RNA-seq-based copy number inference for retrospective genomic characterization of lung cancer transcriptomes
Abstract
A key limitation in cancer transcriptomics is the lack of accompanying genomic profiling such as whole-genome sequencing (WGS) or copy number alteration (CNA) data. Here we address this by showing that RNA-seq alone can be used to infer chromosomal aberrations and identify biologically meaningful patterns in lung cancer. Through a large-scale metaanalysis of publicly available RNA-seq datasets from non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), and matched controls, we reconstructed large scale CNA profiles and identified deletions in 3p, 9p, and 17p as the most frequent genomic events. Validation against paired WGS data confirmed a high degree of accuracy for RNA-seq-based inference. Our analyses revealed that while deletion-associated transcriptional heterogeneity exists, approximately 25% of differentially expressed genes were shared across all three deletion classes, indicating a conserved oncogenic program in lung cancer. Enrichment analysis linked these shared genes to pathways governing cell division, DNA replication, and extracellular matrix organization, while deletion-specific effects reflected disruption of tumor suppressor pathways, notably p53 signaling in 17p-deleted tumors, leading to deregulation of NOS2 and PLOD2. Integrating gene-level expression data, we identified both shared and deletion-specific biomarkers: TPX2 was consistently overexpressed across all deletion groups, while PTPRZ1 and CLDN9 were uniquely associated with del3p and del9p, respectively. Experimental validation in lung cancer cell lines confirmed these predictions, particularly the upregulation of CLDN9 in del9p carriers. By demonstrating that RNA-seq data can capture large-scale chromosomal events and reveal their transcriptional consequences, this study establishes an efficient framework for genomic inference and biomarker discovery, introducing CLDN9 as a novel, deletion-specific marker with potential prognostic and therapeutic value for 9p-deleted lung cancers.
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BibTeX
@article{gumerov2026rnaseqbased,
title = {{RNA-seq-based copy number inference for retrospective genomic characterization of lung cancer transcriptomes}},
author = {Ruslan Gumerov and Wangzhen He and Phong Luong and Filippo Dall’olio and Yegor Vassetzky and Anna Schwager},
journal = {Cancer Letters},
year = {2026},
month = jul,
doi = {10.1016/j.canlet.2026.218741},
url = {https://hal.science/hal-05760284v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - RNA-seq-based copy number inference for retrospective genomic characterization of lung cancer transcriptomes
AU - Ruslan Gumerov
AU - Wangzhen He
AU - Phong Luong
AU - Filippo Dall’olio
AU - Yegor Vassetzky
AU - Anna Schwager
PY - 2026
DA - 2026/07/17
JO - Cancer Letters
DO - 10.1016/j.canlet.2026.218741
UR - https://hal.science/hal-05760284v1
AB - A key limitation in cancer transcriptomics is the lack of accompanying genomic profiling such as whole-genome sequencing (WGS) or copy number alteration (CNA) data. Here we address this by showing that RNA-seq alone can be used to infer chromosomal aberrations and identify biologically meaningful patterns in lung cancer. Through a large-scale metaanalysis of publicly available RNA-seq datasets from non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), and matched controls, we reconstructed large scale CNA profiles and identified deletions in 3p, 9p, and 17p as the most frequent genomic events. Validation against paired WGS data confirmed a high degree of accuracy for RNA-seq-based inference. Our analyses revealed that while deletion-associated transcriptional heterogeneity exists, approximately 25% of differentially expressed genes were shared across all three deletion classes, indicating a conserved oncogenic program in lung cancer. Enrichment analysis linked these shared genes to pathways governing cell division, DNA replication, and extracellular matrix organization, while deletion-specific effects reflected disruption of tumor suppressor pathways, notably p53 signaling in 17p-deleted tumors, leading to deregulation of NOS2 and PLOD2. Integrating gene-level expression data, we identified both shared and deletion-specific biomarkers: TPX2 was consistently overexpressed across all deletion groups, while PTPRZ1 and CLDN9 were uniquely associated with del3p and del9p, respectively. Experimental validation in lung cancer cell lines confirmed these predictions, particularly the upregulation of CLDN9 in del9p carriers. By demonstrating that RNA-seq data can capture large-scale chromosomal events and reveal their transcriptional consequences, this study establishes an efficient framework for genomic inference and biomarker discovery, introducing CLDN9 as a novel, deletion-specific marker with potential prognostic and therapeutic value for 9p-deleted lung cancers.
SN - 0304-3835
ER -