate Cox regression analyses, Kaplan-Meier survival analysis and ROC curves, and correlations between risk scores and patient pathological characteristics were identified. Finally, GSEA, drug prediction and immune checkpoint protein analyses were performed. Results: Pancreatic cancers were divided into Cluster 1 (C1) and Cluster 2 (C1) according to PARG mRNA expression. C1 exhibited longer overall survival (OS) and higher immune scores and CTLA4 expression, whereas C2 exhibited more abundant PD-L1. A 6-PARG-based prognostic model was constructed to divide pancreatic cancer patients into a high-risk score (HRS) group and a low-risk score (LRS) group, where the HRS group exhibited worse OS. The risk score was defined as an independent predictor of OS. The HRS group was significantly associated with pancreatic cancer metastasis, aggregation and immune score. Furthermore, the HRS group exhibited immunosuppression and was sensitive to radiotherapy and guitarbine chemotherapy. Multidrug sensitivity prediction analysis indicated that the HRS group may be sensitive to PI3K/AKT signaling inhibitors (PIK-93, GSK2126458, CAL-101 and rapamycin) and ATP concentration regulators (Thapsigargin). In addition, we confirmed the oncogenic effect of protein phosphatase 2 regulatory subunit B'' subunit alpha (PPP2R3A) in pancreatic cancer Conclusions: PARGs predict prognosis, tumor immune profile, radiotherapy and chemotherapy drug sensitivity and are potential predictive markers for pancreatic cancer treatment that can help clinicians make decisions and personalize treatment.

Indexed as

bioinformaticspancreatic cancerPI3K/AKT-related genesPPP2R3Aprognostic model

Identifiers

PMID37124502
PMCPMC10140326
OpenAlexW4367601316

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.