Targeting the immune privilege of tumor-initiating cells to enhance cancer immunotherapy

Chaperonin containing TCP1 subunit 5 as a novel pan-cancer prognostic biomarker for tumor stemness and immunotherapy response: insights from multi-omics data, integrated machine learning, and experimental validation

BACKGROUND

Chaperonin containing TCP1 subunit 5 (CCT5), a vital component of the molecular chaperonin complex, has been implicated in tumorigenesis, cancer stemness maintenance, and therapeutic resistance. Nevertheless, its comprehensive roles in pan-cancer progression, underlying biological functions, and potential as a predictor of immunotherapy response remains poorly understood.

METHODS

We performed a comprehensive multi-omics pan-cancer analysis of CCT5 across 33 cancer types, integrating bulk RNA-seq, single-cell RNA-seq (scRNA-seq), and spatial transcriptomics data. CCT5 expression patterns, prognostic relevance, stemness association, and immune microenvironment relationships were evaluated. A novel CCT5-based signature (CCT5.Sig) was developed using machine learning on 23 immune checkpoint blockade (ICB) cohorts (n = 1394) spanning eight cancer types. Model performance was assessed using AUC metrics and survival analyses.

RESULTS

CCT5 was significantly overexpressed in tumor tissues and primarily localized to malignant and cycling cells. High CCT5 expression correlated with poor prognosis in multiple cancers and was enriched in oncogenic, cell cycle, and DNA damage repair pathways. CCT5 expression was positively associated with mRNAsi, mDNAsi, and CytoTRACE scores, indicating a role in stemness maintenance. Furthermore, CCT5-high tumors exhibited immune-cold phenotypes, with reduced TILs and CD8⁺ T cell activity. The CCT5.Sig model, based on genes co-expressed with CCT5, achieved superior predictive accuracy for ICB response (AUC = 0.82 in validation and 0.76 in independent testing), outperforming existing pan-cancer signatures.

CONCLUSION

This study reveals the multifaceted oncogenic roles of CCT5 and highlights its potential as a pan-cancer biomarker for prognosis and immunotherapy response. The machine learning-derived CCT5.Sig model provides a robust tool for patient stratification and may inform personalized immunotherapy strategies.

Decoding per- and polyfluoroalkyl substances (PFAS) in hepatocellular carcinoma: a multi-omics and computational toxicology approach

BACKGROUND

Per- and polyfluoroalkyl substances (PFAS), particularly perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), are synthetic chemicals known for their widespread use and environmental persistence. These compounds have been increasingly linked to hepatotoxicity and the development of hepatocellular carcinoma (HCC). However, the molecular mechanisms by which PFAS contribute to HCC remain underexplored.

METHODS

This study employs a multi-omics approach that combines network toxicology, integrated machine learning, single-cell RNA sequencing, spatial transcriptomics, experimental validation, and molecular docking simulations to uncover the mechanisms through which PFAS exposure drives HCC. We analyzed publicly available transcriptomic data from several HCC cohorts and used differential gene expression analysis to identify targets associated with both PFAS exposure and HCC. We constructed a protein-protein interaction (PPI) network and a survival risk model, the PFAS-related HCC signature (PFASRHSig), based on integrated machine learning to identify prognostic biomarkers, with the goal of identifying core targets of PFAS in HCC progression and prognosis. RT-qPCR and immunohistochemical (IHC) staining were used to validate the expression levels of the targets in both tumor and normal tissues. Molecular docking simulations were conducted to assess the binding affinities between PFAS compounds and selected target proteins.

RESULTS

Functional enrichment studies revealed that PFAS targets were associated with metabolic signaling pathways, which are actively involved in lipid, glucose, drug metabolism, etc. Through integrated machine learning and PPI network analysis, we identified six genes, APOA1, ESR1, IGF1, PPARGC1A, SERPINE1, and PON1, that serve as core targets of PFAS in both HCC progression and prognosis. These targets were further validated via bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics, which revealed differential expression patterns across various cell types in the HCC tumor microenvironment. The results of RT-qPCR and IHC staining were consistent with the in silico findings. Molecular docking simulations revealed strong binding affinities between PFAS compounds and these core targets, supporting their potential roles in PFAS-induced hepatocarcinogenesis.

CONCLUSIONS

Our study highlights key molecular targets and pathways involved in PFAS-induced liver carcinogenesis and proposes a robust survival risk model (PFASRHSig) for HCC. These findings provide new insights into PFAS toxicity mechanisms and offer potential therapeutic targets for mitigating the health risks associated with PFAS exposure. Collectively, our findings help in advancing clinical applications by providing insights into disease mechanisms and potential therapeutic interventions.

