RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Supplemental Information
(A) The mutational landscape reflected that mutated events occurred more frequently in high ARGs-score samples than that in low group. Besides, the Chi-square test revealed that TP53, KMT2D, FOXA1 and PTEN especially harbored more mutants compared with that in group with low risk ARGs scores. (B) Scatter plot exhibiting the associations between ARGs scores and TMB levels. (C) Kaplan-Meier analysis indicating the differential PFS survival outcomes between TMB-high and TMB-low groups.
Figure 1: The schematic flowchart of screening of ARGs signature and identification of ORC1 in PCa.

  Download full-size image
DOI: 10.7717/peerj.16850/fig-1
Figure 2: Single-cell RNA-seq (scRNA-seq) analysis identified top differentially expressed genes associated with androgen treatment.
(A and B) Quality control of scRNA-seq for cell sub-populations. The cells with poor quality were filtered out and we analyzed the positive associations between detected gene counts and sequencing depth. (C) The gene symbols with significant difference across cells were noted in red and we illustrated them by the characteristic variance diagram. (D) Cluster analysis of DEGs across three cell groups by heatmap, including 0 h: Control, 12 h−: R1881, 12 h+: R1881. (E) The principal component analysis (PCA), a linear dimensionality reduction method, was used to find the significantly available dimensions of data sets with the estimated P value, where we divided the cell groups into two categories. (F) Gene ontology (GO) analysis via Metascape platform identified the top significantly enriched crosstalk.  

  Download full-size image
DOI: 10.7717/peerj.16850/fig-2
Figure 3: Identification and construction of ARGs in PCa samples.
(A and B) We conducted the LASSO algorithmto identify the 10 prognostic genes in TCGA training cohort, where the optimal cutoff value was −4 and the minimum account of genes was 18. (C) The bubble plot exhibited the significantly differential expressions of 10 prognostic genes. (D) The receiver operating characteristic curve (ROC) analysis showing the predictive efficiency of 3-, 5- and 7-year in predicting PFS of patients. (E) Kaplan-Meier analysis showing the differential survival outcomes between ARGs-high and ARGs-low samples.

  Download full-size image
DOI: 10.7717/peerj.16850/fig-3
Figure 4: Correlation analysis between ARGs with clinical factors and prognostic significance of ARGs in PRAD.
(A–C) Kruskal-Wallis test showing that increasing ARGs-score correlated with higher T stages (P = 3.414e−15), higher positive rate of lymph nodes (P = 3.815e−08) and Gleason scores (P = 3.352e−23). (D) Univariate- and multi-variate Cox regression analysis exhibited the robustness of ARGs in PRAD samples. (E) The receiver operating characteristic curve (ROC) analysis showing the predictive efficiency of 3-, 5- and 7-year in predicting PFS of PSA levels. (F) Kaplan-Meier analysis showing the significance of PSA in PRAD samples. (G) DCA analysis was further used to evaluate the performance of ARGs model and PSA levels in predicting clinical outcomes of PRAD patients.

  Download full-size image
DOI: 10.7717/peerj.16850/fig-4
Figure 5: External validation of ARGs signature and clustering analysis across PRAD samples.
 (A) The ROC analysis of ARGs exhibiting its predictive efficiency of 3-, 5- and 7-year in predicting PFS. (B) Kaplan-Meier curves showing the differential survival outcomes in AGRs-high and ARGs-low samples in GSE116918. (C and D) The ROC analysis and Kaplan-Meier curves of ARGs in GSE70769. (E and F) The ROC analysis and Kaplan-Meier curves of ARGs in MSKCC-PRAD cohort. (G) Color‐coded heatmap corresponding to the consensus matrix for k = 2 obtained by consensus clustering. The color gradients from 0 to 1 represent the degree of consensus, with white corresponding to 0 and dark blue corresponding to 1. (H) Consensus among clusters for each category number k. (I) Delta area curve of consensus clustering indicating the relative change in area under the cumulative distribution function (CDF) curve for each category number k compared to k − 1. (J) PCA analysis indicated the identified two groups based on ARGs signature. (K) Kaplan-Meier analysis indicated the differential survival outcomes of two groups. (L) Gene set enrichment analysis (GSEA) exhibiting the enriched crosstalk between Cluster1 and Cluster2.

  Download full-size image
DOI: 10.7717/peerj.16850/fig-5
Figure 6: Experimental validations identified ORC1 as an essential target in PRAD.
(A) MTT assay was used to examine the viability of C4-2B and 22RV-1 after the administration of specific shRNAs of USP4S, HANK3, PPP1R1B, MYLB2, ORC1, INPP5B, EXO1, DYNLL1, CENPF and B4GALT1. (B) CCK8 assays (upper) indicating that ORC1 knockdown remarkably suppressed cell growth in C4-2B and 22RV-1 cells. The knockdown efficiency of ORC1 was confirmed by western blot below. (C) Boxplot showing the differential analysis of ORC1 mRNAs in tumor and normal samples of TCGA-PRAD cohort. (D) Boxplot showing the differential analysis of ORC1 mRNAs in tumor and normal samples in GSE94767. (E) Correlation analysis showing the associations between ORC1 levels and Gleason scores. (F and G) Kaplan-Meier analysis indicating the differential survival outcomes between high-ORC1 and low-ORC1 groups in TCGA-PRAD (F) and GSE116918 (G). ***p < 0.001.

  Download full-size image
DOI: 10.7717/peerj.16850/fig-6
Figure 7: AR activates ORC1 to sustain tumor progression and enzalutamide resistance in PRAD.
(A) The anchorage-independent growth of C4-2B and 22RV-1 cells in soft agar (scale bars = 200 µm, left). Quantification of the soft agar colony formation assay results (right). (B) Sphere formation assays revealing the self-renewal ability of ctrl and ORC1-overexpressing cells (scale bars = 200 µm, left). Quantification of the soft agar colony formation assay results (right). (C) Western-blotting assay indicating the regulations between AR and ORC1. (D) The scatter diagram indicating the positive correlations between AR and ORC1. (E) ChIP assays were utilized to determine the level of AR binding (left) and the enrichment of H3K27ac (right) at the promoter of ORC1 in AR deficiency or control PCa cells. (F) Cell proliferation determined in indicated cells with enzalumitide treatment. C4-2B-Parental and C4-2B-ENZR cells were transiently transfected as indicated. (G) Mice bearing C4-2B-ENZR xenografts were treated with contro DMSO and enzalutamide (10 mg/kg p.o.) for 4 weeks (n = 5/group). (H) The tumor volumes in indicated groups were detected the curves were generated. (I) The tumor weight in indicated groups were recorded and compared. ***p < 0.001.

  Download full-size image
DOI: 10.7717/peerj.16850/fig-7