Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 May 30:13:877278.
doi: 10.3389/fgene.2022.877278. eCollection 2022.

The Prognostic Signature and Therapeutic Value of Phagocytic Regulatory Factors in Prostate Adenocarcinoma (PRAD)

Shiyong Xin  1 , Xianchao Sun  1 , Liang Jin  1 , Weiyi Li  1 , Xiang Liu  2 , Liqing Zhou  3 , Lin Ye  1
Affiliations

The Prognostic Signature and Therapeutic Value of Phagocytic Regulatory Factors in Prostate Adenocarcinoma (PRAD)

Shiyong Xin et al. Front Genet. .

Abstract

There is growing evidence that phagocytosis regulatory factors (PRFs) play important roles in tumor progression, and therefore, identifying and characterizing these factors is crucial for understanding the mechanisms of cellular phagocytosis in tumorigenesis. Our research aimed to comprehensively characterize PRFs in prostate adenocarcinoma (PRAD) and to screen and determine important PRFs in PRAD which may help to inform tumor prognostic and therapeutic signatures based on these key PRFs. Here, we first systematically described the expression of PRFs in PRAD and evaluated their expression patterns and their prognostic value. We then analyzed prognostic phagocytic factors by Cox and Lasso analysis and constructed a phagocytic factor-mediated risk score. We then divided the samples into two groups with significant differences in overall survival (OS) based on the risk score. Then, we performed correlation analysis between the risk score and clinical features, immune infiltration levels, immune characteristics, immune checkpoint expression, IC50 of several classical sensitive drugs, and immunotherapy efficacy. Finally, the Human Protein Atlas (HPA) database was used to determine the protein expression of 18 PRF characteristic genes. The aforementioned results confirmed that multilayer alterations of PRFs were associated with the prognosis of patients with PRAD and the degree of macrophage infiltration. These findings may provide us with potential new therapies for PRAD.