METTL1-driven nucleotide metabolism reprograms the immune microenvironment in hepatocellular carcinoma: a multi-omics approach for prognostic biomarker discovery

Background

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality worldwide, partly due to an incomplete understanding of the metabolic and immune dysregulation driving its progression. Here, we uncover a novel role of METTL1 in driving nucleotide metabolism reprogramming, which significantly modulates the tumor immune microenvironment.

Methods

Utilizing an integrated multi-omics approach, we analyzed nucleotide metabolism-related genes derived from TCGA, GEO, and ICGC datasets. Non-negative matrix factorization (NMF) clustering stratified HCC patients into distinct subgroups with varied clinical features. Weighted Gene Co-expression Network Analysis (WGCNA) identified hub genes that were subsequently used to construct robust prognostic models via multiple machine learning algorithms. These computational findings were validated through in vitro experiments, immune infiltration assessments, and single-cell RNA sequencing analysis.

Results

Our analyses demonstrate that METTL1 is markedly upregulated in HCC, driving a reprogramming of nucleotide metabolism that modulates the expression of key immune checkpoints, including PD-L1 and CTLA-4. This regulation is associated with an immunosuppressive tumor microenvironment, reduced infiltration of activated T cells, and poorer clinical outcomes. Moreover, the prognostic model integrating METTL1 expression and immune checkpoint profiles shows strong predictive performance across independent cohorts, highlighting its potential clinical utility.

Conclusion

This study highlights the innovative role of METTL1-driven nucleotide metabolism reprogramming in reshaping the immune microenvironment of HCC. The findings provide novel insights into HCC pathogenesis and pave the way for developing personalized therapeutic strategies based on targeting METTL1 and its associated metabolic pathways.

Depletion of tumor-derived CXCL5 improves T cell infiltration and anti-PD-1 therapy response in an obese model of pancreatic cancer

BACKGROUND

CXCR1/2 inhibitors are being implemented with immunotherapies in PDAC clinical trials. CXC-ligands are a family of cytokines responsible for stimulating these receptors; while typically secreted by activated immune cells, fibroblasts, and even adipocytes, they are also secreted by immune-evasive cancer cells. CXC-ligand release is known to occur in response to inflammatory stimuli. Adipose tissue is an endocrine organ and a source of inflammatory signaling peptides. Importantly, adipose-derived cytokines and chemokines are implicated as potential drivers of tumor cell immune evasion; cumulatively, these findings suggest that targeting CXC-ligands may be beneficial in the context of obesity.

METHODS

RNA-sequencing of human PDAC cell lines was used to assess influences of adipose conditioned media on the cancer cell transcriptome. The adipose-induced secretome of PDAC cells was validated with ELISA for induction of CXCL5 secretion. Human tissue data from CPTAC was used to correlate IL-1β and TNF expression with both CXCL5 mRNA and protein levels. CRISPR-Cas9 was used to knockout CXCL5 from a murine PDAC KPC cell line to assess orthotopic tumor studies in syngeneic, diet-induced obese mice. Flow cytometry and immunohistochemistry were used to compare the immune profiles between tumors with or without CXCL5. Mice-bearing CXCL5 competent or deficient tumors were monitored for differential tumor size in response to anti-PD-1 immune checkpoint blockade therapy.

RESULTS

Human adipose tissue conditioned media stimulates CXCL5 secretion from PDAC cells via either IL-1β or TNF; neutralization of both is required to significantly block the release of CXCL5 from tumor cells. Ablation of CXCL5 from tumors promoted an enriched immune phenotype with an unanticipatedly increased number of exhausted CD8 T cells. Application of anti-PD-1 treatment to control tumors failed to alter tumor growth, yet treatment of CXCL5-deficient tumors showed response by significantly diminished tumor mass.

CONCLUSIONS

In summary, our findings show that both TNF and IL-1β can stimulate CXCL5 release from PDAC cells in vitro, which correlates with expression in patient data. CXCL5 depletion in vivo alone is sufficient to promote T cell infiltration into tumors, increasing efficacy and requiring checkpoint blockade inhibition to alleviate tumor burden.

Tumor-lymph cross-plane projection reveals spatial relationship features: a ResNet-CBAM model for prognostic prediction in esophageal cancer

Background

Prognostic models for esophageal cancer based on contrast-enhanced chest CT can aid thoracic surgeons in developing personalized treatment plans to optimize patient outcomes. However, the extensive lymphatic drainage and early lymph node metastasis of the esophagus present significant challenges in extracting and analyzing meaningful lymph node characteristics. Previous studies have primarily focused on tumor and lymph node features separately, overlooking spatial correlations such as position, direction, and volumetric ratio.

