Dissecting the role of cancer-associated fibroblast-derived biglycan as a potential therapeutic target in immunotherapy resistance: A tumor bulk and single-cell transcriptomic study

The metabolic crosstalk of cancer-associated fibroblasts and tumor cells: Recent advances and future perspectives

Tumor cells grow in a microenvironment with a lack of nutrients and oxygen. Cancer-associated fibroblasts (CAFs) as one major component of tumor microenvironment have strong ability to survive under stressful conditions through metabolic remodelling. Furthermore, CAFs are educated by tumor cells and help them adapt to the hostile microenvironment through their metabolic communication. By inducing catabolism, CAFs release nutrients into the microenvironment which are taken up by tumor cells to satisfy their metabolic requirements. Furthermore, CAFs can recycle toxic metabolic wastes produced by cancer cells into energetic substances, allowing cancer cells to undergo biosynthesis. Their metabolic crosstalk also enhances CAFs' pro-tumor phenotype and reshape the microenvironment facilitating tumor cells' metastasis and immune escape. In this review, we have analyzed the effect and mechanisms of metabolic crosstalk between tumor cells and CAFs. We also analyzed the future perspectives in this area from the points of CAFs heterogeneity, spatial metabonomics and patient-derived tumor organoids (PDOs). These information may deepen the knowledge of tumor metabolism regulated by CAFs and provide novel insights into the development of metabolism-based anti-cancer strategies.

Cancer-associated fibroblasts promote proliferation, angiogenesis, metastasis and immunosuppression in gastric cancer

Despite advances in surgery, radiotherapy and immunotherapy, the mortality rate for gastric cancer remains one of the highest in the world. A large body of evidence has demonstrated that cancer-associated fibroblasts (CAFs), as core members of the stroma, can secrete cytokines, proteins and exosomes to create a tumour microenvironment that is conducive to cancer cell survival. CAFs can also interact with cancer cells to form a complex signalling network, enabling cancer cells to more easily metastasise to other organs and tissues in the body and develop metastatic foci. In this review, we provide an overview of the CAFs concept and activators. We focus on elucidating their effects on immune cells, intratumoural vasculature, extracellular matrix, as well as cancer cell activity, metastatic power and metabolism, and on enhancing the metastatic ability of cancer cells through activation of JAK/STAT, NF/κB and CXCL12/CXCR4. Various therapeutic agents targeting CAFs are also under development and are expected to improve the prognosis of gastric cancer in combination with existing treatment options.

Comprehensive molecular analyses and experimental validation of CDCAs with potential implications in kidney renal papillary cell carcinoma prognosis

Previous reports have revealed that the abnormal expression of the cell division cycle-associated gene family (CDCAs) is closely associated with some human cancers. However, the precise functional roles and mechanisms of CDCAs in kidney renal papillary cell carcinoma (KIRP) remain unclear. In this study, RNA sequencing data from the Cancer Genome Atlas database and Genotype-Tissue Expression databases were utilized to perform the expression, correlation, survival, mutation, functional enrichment analysis, and immunoinfiltration analyses of CDCAs in KIRP. We found that the expression levels of CDCA genes were significantly increased in KIRP across multiple databases, as confirmed by immunohistochemistry and quantitative reverse transcription PCR (RT-qPCR). Moreover, increased expression of CDCA genes is significantly associated with poor prognosis. Univariate and multivariate Cox regression analyses demonstrated that pathologic T and N staging, NUF2, CDCA2, CDCA3, CDCA5, CBX2, CDCA7, and CDCA8 were independent prognostic factors for patients with KIRP. Utilizing these nine variables, we developed a nomogram prognostic model. Furthermore, the results of GO and KEGG functional enrichment analyses suggested that CDCA genes were associated with nuclear division, mitotic nuclear division, and chromosome segregation and were involved in the cell cycle, p53 signaling pathway, and cellular senescence. We found that the expression of NUF2, CDCA2, CDCA5, and CBX2 was closely associated with the expression of lymphocytes, immunostimulatory molecules, immunoinhibitory molecules, and chemokines. In summary, NUF2, CDCA2, CDCA3, CDCA5, CBX2, CDCA7, and CDCA8 are potential biomarkers for KIRP diagnosis and prognosis.