Keywords: PRAD; immune infiltration; phagocytic factor; prostate cancer; survival analysis.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The flowchart of this study.
FIGURE 2
FIGURE 2
The expression of phagocytic factors and correlation with the phagocytic enrichment score in PRAD. (A) Analysis of phagocytic factor expression differences between normal and tumor samples (left: volcano diagram, red dots indicate up-regulation, blue dots indicate down-regulation); (B) Heat maps of the phagocytic factor expression in normal and tumor samples (right: red indicates the tumor group; blue indicates the normal group); (C–H) Correlation between 6 top phagocytic factors and phagocytic enrichment scores in PRAD samples (upper part); Comparison of the phagocytic enrichment score between the high- and low-expression group of top 6 top phagocytic genes (lower part).
FIGURE 3
FIGURE 3
Relationship between the top 6 phagocytic factors and prognoses in PRAD. (Upper part of figure: Patient’s survival probability between different expression group at different PFS times; Vertical axis: Survival probability; Horizontal axis: PFS Time; Lower part of figure: Number of cases in different gene expression groups at different PFS Times. (A) GTBP3, (B) NDUFV1, (C) PACS2, (D) KIF23, (E) FADD, and (F) MIB2. (p < 0.0001, p < 0.0001, p = 0.00017, p < 0.0001, p < 0.0001, p < 0.0001, and p < 0.0001, respectively).
FIGURE 4
FIGURE 4
Prognostic value of the risk model. (A) KM curve: upper part: the probability of survival at different times in the high- and low-risk groups; lower part: the number of cases in the high- and low-score groups at different times; (B) Triple plot: upper part: risk scores for the high- and low-risk groups; middle part: survival time of the living and deceased cases; lower part: gene expression in the high- and low-score groups; (C) ROC analysis: assess the AUC values for OS predictive efficacy of risk scores; (D–F) Prognostic model validation in GSE116918: KM curve, triple plot, and ROC analysis.
FIGURE 5
FIGURE 5
Correlation between the risk score with clinical characteristics. (A–H) Risk score comparison in different clinical characteristics in the training cohort (age, reccurrence, tnm_T, tnm_N, tnm_M, lymph_node, PSA, and Gleason, respectively. *p < 0.05, **p < 0.01, ***p < 0.001, ns: no significant). (I–L) Risk score comparison in different clinical characteristics in the validation cohort. (age, PSA, Gleason, and stage, respectively. *p < 0.05, **p < 0.01, ***p < 0.001, ns: no significant).
FIGURE 6
FIGURE 6
Forest map about the relationship between risk model and clinical characteristics (From left to right: Column 1: Clinical features; Column 2: p value; Column 3: Hazard Ratio forest map; Column 4: hazard ratio (HR) analysis with 95% confidence intervals (CI).). (A) Univariate Cox of model risk and eight clinical characteristics (age, recurrence, tnm_T, tnm_N, tnm_M, lymph_node, PSA, and Gleason) in the training cohort; (B) Multivariate Cox of recurrence, tnm_T, PSA, Gleason, and model risk in training cohort; (C) Univariate Cox of model risk and four clinical characteristics (Age, PSA, Gleason, and Stage) in the validation cohort; (D) Multivariate Cox of model risk and four clinical phenotypes (Age, PSA, Gleason, and Stage) in validation cohort.
FIGURE 7
FIGURE 7
Relationship between clinical characteristics and prognosis (TCGA: PRAD). (Upper part of figure: Patient’s survival probability between different clinical features group at different times; Vertical axis: Survival probability; Horizontal axis: Time; lower part of figure: Number of cases in different groups at different Times.). (A) Age, (B) tnm-N, (C) PSA, (D) Recurrence, (E) tnm-M, (F) Gleason, (G) tnm-T (H) Lymph node, and (I) Risk. (p = 0.0054,p = 0.3, p = 0.001, p < 0.0001, p = 0.014, p = 0.0063, p < 0.0001, p = 0.1, and p < 0.0001, respectively).
FIGURE 8
FIGURE 8
Relationship between classic drugs and PRFs. (A) The IC50 of Cisplatin, Paclitaxel, Methotrexate, Gemcitabine, and Doxorubicin in the high- and low-risk score groups. (B) The IC50 of Docetaxel in the high and low riskscore group. (C) The IC50 of Gefitinib in the high- and low-risk score groups. (D) The IC50 of Repamicin in the high- and low-risk score group (*p < 0.05, **p < 0.01, ***p < 0.001, ns: no significant).
FIGURE 9
FIGURE 9
Correlation between the risk model with immune infiltration. (A) Differences in immune score; (B) estimated score; (C,D) Risk score is correlated with the immune score and estimated score; (E) immune infiltration between high- and low-risk groups; (F–K) Comparison of immune checkpoints (PD-L1, PD1, and CTL-4) and pro-inflammatory factor (IL-6 and IL-8) expression between high- and low-risk groups.
FIGURE 10
FIGURE 10
Relationship between risk score and effect of immunotherapy in bladder cancer. (A) KMcurve; (B) Riskscore distribution of different efficacy groups; (C) Distribution of different immune efficacy groups in the high- and low-risk score groups; (D) Risk score histogram of different efficacy groups; (E) ROC curve at different times (bladder cancer).
FIGURE 11
FIGURE 11
Translational levels of the signature genes in the Human Protein Atlas (HPA) database. (A) ELVOL1 in normal prostate tissue; (B) ELVOL1 in prostate cancer tissue; (C) GNE in normal prostate tissue; (D) GNE in prostate cancer tissue; (E) PDCD10 in normal prostate tissue; and (F) PDCD10 in prostate cancer tissue.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Alanazi I. O., Alyahya S. A., Ebbrahimie E., Manijeh M-D. (2018). Computational Systems Biology Analysis of Biomarkers in Lung Cancer; Unravelling Genomic Regions Which Frequently Encode Biomarkers, Enriched Pathways, and New Candidates[J]. Gene 659, 29–36. 10.1016/j.gene.2018.03.038 - DOI - PubMed
    1. Andrea L., Giulio Di M., Paolo P., Ferro M. D., Carotti M., Sal G. D. (2010). A Genome-Scale Protein Interaction Profile of Drosophilap53 Uncovers Additional Nodes of the Human P53 Network [J]. Proc. Natl. Acid. Sci. U. S. A. 107 (14), 6322–6327. 10.1073/pnas.1002447107 - DOI - PMC - PubMed
    1. Arandjelovic S., Ravichandran K. S. (2015). Phagocytosis of Apoptotic Cells in Homeostasis. Nat. Immunol. 16, 907–917. 10.1038/ni.3253 - DOI - PMC - PubMed
    1. Bray F., Ferlay J., Soerjomataram I., Siegel R. L., Torre L. A., Jemal A. (2018). Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A Cancer J. Clin. 68 (6), 394–424. 10.3322/caac.21492 - DOI - PubMed
    1. Chae Y. C., Kim J. Y., Park J. W., Kim K. B., Oh H., Lee K. H., et al. (2019). FOXO1 Degradation via G9a-Mediated Methylation Promotes Cell Proliferation in Colon Cancer[J]. Nucleic Acids Res. 47 (4), 1692–1705. 10.1093/nar/gky1230 - DOI - PMC - PubMed

LinkOut - more resources