Jul 2026· Cancer Drug Resistance· Vol 9· 0 citations· 40 references
Medicine
TL;DR
A gene signature associated with epithelial-mesenchymal transition that integrates TME dynamics, prognosis prediction, and drug resistance assessment in CRC holds significant value for predicting clinical prognosis, immunotherapy responsiveness, and chemotherapy sensitivity in CRC.
Abstract
Aim: Colorectal cancer (CRC) ranks among the most prevalent malignancies across the globe, with treatment resistance often closely linked to the complexity of the tumor microenvironment (TME). This study aims to establish a gene signature associated with epithelial-mesenchymal transition (EMT) that integrates TME dynamics, prognosis prediction, and drug resistance assessment in CRC. Methods: We employed the Cancer Genome Atlas (TCGA) resource and bulk RNA-sequencing profiles linked to EMT to identify common differentially expressed genes (DEGs). An eight-gene signature was constructed using multivariable Cox regression analysis. The correlations of risk groups (scores) with overall survival, biological characteristics, and drug sensitivity were analyzed in CRC patients. Results: Patients in the high-risk group exhibited significantly worse clinical outcomes than those in the low-risk group. Moreover, a lower risk score was significantly correlated with increased responsiveness to both immune checkpoint inhibitors and 5-fluorouracil in CRC patients. Additionally, experiments in vitro and in vivo confirmed that FABP4 functions as an oncogene in CRC by facilitating cell growth, migration, and chemotherapy resistance. Conclusion: The EMT-associated gene signature holds significant value for predicting clinical prognosis, immunotherapy responsiveness, and chemotherapy sensitivity in CRC.
A seven-gene immune-related prognostic signature that, combined with clinicopathological parameters, provides a robust tool for individualized survival prediction and may guide precision management in CRC patients is developed and validated.
A dual-gene model based on neutrophil heterogeneity demonstrated strong predictive performance and functioned as an independent prognostic indicator, and distinct immune and prognostic differences were identified among molecular subtypes.
Colorectal cancer (CRC) is recognized as the third most common malignant tumor globally. Matrix stiffness (MS) has been associated with the progression of various cancer types and is often abnormally high in tumor tissues. Regardless, the precise molecular pathways through which MS influences CRC initiation and disease advancement remain underexplored. This research was designed to investigate MS-related prognostic genes and their potential biological relevance in CRC progression. Transcriptomic datasets alongside single-cell sequencing information were obtained from open-access repositories for the current investigation. Univariate Cox regression analysis, combined with other analytical methods, were used to identify prognosis-associated genes. Based on these findings, a risk prediction model was constructed and its validity was confirmed using separate training and validation cohorts. Computational biology techniques were then applied to delineate the characteristics of the tumor immune landscape and assess therapeutic agent responsiveness between different risk stratifications. Key cell populations were characterized using single-cell sequencing, and the expression patterns of prognostic genes were examined across distinct cell subtypes. Moreover, the expression levels of these prognostic markers in the experimental specimens were quantified using RT-qPCR. Four prognostic genes were identified—
SIX4
,
PCOLCE2
,
TIMP1
, and
TNNT1
—all of which functioned as risk factors (HR > 1,
p
< 0.05). The prognostic risk model effectively predicted outcomes in patients with CRC. Furthermore, TIMP1 exhibited a significantly positive correlation with natural killer T cells (correlation coefficient [cor] = 0.55,
p
= 3.97e−42). A total of 95 drugs showed exploratory differences in computationally predicted sensitivity between risk groups, with Dasatinib being a representative example (adjusted
p
= 2.38e−15). Epithelial cells were determined as the key cell population, and the expression dynamics of all prognostic genes exhibited broadly consistent trends throughout epithelial cell differentiation. Finally, RT-qPCR analysis preliminarily validated the bioinformatics-predicted expression patterns.
SIX4
,
PCOLCE2
,
TIMP1
, and
TNNT1
were identified as MSRG-informed prognostic genes, providing potential clues for future studies of CRC prognosis and MS-related tumor biology.