Modulation of the tumor microenvironment and mechanism of immunotherapy-based drug resistance in breast cancer

Breast cancer, the most frequent female malignancy, is often curable when detected at an early stage. The treatment of metastatic breast cancer is more challenging and may be unresponsive to conventional therapy. Immunotherapy is crucial for treating metastatic breast cancer, but its resistance is a major limitation. The tumor microenvironment (TME) is vital in modulating the immunotherapy response. Various tumor microenvironmental components, such as cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), and myeloid-derived suppressor cells (MDSCs), are involved in TME modulation to cause immunotherapy resistance. This review highlights the role of stromal cells in modulating the breast tumor microenvironment, including the involvement of CAF-TAM interaction, alteration of tumor metabolism leading to immunotherapy failure, and other latest strategies, including high throughput genomic screening, single-cell and spatial omics techniques for identifying tumor immune genes regulating immunotherapy response. This review emphasizes the therapeutic approach to overcome breast cancer immune resistance through CAF reprogramming, modulation of TAM polarization, tumor metabolism, and genomic alterations.

Identification of BGN positive fibroblasts as a driving factor for colorectal cancer and development of its related prognostic model combined with machine learning

BACKGROUND

Numerous studies have indicated that cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, there are still many unknowns regarding the exact role of CAF subtypes in CRC.

METHODS

The data for this study were obtained from bulk, single-cell, and spatial transcriptomic sequencing data. Bioinformatics analysis, in vitro experiments, and machine learning methods were employed to investigate the functional characteristics of CAF subtypes and construct prognostic models.

RESULTS

Our study demonstrates that Biglycan (BGN) positive cancer-associated fibroblasts (BGN + Fib) serve as a driver in colorectal cancer (CRC). The proportion of BGN + Fib increases gradually with the progression of CRC, and high infiltration of BGN + Fib is associated with poor prognosis in terms of overall survival (OS) and recurrence-free survival (RFS) in CRC. Downregulation of BGN expression in cancer-associated fibroblasts (CAFs) significantly reduces migration and proliferation of CRC cells. Among 101 combinations of 10 machine learning algorithms, the StepCox[both] + plsRcox combination was utilized to develop a BGN + Fib derived risk signature (BGNFRS). BGNFRS was identified as an independent adverse prognostic factor for CRC OS and RFS, outperforming 92 previously published risk signatures. A Nomogram model constructed based on BGNFRS and clinical-pathological features proved to be a valuable tool for predicting CRC prognosis.

CONCLUSION

In summary, our study identified BGN + Fib as drivers of CRC, and the derived BGNFRS was effective in predicting the OS and RFS of CRC patients.

Identification of a novel cancer-associated fibroblasts gene signature based on bioinformatics analysis to predict prognosis and therapeutic responses in breast cancer

Cancer-associated fibroblasts (CAFs) provide suitable conditions for growth of tumor cell and facilitate tumor progression. Hence, we aimed to identify a CAFs-related gene signature associated with the prognosis of patients with breast cancer (BRCA). We downloaded datasets from Gene Expression Omnibus (GEO) and confirmed the correlation between CAFs infiltration scores and prognosis. By performing weighted gene co-expression network analysis (WGCNA) and Lasso Cox regression analysis, we constructed a four-gene (COL5A3, FN1, POSTN, and RARRES2) prognostic CAFs signature model. Based on the median risk score of CAFs, patients with BRCA were divided into high- and low-risk groups. Compared with low-risk group, patients in high-risk group exhibited a poor prognosis and limited response to immunotherapy. Furthermore, patients with high CAFs risk scores were found to have a detrimental prognosis due to the induction of immunosuppressive cell infiltration, resulting in an immunosuppressive tumor microenvironment. Importantly, we found that CAFs overexpressing FN1 and POSTN significantly promoted the wound healing and invasion ability of tumor cells in vitro validation. Taking together, we identified a four-gene prognostic CAFs signature, which was proven to be a reliable indicator for prognosis and therapeutic efficacy in patients with BRCA. This study provided evidence for novel CAFs-based stromal therapy.

scRNA-seq characterizing the heterogeneity of fibroblasts in breast cancer reveals a novel subtype SFRP4+ CAF that inhibits migration and predicts prognosis

Introduction

Cancer-associated fibroblasts (CAFs) are a diverse group of cells that significantly impact the tumor microenvironment and therapeutic responses in breast cancer (BC). Despite their importance, the comprehensive profile of CAFs in BC remains to be fully elucidated.

