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Article Open Access

Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single‑cell and Mendelian randomization analyses

  • Authors:
    • Hua Yang
    • Lemeng Zhang
    • Jianhua Chen
    • Junjie Zhang
  • View Affiliations / Copyright

    Affiliations: Department of Thoracic Oncology, Hunan Cancer Hospital, Changsha, Hunan 410017, P.R. China, Department of Anesthesiology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, P.R. China
    Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 310
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    Published online on: May 21, 2026
       https://doi.org/10.3892/ol.2026.15665
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Abstract

The progression of lung adenocarcinoma (LUAD) is influenced by polyamine metabolism, which modulates antitumor immunity, although the underlying mechanisms remain unclear. The present study investigates the role of polyamine metabolism‑related genes (PMRGs) in LUAD using transcriptomic data, single‑cell RNA sequencing (scRNA‑seq) and Mendelian randomization. Differentially expressed PMRGs were identified through differential expression analysis and weighted gene co‑expression network analysis. Prognostic genes were selected via Cox regression and least absolute shrinkage and selection operator regression to construct a risk model. Immune infiltration, machine learning and scRNA‑seq were employed to explore molecular mechanisms whilst reverse transcription‑quantitative PCR (RT‑qPCR) validated gene expression in LUAD tissues. A nomogram incorporating risk scores assisted in predicting LUAD prognosis (area under the curve >0.6). Distinct immune cell profiles, particularly involving B cells and CD4+ T cells, were observed between high‑ and low‑risk groups. Drug sensitivity analysis identified 15 drugs with differential responses. Epithelial cells emerged as a key cluster, with dynamic changes in calcium voltage‑gated channel auxiliary subunit α2δ2 (CACNA2D2) expression during pseudotime. RT‑qPCR confirmed the downregulation of prognostic genes in LUAD. A polyamine metabolism‑related prognostic signature (CACNA2D2, adenoreceptor β‑1, immunoglobulin superfamily member 10 and carbonic anhydrase 4) associated with the tumor microenvironment was established, offering potential for enhanced prognosis prediction in LUAD.
View Figures

Figure 1

DE-PMRGs identification. (A) ssGSEA
rating violin chart: Blue represents the control group and red
represents the LUAD group. (B) Sample-level clustering and trait
heatmap: Blue represents the control group and red represents the
LUAD group. (C) Soft threshold selection: The horizontal axes of
all panels indicate the weight parameter power value. The vertical
axis of the left panel shows the scale-free fit index
(R2), while that of the right panel represents the mean
adjacency function values for all genes in the corresponding gene
module. (D) Identification of co-expression modules: The upper part
displays the hierarchical clustering dendrogram of genes, and the
lower part shows the gene modules. (E) Correlation heatmap between
modules and ssGSEA. The color block on the far left corresponds to
the modules, and the color bar on the far right indicates the
correlation range. In the middle heatmap, deeper colors represent
higher correlations, with red indicating positive correlation and
blue indicating negative correlation. DE-PMRG, differentially
expressed-polyamine metabolism-related genes; LUAD, lung
adenocarcinoma; ssGSEA, single sample gene set enrichment
analysis.

Figure 2

DEGs identification, functional
enrichment analysis and PPI network between LUAD and control
tissue. (A) Volcano plot highlighting the top 10 upregulated and
downregulated DEGs. The vertical axis represents
-log10(adj.P-value), and the horizontal axis denotes the fold
change (log2FC); each dot corresponds to one gene, with
circles representing individual samples. (B) Heatmap displaying
gene expression: Top 10 upregulated and top 10 downregulated genes
ranked by log2FC. (C) Identification of 470 DE-PMRGs. (D
and E) GO and KEGG enrichment analysis of DE-PMRGs: From the
outermost to the innermost circle: (1) The first layer shows GO functional IDs
across three categories: BP, CC and MF. (2) The second layer: Color intensity
indicates significance, with the length, width and numerical labels
representing the number of genes enriched in each function.
(3) The third layer: The number and
trend (indicated by color) of upregulated and downregulated genes
in each function. (4) The innermost
layer: the color of each block represents different functional
categories and the size corresponds to the RichFactor of each
pathway. (F) PPI network of DE-PMRGs. DEGs, differentially
expressed genes; PPI, protein-protein interactions; KEGG, Kyoto
Encyclopedia of Genes and Genomes; GO, Gene Ontology; DE-PMRGs,
differentially expressed polyamine metabolism-related genes; FC,
fold change; BP, biological process; CC, cellular component; MF,
molecular function; padj, adjusted P-value.

