RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Supplemental Information
(A) The volcano map of DEGs between culster2 and culster1. (B) The heatmap of DEGs between culster2 and culster1. (C) The top 8 BP entries, top 8 CC entries, and 7 MF entries were enriched by DEGs between culster2 and culster1. (D) The KEGG pathways were enriched by DEGs between cluster2 and cluster1.

(A) Correlation between ACTA2 and immunoinhibitors. (B) Correlation between APOD and immunoinhibitors. (C) Correlation between APOD and immunoinhibitors. (D) Correlation between SAPCD2 and immunoinhibitors.

(A) Correlation between ACTA2 and immunostimulators. (B) Correlation between APOD and immunostimulators. (C) Correlation between APOD and immunostimulators. (D) Correlation between SAPCD2 and immunostimulators.

(A) The PPI network of prognostic genes. (B) The relevance of prognostic genes and DERMGs. ** p < 0.01.

(A) ACTA2 (B) SAPCD2 (C) PDK4 (D) APOD * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.
Figure 1: Differential RNA methylation regulatory gene screening.
(A) Volcano plot of DEGs between STAD and normal samples. (B) The intersection of DEGs and RNA methylation regulation genes. (C) The violin map of the expression of DERMGs association with M1A and M5C modify. (D) The expression heat map of DERMGs. (E) The violin map of the expression of DERMGs association with m6A and M7G modify. (F) The expression heat map of DERMGs association with m6A and M7G modify. (G) The GO terms enriched in DERMGs. Four asterisks (****) represent p < 0.0001.

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DOI: 10.7717/peerj.16951/fig-1
Figure 2: Identification of DERMGs-related subtypes in STAD.
(A) Forest plot of univariate Cox of four DERMGs associated with overall survival (OS) in STAD patients. (B) Relative change in area under CDF area for k = 2 to 6. (C) The cumulative distribution functions. (CDF) for k = 2 to 6. (D) Tracking plot for k = 2 to 6. (E) Consensus score matrix of all samples when k = 2. (F) t-SNE dimension reduction analysis verifies the reliability of the clustering results. (G) Kaplan-Meier survival curve based on two clusters. (H) The expression heat map of four DERMGs associated with OS of STAD patients in two clusters and different clinicopathological subgroups.

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Figure 3: Construction and validation of prognostic risk profiles in STAD patients with four survival related genes.
(A) Forest plot of univariate Cox of five genes associated with OS of STAD patients. (B and C) Four prognostic genes were selected using LASSO regression. (D–F) Kaplan-Meier survival curve of high- and low-risk group of training set, testing set, and validation set. (G–I) The 1, 3, and 5 years ROC curve verifies the specificity and sensitivity of risk feature prediction in training set, testing set, and validation set. (J–L) The risk curve in training set, testing set, and validation set according to the risk score. The risk curve is composed of the upper, middle and lower parts, and the abscissa is the patient samples sorted according to the risk score. In (J–L), the upper figure is consistent with the abscissa in the middle figure, and the risk score increases successively from left to right. The ordinate is risk score and time to survival, and the dotted line is the median risk score and the corresponding number of patients. In the (J–L) figures, the lower figure is a heat map of model gene expression in the high- and low-risk group.

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Figure 4: Association between clinicopathological features and prognostic risk scores.
Differences of risk scores in clinical characteristics among different groups, including age (A), gender (B), pathologic M stage (C), pathologic N stage (D), pathologic stage (E) and pathologic T stage (F).

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Figure 5: Independent prognostic analysis.
(A) The forest plot of univariate Cox analysis to select the features related to patients’ survival. (B) The forest plot of multivariate Cox analysis to select the independent prognostic factors. (C) The nomogram includes independent prognostic factors to predict the survival of patients. (D) The 1, 3, and 5 years of calibration curve of nomogram.

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Figure 6: GSEA results for the high- and low-risk groups.
(A) Top 10 GO entries in GSEA analysis between high- and low-risk groups. (B) Top 10 KEGG pathways in GSEA analysis between high- and low-risk groups.

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Figure 7: Correlation between prognostic genes and TME in STAD and expression of biomarkers in different cell clusters.
(A and B) The cell types and their distribution in the GSE134520 dataset. (C–F) Using the TISCH database, the correlation between the expression of APOD, ACTA2, PDK4 and SAPCD2 and TME in STAD tissues was analyzed. (G–J) The distribution of APOD, ACTA2, PDK4 and SAPCD2 in different cell types was analyzed using single-cell resolution in the GSE134520 dataset. STAD, gastric adenocarcinoma; TME, tumor microenvironment; TISCH, tumor immune single-cell center.

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Figure 8: The mutation analysis of prognostic geness.
(A) Summary of alterations in prognostic genes in STAD through cBioPortal. (B) The distribution of mutation sites of prognostic genes. (C) Correlation between the prognostic genes and copy number variation.

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DOI: 10.7717/peerj.16951/fig-8
RNA methylated genes enrolled in this study.
The reaction system of qRT-PCR validation.
The amplification procedures of PCR system.
The sequences of the primers for qPCR.