Methods

To address this gap, we utilized single-cell RNA sequencing (scRNA-seq) to delineate the CAF landscape within 14 BC normal-tumor paired samples. We further corroborated our findings by analyzing several public datasets, thereby validating the newly identified CAF subtype. Additionally, we conducted coculture experiments with BC cells to assess the functional implications of this CAF subtype.

Results

Our scRNA-seq analysis unveiled eight distinct CAF subtypes across five tumor and six adjacent normal tissue samples. Notably, we discovered a novel subtype, designated as SFRP4+ CAFs, which was predominantly observed in normal tissues. The presence of SFRP4+ CAFs was substantiated by two independent scRNA-seq datasets and a spatial transcriptomics dataset. Functionally, SFRP4+ CAFs were found to impede BC cell migration and the epithelial-mesenchymal transition (EMT) process by secreting SFRP4, thereby modulating the WNT signaling pathway. Furthermore, we established that elevated expression levels of SFRP4+ CAF markers correlate with improved survival outcomes in BC patients, yet paradoxically, they predict a diminished response to neoadjuvant chemotherapy in cases of triple-negative breast cancer.

Conclusion

This investigation sheds light on the heterogeneity of CAFs in BC and introduces a novel SFRP4+ CAF subtype that hinders BC cell migration. This discovery holds promise as a potential biomarker for refined prognostic assessment and therapeutic intervention in BC.

Non-invasive prediction for pathologic complete response to neoadjuvant chemoimmunotherapy in lung cancer using CT-based deep learning: a multicenter study

Neoadjuvant chemoimmunotherapy has revolutionized the therapeutic strategy for non-small cell lung cancer (NSCLC), and identifying candidates likely responding to this advanced treatment is of important clinical significance. The current multi-institutional study aims to develop a deep learning model to predict pathologic complete response (pCR) to neoadjuvant immunotherapy in NSCLC based on computed tomography (CT) imaging and further prob the biologic foundation of the proposed deep learning signature. A total of 248 participants administrated with neoadjuvant immunotherapy followed by surgery for NSCLC at Ruijin Hospital, Ningbo Hwamei Hospital, and Affiliated Hospital of Zunyi Medical University from January 2019 to September 2023 were enrolled. The imaging data within 2 weeks prior to neoadjuvant chemoimmunotherapy were retrospectively extracted. Patients from Ruijin Hospital were grouped as the training set (n = 104) and the validation set (n = 69) at the 6:4 ratio, and other participants from Ningbo Hwamei Hospital and Affiliated Hospital of Zunyi Medical University served as an external cohort (n = 75). For the entire population, pCR was obtained in 29.4% (n = 73) of cases. The areas under the curve (AUCs) of our deep learning signature for pCR prediction were 0.775 (95% confidence interval [CI]: 0.649 - 0.901) and 0.743 (95% CI: 0.618 - 0.869) in the validation set and the external cohort, significantly superior than 0.579 (95% CI: 0.468 - 0.689) and 0.569 (95% CI: 0.454 - 0.683) of the clinical model. Furthermore, higher deep learning scores correlated to the upregulation for pathways of cell metabolism and more antitumor immune infiltration in microenvironment. Our developed deep learning model is capable of predicting pCR to neoadjuvant chemoimmunotherapy in patients with NSCLC.

Integrating single-cell and bulk transcriptomic analyses to develop a cancer-associated fibroblast-derived biomarker for predicting prognosis and therapeutic response in breast cancer

Background

Cancer-associated fibroblasts (CAFs) contribute to the progression and treatment of breast cancer (BRCA); however, risk signatures and molecular targets based on CAFs are limited. This study aims to identify novel CAF-related biomarkers to develop a risk signature for predicting the prognosis and therapeutic response of patients with BRCA.