Figure 3

Mendelian screening forest map.
Identification of 30 candidate genes by Mendelian randomization
analysis, dominated by the results of the inverse variance weighted
algorithm.

Figure 4

Development and validation of the
polyamine-related risk model. (A) Forest plot of univariate Cox
regression analysis for prognostic genes (n=513 patients with
LUAD). (B) Lasso regression coefficient profiles of the candidate
genes. (C) Cross-validation for optimal λ selection in the Lasso
model. (D and E) Distribution of risk scores and survival status in
the training set; red dots represent high-risk samples and blue
dots represent low-risk samples. (F) Kaplan-Meier survival curves
comparing high- and low-risk groups. (G) ROC curves predicting 3-,
5- and 7-year overall survival. (H) Heatmap showing the expression
of four prognostic genes in the training set. AUC, area under the
curve, LUAD, lung adenocarcinoma; ROC, receiver operating
characteristic.

Figure 5

Validation of the prognostic risk
model in independent cohorts. Risk score distribution and survival
status mapping in the external validation datasets (A) GSE30219
(n=85) and (B) GSE31210 (n=226). (C) Kaplan-Meier survival curves
for the high- and low-risk groups in the combined validation set,
showing significant differences in overall survival. (D) ROC curve
analysis evaluating the accuracy of the prognostic model in
forecasting 3-, 5- and 7-year survival outcomes in the validation
cohorts. Red lines and blue lines in risk plots follow the same
definitions as in Fig. 4. Log-rank
test P-values are provided for survival comparisons. KM,
Kaplan-Meier; ROC, receiver operating characteristic; AUC, area
under the curve.

Figure 6

Independent prognostic factors and
LUAD nomogram construction. (A) Univariate and (B) multivariate Cox
regression analyses for screening independent prognostic factors in
LUAD. (C) Nomogram model: Variables in the model are key genes, and
‘Total Points’ represents the sum of individual scores
corresponding to the values of all variables. (D) Calibration curve
of the nomogram model with predicted probability on the x-axis,
actual probability on the y-axis. A calibration curve slope closer
to 1 reflects higher model prediction accuracy. (E and F) Decision
curve analysis curve with threshold probability on the x-axis, and
net benefit rate (after subtracting harms from benefits) is on the
y-axis. (G) Box plot showing risk score differences across clinical
characteristics. *P<0.05 and **P<0.01. ns, not significant;
OS, overall survival; T, tumour; N, lymph node; M, metastasis;
LUAD, lung adenocarcinoma,

Figure 7

Prognostic gene-related genes and
functional similarity analysis. (A) GeneMANIA database analysis
network diagram. A total of 20 genes associated with prognostic
gene functions were predicted. The large central circle represents
prognostic genes, and the small outer circles represent genes
correlated with prognostic genes. (B) Functional similarity cloud
rain diagram. The horizontal axis denotes the similarity score, and
the vertical axis represents prognostic genes. (C) Chromosomal
localization of prognostic genes: The first ring shows gene names,
with red indicating upregulated genes and blue indicating
downregulated genes; the second ring represents the chromosomal
locations of genes; the third ring displays the expression profiles
of genes in samples; the fourth ring presents the log2FC
values of genes, where blue indicates log2FC <0 and
red indicates log2FC >0. (D) Subcellular localization
of prognostic genes: The horizontal axis represents prognostic
genes, the vertical axis denotes the percentage and different
colors correspond to distinct subcellular localizations. (E)
lncRNA-mRNA-miRNA regulatory network where red represents
prognostic genes, yellow represents miRNA and blue represents
lncRNA. (F) TF-mRNA-miRNA network diagram where red represents
prognostic genes, blue represents TF and yellow represents miRNA.
FC, fold change, lncRNA, long non-coding RNAs; TF, transcription
factor; miRNA/miR, microRNA.