Background: In recent decades, the incidence of colorectal cancer (CRC) has been rising worldwide. CRC ranks second in cancer-related mortality. The identification of reliable biomarkers for early diagnosis and prognosis prediction, along with a deeper understanding of the underlying molecular events, holds substantial promise for improving patient outcomes. The tissue inhibitor of the metalloproteinase 1 (TIMP1) gene is overexpressed in various gastrointestinal malignancies and contributes to tumor progression. However, its role in regulating the CRC tumor immune microenvironment (TIME) and its potential as a clinically actionable prognostic biomarker remain unclear. Methods: To probe how TIMP1 acts as a prognosis-related candidate biomarker in colorectal carcinoma, TCGA-derived datasets were adopted to conduct Kaplan–Meier survival assessment. We also investigated the connection between the expression abundance of TIMP1 and the infiltration of immune populations and intratumoral lymphocytes; furthermore, immune checkpoint-related genes were systematically assessed across multiple tumor types via the TISIDB and TIMER2.0 platforms, with particular emphasis on CRC. We adopted the ESTIMATE scoring system to figure out how TIMP1 gene expression correlates with the phenotypic properties of the colorectal-cancer TIME. We relied on the limma toolkit for the screening of differential transcripts from high-TIMP1 and low-TIMP1 cohorts. Enrichment assessments covering Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes entries were then carried out to predict the potential biological pathways associated with TIMP1. We constructed the protein–protein interaction map for TIMP1-interacting partners via the STRING repository. To further explore TIMP1-correlated genes, we performed Venn diagram intersection analysis combined with Spearman’s correlation test. Finally, quantitative reverse-transcription PCR was then implemented to detect TIMP1 messenger-RNA abundance inside the RKO colorectal carcinoma cell line as well as normal colonic epithelial CCD-18Co cells, which offered in vitro experimental verification for our bioinformatic outcomes. Results: According to outcome data, TIMP1 transcripts were markedly up-regulated in CRC specimens and cell lines relative to normal samples. Elevated TIMP1 expression served as a poor-prognosis indicator for overall survival (hazard ratio [HR] = 0.43, 95% confidence interval [CI] = 0.29–0.64, p < 0.001) and disease-specific survival (HR = 0.39, 95% CI = 0.22–0.68, p = 0.001) among colorectal-carcinoma patients. TIMP1-high and TIMP1-low groups exhibited notable differences in immune cell infiltration (CD8+ T, macrophage, mast, neutrophil, B, monocyte, dendritic, and CD4+ T cells). TIMP1 expression was also significantly correlated with tumor-infiltrating lymphocytes, key immune checkpoint genes (e.g., CD274 [PD-L1] and CTLA4), and immunomodulatory chemokines (e.g., CCL3 and CCL5). Twelve TIMP1-interacting DEGs were selected: COL5A1, FN1, PRG4, and a cluster of nine MMPs (MMP1/2/3/7/8/9/11/13/14), all of which showed significant positive correlations with TIMP1 (r = 0.31–0.63, all p < 0.001). Conclusions: TIMP1 expression correlates with features of the tumor immune microenvironment and extracellular matrix remodeling in CRC, suggesting that TIMP1 shows potential as a candidate biomarker. However, its potential as a therapeutic target warrants further experimental investigation.
Yunyi Xie, Jun Li, Zuwei Yan et al.· Genes· 0 citations
Immunotherapy has emerged as a pivotal approach in cancer treatment, yet its efficacy is influenced by the interactions within the tumor microenvironment (TME). Angiogenesis, the formation of new blood vessels, is a hallmark feature of the TME, particularly in aggressive malignancies such as pancreatic ductal adenocarcinoma (PDAC). This study aims to elucidate angiogenesis patterns in PDAC and investigate their associations with clinical characteristics, TME features, and the response to immunotherapy. By analyzing 40 angiogenesis‐related genes, 28 angiogenesis‐associated immune genes (AIGs) were identified, enabling classification of PDAC patients into two subclusters with distinct clinical and TME profiles. Afterwards, we constructed an AIGScore risk model using least absolute shrinkage and selection operator regression in PDAC, and its reliable predictive ability was confirmed in both training and validation sets. Functional analyses suggested that a high AIGScore may be associated with worse prognosis, reduced tumor immune cycle activity, elevated immune and stromal scores, and diminished response to anti‐PD‐1 immunotherapy. Pan‐cancer analyses further indicated a potential correlation between the seven AIGs and tumor progression, as well as patient outcomes across other malignancies. In summary, this study proposes a novel AIGScore model with significant prognostic utility for PDAC. The findings may provide insights into TME characteristics and offer a potential framework for optimizing immunotherapeutic strategies in patients with PDAC.
Qing Chang, Qian Wang, Xiumei Jiang et al.· Visual Information Expert Wo...· 0 citations
A six-gene-fibrosis-based prognostic model based on six genes stratifies survival risk and correlates with immune features and drug sensitivity, but provides a preliminary framework requiring prospective clinical validation.
Yanyan Qiu, Cui Lv, Shu-Bo Ding· Clinical and Translational O...· 0 citations
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