Methods

CAF-related genes (CAFRGs) and a risk signature based on these genes were comprehensively analyzed using publicly available bulk and single-cell transcriptomic datasets. Modular genes identified from bulk sequencing data were intersected with CAF marker genes identified from single-cell analysis to obtain reliable CAFRGs. Signature CAFRGs were screened via Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. Multiple patient cohorts were used to validate the prognosis and therapeutic responsiveness of high-risk patients stratified based on the CAFRG-based signature. In addition, the relationship between the CAFRG-based signature and clinicopathological factors, tumor immune landscape, functional pathways, chemotherapy sensitivity and immunotherapy sensitivity was examined. External datasets were used and sample experiments were performed to examine the expression pattern of MFAP4, a key CAFRG, in BRCA.

Results

Integrated analyses of single-cell and bulk transcriptomic data as well as prognostic screening revealed a total of 43 prognostic CAFRGs; of which, 14 genes (TLN2, SGCE, SDC1, SAV1, RUNX1, PDLIM4, OSMR, NT5E, MFAP4, IGFBP6, CTSO, COL12A1, CCDC8 and C1S) were identified as signature CAFRGs. The CAFRG-based risk signature exhibited favorable efficiency and accuracy in predicting survival outcomes and clinicopathological progression in multiple BRCA cohorts. Functional enrichment analysis suggested the involvement of the immune system, and the immune infiltration landscape significantly differed between the risk groups. Patients with high CAF-related risk scores (CAFRSs) exhibited tumor immunosuppression, enhanced cancer hallmarks and hyposensitivity to chemotherapy and immunotherapy. Five compounds were identified as promising therapeutic agents for high-CAFRS BRCA. External datasets and sample experiments validated the downregulation of MFAP4 and its strong correlation with CAFs in BRCA.

Conclusions

A novel CAF-derived gene signature with favorable predictive performance was developed in this study. This signature may be used to assess prognosis and guide individualized treatment for patients with BRCA.

Immune infiltration and clinical significance analyses of the cancer-associated fibroblast-related signature in skin cutaneous melanoma

BACKGROUND

Skin cutaneous melanoma (SKCM) is one of the most aggressive cancers with high mortality rates. Cancer-associated fibroblasts (CAFs) play essential roles in tumor growth, metastasis and the establishment of a pro-tumor microenvironment. This study aimed to establish a CAF-related signature for providing a new perspective for indicating prognosis and guiding therapeutic regimens of SKCM patients.

METHODS

In this study, the CAF-related genes were screened out based on melanoma-associated fibroblast markers identified from single-cell transcriptome analysis in the Gene Expression Omnibus (GEO) database and a CAF-related module identified from weighted gene co-expression analysis using The Cancer Genome Atlas (TCGA) dataset. We extracted these gene expression data of SKCM samples from TCGA and constructed a prognostic CAF-related signature. The prediction abilities of the signature for survival prognosis, tumor immune landscape and responses to chemo-/immunotherapies were evaluated in the TCGA-SKCM cohort.

RESULTS

We suggested that CAFs were significantly involved in the clinical outcomes of SKCM. A 10-gene CAF-related model was constructed, and the high-CAF risk group exhibited immunosuppressive features and worse prognosis. Patients with high CAF score were more likely to not respond to immune checkpoint inhibitors but were more sensitive to some chemotherapeutic agents, suggesting a potential approach of chemotherapy/anti-CAF combination treatment to improve the SKCM patient response rate of current immunotherapies.

CONCLUSIONS

The CAF-related risk score could serve as a robust prognostic indicator and personal assessment of this score could uncover the degree of immunosuppression and provide treatment strategies to improve outcomes in clinical decision-making in SKCM patients.

Single-cell histone chaperones patterns guide intercellular communication of tumor microenvironment that contribute to breast cancer metastases

BACKGROUND

Histone chaperones (HCs) are crucial for governing genome stability and gene expression in multiple cancers. However, the functioning of HCs in the tumor microenvironment (TME) is still not clearly understood.

