bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.08.12.607523

Unraveling lncRNA Diversity at a Single Cell Resolution and in a Spatial Context across Different Cancer Types

Prakrithi, P.·Vo, T.·Vu, H.·Xiong, A.·Nguyen, L. T.·Newman, A.·Whitehall, V.·Gonzalez Cruz, J. L.·Gupta, I.·Nguyen, Q.
View original source ↗Export metadata ↓

Abstract

Long non-coding RNAs (lncRNAs) play pivotal roles in gene regulation and disease, including cancer. Overcoming the limitations of lncRNA analysis with bulk data, we analyzed single-cell and spatial transcriptomics data to uncover 354937 novel lncRNAs and their functions across 13 cancer types. LncRNA functions were assessed by identifying their cell-type specificity and distinct spatial distributions across different tissue regions. First, lncRNAs were computationally validated by comparing to existing databases, and experimentally validated using spatial long read sequencing methods. Further, genome-wide computation of spatial-autocorrelation identified coexpression of lncRNAs with cancer-associated protein coding genes across the tissue. Additionally, genomic co-localization of lncRNAs with regulatory features and disease-associated genetic variants suggest possible functional association. The identified lncRNAs were analyzed for responses to immunotherapy and prognostic value, revealing cancer-outcome associated lncRNAs. We have made this novel resource available as an open website SPanC-Lnc hosted on AWS cloud to serve as a pan-cancer atlas of single cell- and spatially-resolved lncRNAs. These can complement established biomarkers because they reflect the unique characteristics of specific cell populations within tumors, offering new insights into disease progression and treatment response.

Source connections

Explore related subjects

cancer biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Prakrithi, P., Vo, T., Vu, H., Xiong, A., Nguyen, L. T., Newman, A., Whitehall, V., Gonzalez Cruz, J. L., Gupta, I., Nguyen, Q.. 2024-08-12. Unraveling lncRNA Diversity at a Single Cell Resolution and in a Spatial Context across Different Cancer Types. https://doi.org/10.1101/2024.08.12.607523

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

An Oxidative Stress-Associated Seven-Gene Prognostic Signature in Lung Adenocarcinoma: Integrative Transcriptomic Analysis Across Public Cohorts

Lung adenocarcinoma is molecularly heterogeneous, and oxidative-stress programs can support either tumor restraint or tumor adaptation depending on cellular context. This study integrated public lung adenocarcinoma transcriptomic cohorts to identify oxidative-stress-associated expression features and evaluate their prognostic relevance. Expression profiles from The Cancer Genome Atlas, Genotype-Tissue Expression project, and GEO series GSE31210, GSE40791, and GSE30219 were analyzed. Differential expression, weighted gene co-expression network analysis, functional enrichment, univariable Cox regression, and least absolute shrinkage and selection operator Cox modeling were combined to derive a risk signature. Immune-cell enrichment, gene set enrichment analysis, gene set variation analysis, and pan-cancer analyses were used for biological characterization. A total of 1,305 genes differed between tumor and control samples, including 498 upregulated and 807 downregulated genes. Intersection of differentially expressed genes, the oxidative-stress-associated co-expression module, and the oxidative-stress gene set yielded 44 genes enriched in responses to reactive oxygen species and hydrogen peroxide, antioxidant and peroxidase activities, focal adhesion, Rap1 signaling, and PI3K-Akt signaling. A seven-gene signature comprising FBLN5, HBB, FYN, HGF, TFAP2A, PLIN5, and F2RL1 stratified the 523-sample training cohort and the 207-sample internal validation cohort into groups with different overall survival. Time-dependent areas under the receiver operating characteristic curve at 1, 3, and 5 years were 0.677, 0.622, and 0.649 in training and 0.613, 0.691, and 0.706 in internal validation. In the 85-case GSE30219 external cohort, corresponding values were 0.588, 0.661, and 0.631; survival separation followed the expected direction but did not reach statistical significance (log-rank P = 0.100). Seventeen immune-cell signatures differed between risk groups, while high-risk tumors were enriched for cell-cycle, DNA-replication, mismatch-repair, glycolytic, E2F, G2M-checkpoint, MYC-target, and mTORC1-related programs. The signature therefore captures reproducible oxidative-stress-associated transcriptional variation with moderate prognostic discrimination. Its clinical utility requires prospective evaluation, complete clinical adjustment, and experimental validation.

cancer biology↗
bioRxiv2026

Glucocorticoids reprogram human AML leukemic stem cells to promote elimination through differentiation and apoptosis

Acute myeloid leukemia (AML) is sustained by leukemic stem cells (LSCs) that can evade standard therapies and drive relapse. Targeting LSC-specific vulnerabilities is therefore essential for durable remission. Here we demonstrate that glucocorticoids (GCs) induce potent depletion of AML LSCs by promoting terminal differentiation and apoptosis. This effect is observable within 24 hours and is conserved across multiple LSC-enriched models and primary patient samples. Mechanistically, we establish that GC targeting of LSCs is mediated through the glucocorticoid receptor (NR3C1), with higher receptor binding affinity correlating with greater anti-LSC activity. We performed structure activity relationship (SAR) modeling of 24 corticosteroids and identified key features, including bulky D-ring substituents, associated with enhanced anti-LSC efficacy. Bulk and single-cell transcriptomic data revealed that GC treatment of LSCs suppresses NF-{kappa}B inflammatory signaling and disrupts stemness and quiescence programs while inducing transcriptional signatures associated with transient proliferation, metabolic stress, and terminal differentiation. Notably, GC sensitivity was associated with the expression of pre-existing inflammatory or extracellular matrix (ECM) signatures. Finally, we found that FLT3 ligand (FLT3L) is required for GC-induced proliferation of CD34- blasts but not for LSC depletion, suggesting that FLT3L levels may serve as a biomarker for blast expansion in patients receiving GC therapy. These findings support the clinical development of GC-based therapies in AML and provide mechanistic insights into how GCs target inflammatory and metabolic programs required for LSC survival.

cancer biology↗
bioRxiv2026

HrasG12V induces follicular thyroid cancer with attenuated MAPK activation and increased latency compared to KrasG12D

RAS mutations are found in nearly 50% of follicular thyroid cancers (FTCs), frequently accompanied by secondary mutations in the P13K/AKT pathway as tumors advance to more poorly differentiated states. To examine the role that oncogenic Hras plays in thyroid tumor initiation and progression, we developed murine models with thyroid-specific expression of HrasG12V combined with heterozygous or homozygous loss of Pten. Loss of Pten cooperated with HrasG12V in a dose-dependent manner to induce the development of follicular thyroid carcinoma and poorly-differentiated thyroid carcinoma. Histopathology of HrasG12V/PtenHom tumors closely resembled those from the established KrasG12D/PtenHom model, but tumor onset was significantly delayed in HrasG12V/PtenHom mice. At three weeks of age, downregulation of MAPK pathway inhibitors was observed in KrasG12D/PtenHom thyroids, accompanied by increased MAPK pathway activation compared to HrasG12V/PtenHom mice. Furthermore, amplification of oncogenic Ras was found in HrasG12V tumors and cell lines, while allelic balance was maintained in KrasG12D models. These studies demonstrate clear phenotypic differences between mutant Hras and Kras in the thyroid and suggest that delayed MAPK activation may mediate the increased tumor latency observed in the HrasG12V/PtenHom model.

cancer biology↗