RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Conclusion
In summary, we elucidated the abnormalities among cellular senescence-related pathways in TME of malignant cells of TNBC. Senescence-related subtypes and a risk model were constructed based on the genes in these pathways. Also, we validated the oncogenic gene ACP5 ’s ability to migrate and invade in TNBC cells. Additionally, a nomogrom was designed for future clinical decision-making. All these findings provide a basis for the revelation of the molecular mechanisms by which senescence-associated genes influence TNBC progression and directions for TNBC therapy and prognosis.
Supplemental Information
A: A total of 961 promising candidates were identified among the DEGs; B: Changing trajectory of each independent variable with lambda; C: Confidence interval for lambda; D: Coefficients of prognosis-related genes in multivariate Cox analysis.
Figure 1: TSNE dimensionality-reduction results.
(A) TNSE plots for the distribution of nine samples; (B) TSNE plot for 11 immune cell subpopulations; (C) TSNE plot for cell distribution after annotation; (D) dot plots of the top five marker genes’ expression in the annotated subpopulations; (E) proportions and cell count of the annotated subpopulations in each sample; (F) distribution of malignant and non-malignant cells predicted by “copykat”; (G) proportion of malignant and non-malignant cells in each sample.

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DOI: 10.7717/peerj.16935/fig-1
Figure 2: Single-cell GSVA results of cellular senescence-related pathways in malignant and non-malignant cells.

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DOI: 10.7717/peerj.16935/fig-2
Figure 3: Enrichment analysis for bulk RNA-seq data.
(A) GSEA results in TCGA dataset; (B) Heat map of the distribution of cellular senescence-related pathway ssGSEA scores in cancerous and para-cancerous tissues in TCGA dataset.

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DOI: 10.7717/peerj.16935/fig-3
Figure 4: Analysis of cellular senescence-related prognostic genes.
(A) Forest plot of prognosis-related genes; (B) waterfall plot of SNVs in the seven prognosis-related genes; (C) Percentage plot of CNVs in the seven prognosis-related genes; (D) The expression of the seven prognosis-related genes expressed in tumor and normal tissues; (E) CDF curve of TCGA cohort; (F) CDF-delta area curves for TCGA cohort. The Delta area curve for consensus clustering indicates the relative change in the area under the CDF curve for each category number, k, relative to k-1. The horizontal axis represents the category number, k, and the vertical axis represents the relative change in area under the CDF curve; (G) Heat map of sample clustering at consensus k = 3; (H) KM curve of the relationship between the prognoses of the three subtypes in TCGA; (I) Heat map of the expression of seven prognosis-related genes among three subtypes in TCGA dataset.

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DOI: 10.7717/peerj.16935/fig-4
Figure 5: Differential analysis of cellular senescence-related subtype mutations.
(A) Comparison of CNVs among subtypes; (B) Waterfall plots of the top 10 genes with the highest SNVs among subtypes; (C) Peak plots of genes amplified (red) and missing (blue) among the three subtypes (G scores (top) and q-values (bottom) relative to the entire graph for amplification of all markers on the analyzed region).

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DOI: 10.7717/peerj.16935/fig-5
Figure 6: Biological characterization of cellular senescence-related subtypes in TCGA-TNBC dataset.
(A) CCP scores compared across three subtypes; (B) G1/S phase scores compared across three subtypes; (C) G2M-phase immune checkpoint scores compared across three subtypes; (D) Telomere extension scores of telomerase compared across three subtypes; (E) EMT scores among three different subtypes; (F) Hypoxic scores among three different subtypes; (G) Angiogenesis scores among three different subtypes; (H) Inflammatory factor scores among three different subtypes. (p > 0.05 denotes no significance; ****p < 0.0001, ***p < 0.001, **p < 0.01, and *p < 0.05).

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Figure 7: Immune characterization of cellular senescence-related subtypes in TCGA dataset.
(A) Comparison of ESTIMATE-predicted immune scores among the three subtypes (kruskal.test); (B) Comparison of MCPcounter-predicted cell scores among the three subtypes(kruskal.test); (C) Comparison of TIMER-predicted cell scores among the three subtypes (kruskal.test); (D) Comparison of EPIC-predicted cell scores among the three subtypes (kruskal.test); (E) Differences in immune checkpoint gene expression among the three subtypes (kruskal.test); (F) TIDE analysis for differential scores among the three subtypes (wilcox.test) (p > 0.05 denotes no significance; ****p < 0.0001, ***p < 0.001, **p < 0.01, and *p < 0.05).

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Figure 8: Construction and validation of the risk model.
(A) ROC curves for the risk model constructed using four genes from TCGA dataset analysis; (B) KM curves for the risk model based on TCGA dataset; (C) ROC curves for the risk model constructed using four genes for the GSE58812 dataset; (D) KM curves for the risk model based on the GSE58812 dataset.

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Figure 9: The validation of cellular regulation function of signature.
(A–D) The mRNA expression levels of MMP28, CT83, ACP5 and KRT6A in MCF-10A, MDA-MB-468 and MDA-MB-231 cell lines. (E) Representative migration and invasion images of MDA-MB-468 and MDA-MB-231 cell lines after inhibition of ACP5. (F–G) Quantitative analysis for migration and invasion ability. n = 3, *≤0.05, **≤0.01, ***≤0.001. The results are presented as mean ± S.E.M.

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Figure 10: Immunological characteristics of RiskScore subgroups and sensitivity analysis in TCGA-TNBC cohort.
(A) Differences in MCPcounter-predicted cell scores between risk groups; (B) Differences in immune and stromal scores between risk groups; (C) comparison of TIMER-predicted cell scores between risk groups; (D) comparison of EPIC-predicted cell scores between risk groups; (E) Differentially expressed immune checkpoints between risk groups; (F) Correlation analysis of cell scores with risk scores; (G) Correlation analysis of ImmuneScore, StromalScore, and ESTIMATEScore with risk scores; (H) The box plot of estimated IC50 for drugs (wilcox.test; p > 0.05 denotes no significance, *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001).

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Figure 11: Prognostic model and survival prediction.
(A–B) Univariate and multivariate Cox analyses of RiskScore and clinicopathological characteristics; (C) Nomogram model; (D) 1-, 3-, and 5-year calibration curves for the nomogram; (E) Decision curve for the nomogram; (F) Relative to other clinicopathological features, the nomogram exhibits the best capacity for survival prediction.

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DOI: 10.7717/peerj.16935/fig-11
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