Methods

A total of 285 patients who underwent radical resection surgery at Fujian Provincial Hospital from 2018 to 2022 were retrospectively analyzed. This study introduced a tumor-lymph node projection plane, created by projecting lymph node ROIs onto the tumor ROI plane. A ResNet-CBAM model, integrating a residual convolutional neural network with a CBAM attention module, was employed for feature extraction and survival prediction. The PJ group utilized tumor-lymph node projection planes as training data, while the TM and ZC groups utilized tumor ROIs and concatenated images of tumor and lymph node ROIs, respectively, as controls. Additional comparisons were made with traditional machine learning models (support vector machines, logistic regression, and K-nearest neighbors). Survival outcomes (median, 1-year, 3-year, 5-year) were used as target labels to evaluate model performance in distinguishing high-risk patients and predicting both short- and long-term survival.

Results

In the PJ group, the ResNet-CBAM model achieved accuracy rates of 0.766, 0.981, 0.883, and 0.778 for predicting median, 1-year, 3-year, and 5-year survival, respectively. Its corresponding AUC values for 1-, 3-, and 5-year survival were 0.992, 0.913, and 0.835. Kaplan-Meier survival analysis revealed significant differences between high- and low-risk groups identified by the model. The ResNet-CBAM model outperformed those in the TM and ZC groups in distinguishing high-risk patients and predicting both short- and long-term survival. Compared to machine learning models, it demonstrated superior performance in long-term survival prediction.

Conclusion

The ResNet-CBAM model trained on tumor-lymph projection planes effectively distinguished high-risk esophageal cancer patients and outperformed traditional models in predicting survival outcomes. By capturing spatial relationships between tumors and lymph nodes, it demonstrated enhanced predictive efficiency.

Reprogramming tumor-associated macrophages in gastric cancer: a pathway to enhanced immunotherapy

Gastric cancer (GC) remains a significant global health concern due to its poor prognosis and limited therapeutic options, particularly in advanced stages. Tumor microenvironment (TME), particularly tumor-associated macrophages (TAMs), plays a key role in tumor progression, immune evasion, and therapy resistance. TAMs exhibit plasticity, shifting between pro-inflammatory M1 and immunosuppressive M2 phenotypes, with the latter predominating in GC and contributing to poor outcomes. Recent therapeutic advancements focus on targeting TAMs, including inhibiting M2 polarization, reprogramming TAMs to M1 phenotypes, and combining TAM-targeted approaches with immune checkpoint inhibitors. Innovations in nanotechnology, metabolic reprogramming, and targeting key pathways such as interleukin-6 and C-C motif ligand 2/C-C motif chemokine receptor 2 further enhance these strategies. However, challenges remain, including the spatial and functional heterogeneity of TAMs within the TME and the need for selective targeting to avoid disrupting immune homeostasis. Ongoing research on TAM origins, functions, and interactions within the TME is crucial for developing precise and effective therapies. These advances hold promise not only for improving outcomes in GC but also for addressing other cancers with similarly complex microenvironments.

Cancer stem cells and niches: challenges in immunotherapy resistance

Cancer stem cells (CSCs) are central to tumor progression, metastasis, immune evasion, and therapeutic resistance. Characterized by remarkable self-renewal and adaptability, CSCs can transition dynamically between stem-like and differentiated states in response to external stimuli, a process termed "CSC plasticity." This adaptability underpins their resilience to therapies, including immune checkpoint inhibitors and adoptive cell therapies (ACT). Beyond intrinsic properties, CSCs reside in a specialized microenvironment-the CSC niche-which provides immune-privileged protection, sustains their stemness, and fosters immune suppression. This review highlights the critical role of CSCs and their niche in driving immunotherapy resistance, emphasizing the need for integrative approaches to overcome these challenges.

Develop a prognostic and drug therapy efficacy prediction model for hepatocellular carcinoma based on telomere maintenance-associated genes

Background

Hepatocellular carcinoma (HCC) poses a substantial global health challenge because of its grim prognosis and limited therapeutic options. Telomere maintenance mechanisms (TMM) significantly influence cancer progression, yet their prognostic value in HCC remains largely unexamined. This research aims to establish a telomere maintenance-associated genes(TMGs)-based prognostic model using transcriptomic and clinical data to evaluate its effectiveness in predicting patient outcomes in HCC.

Methods

The identified differentially expressed genes (DEGs) were derived from the analysis of transcriptomic and clinical information sourced from the database of the Cancer Genome Atlas (TCGA) and were cross-referenced with TMGs. Candidate risk factors were initially assessed using univariate Cox regression, subsequently followed by LASSO, and then refined through multivariate Cox regression to establish a risk prediction model. This model's predictive accuracy was validated through Kaplan-Meier(K-M) survival analysis, with external validation in the Gene Expression Omnibus (GEO) dataset. Additionally, a nomogram incorporating age and tumor stage was developed. Tumor mutation burden (TMB), immune profile, and drug sensitivity in HCC were also analyzed. Furthermore, we employed RT-PCR to confirm the expression levels of the genes related to TMGs in HepG2 cell lines.

