Lineage-specific lncRNAs critically determine cross-species differences in tumors

Overview This repository contains all data and code supporting the analyses presented in the manuscript. The study develops a lineage-specific lncRNA (LS lncRNA)-centered comparative pan-cancer framework integrating 9,058 RNA-seq samples from 13 human tumors and their matched mouse counterparts to systematically investigate how LS lncRNAs drive transcriptional divergence, reshape cancer hallmark landscapes, and influence the tumor immune microenvironment (TIME) across species. The 13 tumor types analyzed include bladder, breast, kidney, liver, lung, pancreatic, prostate, ovarian, skin (melanoma), glioblastoma, glioma, leukemia, and lymphoma. The identification of LS lncRNAs is described in Lin et al. Intrinsically linked lineage specificity of transposable elements, lncRNA genes, and transcriptional regulation. bioRxiv 2026. The identified lncRNA DBDs and DBSs can be re-examined using the Fasim-LongTarget program based on expressed genes and transcripts in these cancer RNA-seq data. Information on raw data is described in Supplementary Table 1. Processed data (DEGs, TDGs, differentially expressed LS lncRNAs) are given in Supplementary Tables 2, 3, and 4. The DBS matrices of LS lncRNA-TDG in humans and mice for running the eGRAM program are given in Supplementary Tables 5 and 6. Modules generated by analyses based on the Spearman correlation and extended Spearman correlation are given in Supplementary Tables 7 and 8. All third-party programs used in data processing are publicly available (see papers cited in this manuscript). The data and programs in this deposition are primarily for re-examining the LS lncRNA-centered comparative pan-cancer analysis using eGRAM. Repository Structure .├── TranscriptionalAnalysis_code/├── TIME_analysis_code/├── eGRAM-Code/├── panData-geneExp_NX/├── panData-geneExp_logTPM/├── eGRAM-inputs/├── eGRAM-results/├── UMAP-plots/└── compare-results/ Contents Description 1. TranscriptionalAnalysis_codeScripts for transcriptomic data processing, normalization, cross-species/differential expression analysis, and module construction. 2. TIME_analysis_codeScripts for cross-species immune infiltration comparison and immune divergence module identification. 3. eGRAM-CodeSource code for this eGRAM version (note that this version is different from the one used for AD analysis in Lin et al. Intrinsically linked lineage specificity of transposable elements, lncRNA genes, and transcriptional regulation. bioRxiv 2026), which integrates lncRNA-DNA binding site (DBS) data with expression correlation to identify transcriptional regulatory modules. 4. panData-geneExp_NXPan-cancer normalized expression (NX) datasets for all 13 human and 13 mouse tumor types. NX values are z-scores computed from ComBat-corrected log2TPM matrices using the scikit-learn “preprocessing.StandardScaler”, normalized jointly across all 13 cancer types and 11 normal tissue types within each species. These files serve as the primary input for cross-species differential expression analysis, TDG/TCG classification, and ANOSIM/t-SNE quality control. 5. panData-geneExp_logTPMPan-cancer log2(TPM + 1) expression datasets (post-TMM normalization and ComBat batch correction) for all 13 human and 13 mouse tumor types. These matrices are used as input for eGRAM module identification, LS lncRNA expression quantification, and TIME deconvolution. Genes with TPM < 0.1 in > 80% of samples have been filtered. 6. eGRAM-inputsExpression profiles of DEGs, which are input files for the eGRAM program. 7. eGRAM-resultsOutput files from eGRAM analysis across all 13 human and 13 mouse tumors under three conditions (Normal, Cancer, Preserved). 8. UMAP-plotsUMAP projection outputs and visualization data for hallmark landscape and Jaccard distance landscape analyses. 9. compare-resultsResults from cross-species comparative analyses.