METHODS

Self-tested single-cell RNA-seq data derived from 6 breast cancer (BC) patients with brain and liver metastases were reanalyzed by nonnegative matrix factorization (NMF) algorithm for 36 HCs. TME subclusters were observed with BC and immunotherapy public cohorts to assess their prognosis and immune response. The biological effect of HSPA8, one of the HCs, was verified by transwell assay and wound-healing assays.

RESULTS

Cells including fibroblasts, macrophages, B cells, and T cells, were classified into various subclusters based on marker genes. Additionally, it showed that HCs might be strongly associated with biological and clinical features of BC metastases, along with the pseudotime trajectory of each TME cell type. Besides, the results of bulk-seq analysis revealed that TME cell subclusters mediated by HCs distinguished significant prognostic value for BC patients and were relevant to patients' immunotherapy responses, especially for B cells and macrophages. In particular, CellChat analysis exhibited that HCs-related TME cell subclusters revealed extensive and diverse interactions with malignant cells. Finally, transwell and wound-healing assays exhibited that HSPA8 deficiency inhibited BC cell migration and invasion.

CONCLUSIONS

Collectively, our study first dissected HCs-guided intercellular communication of TME that contribute to BC metastases.

Development of a Comprehensive Gene Signature Linking Hypoxia, Glycolysis, Lactylation, and Metabolomic Insights in Gastric Cancer through the Integration of Bulk and Single-Cell RNA-Seq Data

BACKGROUND

Hypoxia and anaerobic glycolysis are cancer hallmarks and sources of the metabolite lactate. Intriguingly, lactate-induced protein lactylation is considered a novel epigenetic mechanism that predisposes cells toward a malignant state. However, the significance of comprehensive hypoxia-glycolysis-lactylation-related genes (HGLRGs) in cancer is unclear. We aimed to construct a model centered around HGLRGs for predicting survival, metabolic features, drug responsiveness, and immune response in gastric cancer.

METHODS

The integration of bulk and single-cell RNA-Seq data was achieved using data obtained from the TCGA and GEO databases to analyze HGLRG expression patterns. A HGLRG risk-score model was developed based on univariate Cox regression and a LASSO-Cox regression model and subsequently validated. Additionally, the relationships between the identified HGLRG signature and multiple metabolites, drug sensitivity and various cell clusters were explored.

RESULTS

Thirteen genes were identified as constituting the HGLRG signature. Using this signature, we established predictive models, including HGLRG risk scores and nomogram and Cox regression models. The stratification of patients into high- and low-risk groups based on HGLRG risk scores showed a better prognosis in the latter. The high-risk group displayed increased sensitivity to cytotoxic drugs and targeted inhibitors. The expression of the HGLRG BGN displayed a strong correlation with amino acids and lipid metabolites. Notably, a significant difference in immune infiltration, such as that of M1 macrophages and CD8 T cells, was correlated with the HGLRG signature. The abundant DUSP1 within the mesenchymal components was highlighted by single-cell transcriptomics.

CONCLUSION

The innovative HGLRG signature demonstrates efficacy in predicting survival and providing a practical clinical model for gastric cancer. The HGLRG signature reflects the internal metabolism, drug responsiveness, and immune microenvironment components of gastric cancer and is expected to boost patients' response to targeted therapy and immunotherapy.

The integrated single-cell analysis developed an immunogenic cell death signature to predict lung adenocarcinoma prognosis and immunotherapy

BACKGROUND

Research on immunogenic cell death (ICD) in lung adenocarcinoma (LUAD) has been relatively limited. This study aims to create ICD-related signatures for accurate survival prognosis prediction in LUAD patients, addressing the challenge of lacking reliable early prognostic indicators for this type of cancer.

METHODS

Using single-cell RNA sequencing (scRNA-seq) analysis, ICD activity in cells was calculated by AUCell algorithm, divided into high- and low-ICD groups according to median values, and key ICD regulatory genes were identified through differential analysis, and these genes were integrated into TCGA data to construct prognostic signatures using LASSO and COX regression analysis, and multi-dimensional analysis of ICD-related signatures in terms of prognosis, immunotherapy, tumor microenvironment (TME), and mutational landscape.