Results

A prognostic model comprising 3 core genes was constructed, with high-risk individuals showing significantly lower overall survival (OS). The association between elevated TMB and diminished survival in high-risk patients was uncovered through TMB analysis. Immune profiling indicated notable disparities in immune infiltration among these groups, with high-risk patients displaying elevated Tumor Immune Dysfunction and Exclusion (TIDE) scores, suggesting potential immune evasion.

Conclusion

In short, our prognosis model based on TMGs effectively categorized HCC patients using risk scores, enabling dependable prognostic forecasts and identification of potential therapeutic targets for personalized treatment in HCC management. Future studies should explore integrating this model into clinical practice to improve patient outcomes.

Spatial‒temporal heterogeneities of liver cancer and the discovery of the invasive zone

Solid tumours are intricate and highly heterogeneous ecosystems, which grow in and invade normal organs. Their progression is mediated by cancer cells' interaction with different cell types, such as immune cells, stromal cells and endothelial cells, and with the extracellular matrix. Owing to its high incidence, aggressive growth and resistance to local and systemic treatments, liver cancer has particularly high mortality rates worldwide. In recent decades, spatial heterogeneity has garnered significant attention as an unfavourable biological characteristic of the tumour microenvironment, prompting extensive research into its role in liver tumour development. Advances in spatial omics have facilitated the detailed spatial analysis of cell types, states and cell‒cell interactions, allowing a thorough understanding of the spatial and temporal heterogeneities of tumour microenvironment and informing the development of novel therapeutic approaches. This review illustrates the latest discovery of the invasive zone, and systematically introduced specific macroscopic spatial heterogeneities, pathological spatial heterogeneities and tumour microenvironment heterogeneities of liver cancer.

Overcoming immune evasion with innovative multi-target approaches for glioblastoma

Glioblastoma (GBM) cells leverage complex endogenous and environmental regulatory mechanisms to drive proliferation, invasion, and metastasis. Tumor immune evasion, facilitated by a multifactorial network, poses a significant challenge to effective therapy, as evidenced by the limited clinical benefits of monotherapies, highlighting the adaptive nature of immune evasion. This review explores glioblastoma's immune evasion mechanisms, the role of ICIs in the tumor microenvironment, and recent clinical advancements, offering theoretical insights and directions for monotherapy and combination therapy in glioblastoma management.

DET induces apoptosis and suppresses tumor invasion in glioma cells via PI3K/AKT pathway

Introduction

Gliomas, particularly glioblastomas (GBM), are highly aggressive with a poor prognosis and low survival rate. Currently, deoxyelephantopin (DET) has shown promising anti-inflammatory and anti-tumor effects. Using clinical prognostic analysis, molecular docking, and network pharmacology, this study aims to explore the primary targets and signaling pathways to identify novel GBM treatment approaches.

Methods

Using PharmMapper, the chemical structure of DET was examined for possible targets after being acquired from PubChem. GBM-related targets were obtained through multi-omics approaches. A protein-protein interaction (PPI) network was constructed using Cytoscape and STRING, and target binding was evaluated through molecular docking. Enrichment analysis was conducted using Metascape. The effects of DET on GBM cell invasion, apoptosis, and proliferation were assessed through in vitro assays, including Transwell, EDU, CCK8, and flow cytometry. Western blot analysis was performed to examine the components of the PI3K/AKT signaling pathway.

Results

Among the sixty-four shared targets identified, JUN and CCND1 were the most frequently observed. Enrichment analysis demonstrated that DET influenced the MAPK and PI3K/AKT signaling pathways. In Transwell assays, DET significantly inhibited the invasive behavior of glioma cells. Western blot analysis further confirmed the downregulation of EGFR, JUN, and PI3K/AKT.

Conclusion

DET inhibits GBM cell invasion, proliferation, and apoptosis via modulating the PI3K/AKT signaling pathway, highlighting its potential as a novel therapeutic strategy for GBM treatment.

Targeting immune evasion in hepatocellular carcinoma-initiating cells

Tumor-initiating cells (TICs) are particularly efficient at evading detection and elimination by the human immune system. Recent data from Yang and collaborators demonstrate that - at least in preclinical hepatocellular carcinoma models - the immunological privilege of CD49f+ TICs can be limited by targeting CD155, resulting in restored sensitivity to immune checkpoint inhibitors.