Figure 8

Enrichment analysis, immune cell
infiltration and drug sensitivity analysis. (A) GSEA for GO
categories, GOBP, GOCC and GOMF, in high-vs. low-risk groups. (B)
GSEA for KEGG pathways. (C) Box plot showing differences in 15
immune cell populations; red bars represent high-risk and blue bars
represent low-risk samples (n=513). (D) Correlation heatmap between
prognostic genes and immune cells. (E) Spearman correlation
analysis between risk scores and drug IC50 values. (F)
Differential sensitivity of 15 antineoplastic drugs between risk
groups. All data are presented as median values. *P<0.05,
**P<0.01, ***P<0.001 and ****P<0.0001 (Wilcoxon test). ns,
not significant' KEGG, Kyoto Encyclopedia of Genes and Genomes; GO,
Gene Ontology; GOBP, Gene Ontology Biological Process; GOCC, Gene
Ontology Cellular Component; GOMF, Gene Ontology Molecular
Function; GSEA, gene set enrichment analysis; MDSC, myleoid-derived
suppressor cells.

Figure 9

Significant enrichment of signaling
pathways and identification of key cell clusters. (A) Annotated
UMAP clustering diagram: Eight cell types were annotated according
to marker genes, including epithelial cells, fibroblasts,
endothelial cells, T lymphocytes, NK cells, B lymphocytes, myeloid
cells and mast cells. (B) Proportion chart of each cell type in
LUAD and control groups. (C) Violin plot of prognostic gene
expression between the LUAD and control groups. (D) Enrichment
analysis heatmap of cells. (E) Proposed temporal trajectory
diagram: trajectory diagram of different cell subpopulations and
their differentiation stages. (F) Expression of prognostic genes in
different temporal stages. (G) Quantitative diagram of cell
communication interactions and probability intensity values of cell
communication interactions. *P<0.05, **P<0.01 and
***P<0.001 were considered statistically significant. NK,
natural killer; LUAD, lung adenocarcinoma; UMAP, Uniform Manifold
Approximation and Projection.

Figure 10

Validation of prognostic gene
expression in clinical samples. (A-B) Differential expression of
four prognostic genes in TCGA training and validation sets. (C-F)
RT-qPCR validation of CACNA2D2, ADRB1, IGSF10 and CA4 mRNA levels
in 5 pairs of LUAD and adjacent control tissues collected from
Hunan Cancer Hospital (Changsha, China). Data are expressed as mean
± SD of three technical replicates (n=5 patients per group). The
internal reference gene was GAPDH. Statistical analysis was
performed using Wilcoxon signed-rank test with ***P<0.001 and
****P<0.0001. TCGA, The Cancer Genome Atlas; LUAD, lung
adenocarcinoma; RT-qPCR, reverese transcription-quantitative
PCR.
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Copy and paste a formatted citation
Spandidos Publications style
Yang H, Zhang L, Chen J and Zhang J: Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single‑cell and Mendelian randomization analyses. Oncol Lett 32: 310, 2026.
APA
Yang, H., Zhang, L., Chen, J., & Zhang, J. (2026). Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single‑cell and Mendelian randomization analyses. Oncology Letters, 32, 310. https://doi.org/10.3892/ol.2026.15665
MLA
Yang, H., Zhang, L., Chen, J., Zhang, J."Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single‑cell and Mendelian randomization analyses". Oncology Letters 32.1 (2026): 310.
Chicago
Yang, H., Zhang, L., Chen, J., Zhang, J."Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single‑cell and Mendelian randomization analyses". Oncology Letters 32, no. 1 (2026): 310. https://doi.org/10.3892/ol.2026.15665
Copy and paste a formatted citation
x
Spandidos Publications style
Yang H, Zhang L, Chen J and Zhang J: Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single‑cell and Mendelian randomization analyses. Oncol Lett 32: 310, 2026.
APA
Yang, H., Zhang, L., Chen, J., & Zhang, J. (2026). Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single‑cell and Mendelian randomization analyses. Oncology Letters, 32, 310. https://doi.org/10.3892/ol.2026.15665
MLA
Yang, H., Zhang, L., Chen, J., Zhang, J."Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single‑cell and Mendelian randomization analyses". Oncology Letters 32.1 (2026): 310.
Chicago
Yang, H., Zhang, L., Chen, J., Zhang, J."Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single‑cell and Mendelian randomization analyses". Oncology Letters 32, no. 1 (2026): 310. https://doi.org/10.3892/ol.2026.15665
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