Single-cell analyses reveal a simple multi-gene transcriptomic signature with predictive power in prognosis and therapy effectiveness in triple-negative breast cancer
Re-analysis of single-cell RNA-sequencing data characterizes the diverse cell subtypes within the tumor and microenvironment of TNBC, supporting a luminal progenitor origin for the cancer and providing clues as to the factors involved in progression of the disease.
Abstract
Triple-negative breast cancer (TNBC) is an aggressive, heterogeneous form of breast cancer with limited specific therapy options, prevalent metastasis and frequent relapse. Re-analysis of single-cell RNA-sequencing data characterizes the diverse cell subtypes within the tumor and microenvironment of TNBC, supporting a luminal progenitor origin for the cancer and providing clues as to the factors involved in progression of the disease. The relative burdens of these subtypes can be deconvolved from bulk RNA-sequencing data, readily identifying the stem-like, mesenchymal and stromal cell subtypes significantly associated with poor survival and enrichment in metastasis. Importantly, these can be simplified to ten-gene signatures with comparable predictive power, notably in response to different therapeutic strategies, which are linked to relative burdens of different subtypes of stromal fibroblasts. The expression level of these signatures could provide a cheap means for selecting therapy strategies in personalized medicine.
Background/Objectives: Breast cancer (BC) is the most frequently diagnosed malignancy and a leading cause of cancer-related mortality in women worldwide. This disease is highly heterogeneous and dynamic, and chromosomal instability (CIN) plays a key role in the acquisition of these traits by generating genetic diversity that promotes tumor adaptation and influences therapeutic response and prognosis. Although several methods have been developed to quantify CIN, they are not readily applicable to human tumors and are limited in resolution, hindering a comprehensive understanding of intratumoral heterogeneity. In this study, we aimed to quantify CIN levels and clonal heterogeneity (CH) in HER2-positive (HER2+) and triple-negative (TNBC) breast cancer using single-cell RNA sequencing (scRNA-seq) data. Methods: We analyzed publicly available scRNA-seq data from HER2+ and TNBC tumors and non-malignant controls. CIN was scored at single-cell resolution using transcriptomic signatures, clonal heterogeneity was estimated from single-cell diversity metrics, and copy number alterations were inferred computationally. Differential expression and functional enrichment analyses were performed between cells with very low and extreme CIN levels, with key comparisons confirmed at the patient level. Results: Our analyses revealed pronounced intra- and intertumoral heterogeneity, with higher CIN levels in TNBC than in HER2+ and control samples. Genes differentially expressed in cells with extreme CIN values were mainly involved in cell division and related processes, and included candidate biomarkers not previously reported in this context. Our findings suggest a positive but statistically non-significant trend was observed between CIN and CH. Conclusions: Single-cell approaches such as scRNA-seq provide a powerful framework to elucidate CIN-related mechanisms and to identify potential biomarkers of BC aggressiveness and prognosis, supporting their further application in the study of intratumoral heterogeneity.
María Paula Meléndez-Flórez, N. Rangel, Milena Rondón-Lagos et al.· Biomedicines· 0 citations
A READ-derived response-associated gene signature for recurrence stratification and exploratory cross-cohort evaluation in additional colorectal cancer cohorts, while further exploring its association with treatment-response phenotypes.
Shuai Li, Jing-Xian Li, Xianyue Bu et al.· Human Cell· 0 citations
Highlights • Single-cell transcriptomics reveals epithelial heterogeneity shaping NSCLC immunity.• A 101-algorithm ML framework constructs a Prognostic and Immunotherapeutic Signature.• PIS stratifies NSCLC patients by prognosis and immune checkpoint blockade response.• ADAM12 inhibition enhances anti-PD-1 efficacy in preclinical mouse models.
A robust 17-gene ASIG-based prognostic signature that effectively stratified BRCA patients into high- and low-risk groups and served as an independent prognostic predictor is established, providing a robust tool for patient risk stratification and offering biological insights into senescence-driven microenvironmental remodeling.
Peng-Cheng Chen, Yindan Lin, Jingjia Li et al.· Genes· 0 citations
ABSTRACT Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, its progression and treatment heterogeneity are mainly influenced by driver gene and tumor micro-environment (TME) interactions. Nevertheless, the mechanisms of this process at the single-cell level remain unclear. This study integrated TCGA and multi-center single-cell transcriptome data to identify a 575 genes HCC-specific core set, developing a single-cell “oncogene scoring” system to quantify individual carcinogenic activity. This score is significantly elevated in malignant and proliferative T cells and is closely associated with metabolic reprogramming, aberrant cell‒cell communication, and immunosuppressive phenotypes. Based on these characteristics, we constructed a machine learning-based Random Survival Forest (RSF) prognostic model validated in multiple independent cohorts, which classifies patients into distinct risk subtypes. The high-risk group exhibits genomic instability, increased tumor stemness, and immune evasion, while the low-risk group was more sensitive to drugs such as sorafenib. This study highlights the potential pathways by which high oncogenic activity is associated with HCC progression, suggesting a profound link with single-cell metabolic‒immune crosstalk. The constructed RSF model offers a promising computational framework for risk stratification and provides hypothesis-generating insights that may inform future personalized treatment strategies for HCC patients.
Gastric cancer (GC) is the fourth leading cause of cancer-related mortality worldwide with poor clinical outcomes. The limited efficacy of current treatments necessitates research on deeper mechanistic insights and novel prognostic biomarkers. This study integrated single-cell RNA sequencing (scRNA-seq) with bulk RNA sequencing (RNA-seq) in GC using public datasets and literature-derived gene sets. Through differential expression profiling, Cox regression, and least absolute shrinkage and selection operator regression, we developed a risk stratification model and nomogram based on the five identified genes (
APOA1
,
SERPINE1
,
CD36
,
NPTX1
, and
IGFBP1
). High- and low-risk groups (classified based on risk scores) showed significant differences in immune infiltration, immune checkpoint expression, and chemotherapeutic sensitivity. The scRNA-seq analysis revealed distinct prognostic gene expression patterns in tumor endothelial cells and fibroblasts. Pseudotime analysis demonstrated dynamic expression levels of
CD36
and
SERPINE1
during cell differentiation states. These findings provide novel lymphangiogenesis-associated prognostic signatures for GC.
APOA1
,
SERPINE1
, and
CD36
were clinically validated;
NPTX1
and
IGFBP1
require further validation.