RESULTS

The constructed signature reveals a pronounced disparity in prognosis between the high- and low-risk groups of LUAD patients. The statistical discrepancies in survival times among LUAD patients from both the TCGA and GEO databases further corroborate this observation. Additionally, heightened levels of immune cell infiltration expression are evidenced in the low-risk group, suggesting a potential benefit from immunotherapeutic interventions for these patients. The expression levels of pivotal risk-associated genes in tissue samples were assessed utilizing qRT-PCR, thereby unveiling PITX3 as a plausible therapeutic target in the context of LUAD.

CONCLUSIONS

Our constructed ICD-related signatures provide help in predicting the prognosis and immunotherapy of LUAD patients, and to some extent guide the clinical treatment of LUAD patients.

A comprehensive pan-cancer analysis of CDH5 in immunological response

Background

Cadherin 5 (CDH5) functions critically in maintaining cell adhesion and integrity of endothelial and vascular cells. The expression of CDH5 is abnormal in tumor cells, which may have great potential to serve as a new immune checkpoint. The current pan-cancer analysis was performed to better understand the role of CDH5 in tumor.

Methods

The clinical significance and immunological function of CDH5 in pan-cancers were comprehensively analyzed based on the correlations between CDH5 and clinicopathologic features, prognosis values, tumor mutation burden (TMB), microsatellite instability (MSI), immune cells infiltration and immune response genes using 33 datasets from The Cancer Genome Atlas (TCGA). We further confirmed the expression of CDH5 in bladder cancer (BCa) tissues and cell lines. The CD8+ T cells were screened from peripheral blood of healthy controls and activated. BCa cell-CD8+ T cell co-culture assay and ELISA assay were carried out to verify the immunological function of CDH5.

Results

The expression of CDH5 was down-regulated in 8 types of tumors including in BCa but up-regulated in 4 types of tumors. CDH5 was significantly correlated with tumor stage in 6 types of tumors. In addition, CDH5 was positively or negatively correlated with tumor prognosis. Furthermore, CDH5 was closely associated with TMB in 15 types of tumors and with MSI in 9 types of tumors. KEGG-GSEA and Hallmarks-GSEA analyses results indicated that CDH5 was positively related to immune response in most tumor types. In many tumors, CDH5 showed a positive correlation with immune cell infiltration. Enrichment analyses demonstrated that CDH5 was significantly related to the expression of many immunomodulators and chemokines. Further experiments showed that CDH5 was low-expressed in BCa tissues and cell lines in comparison to adjacent normal tissues and normal urothelial cell line, but it was positively associated with a better prognosis of BCa patients. The results of in vitro co-culture assay and ELISA assay demonstrated that CDH5 could promote the function of CD8+ T cells in TME of BCa.

Conclusion

In summary, CDH5 was positively associated with a favorable prognosis and effective immune response in tumors, showing a great potential to serve as a novel tumor biomarker and immune checkpoint.

Regulatory T cells are associated with the tumor immune microenvironment and immunotherapy response in triple-negative breast cancer

Introduction

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer with a high risk of distant metastasis, an extremely poor prognosis, and a high risk of death. Regulatory T cells (Tregs) contribute to the formation of a tumor immunosuppressive microenvironment, which plays an important role in the progression and treatment resistance of TNBC.

Methods

A public single-cell sequencing dataset demonstrated increased infiltration of Tregs in TNBC tissues relative to normal breast tissue. Weighted gene co-expression network analysis was used to identify Treg infiltration-related modules for METABRIC TNBC samples. Subsequently, we obtained two Treg infiltration-associated clusters of TNBC by applying consensus clustering and further constructed a prognostic model based on this Treg infiltration-associated gene module. The ability of the selected gene in the prognostic model, thymidine kinase-1 (TK1), to promote the progression of TNBC was evaluated in vitro.

Results

We concluded that two Treg infiltration-associated clusters had different prognoses and sensitivities to drugs commonly used in breast cancer treatment, and multi-omics analysis revealed that the two clusters had different copy number variations of key tumor progression genes. The 7-gene risk score based on TNBC Treg infiltration was a reliable prognostic indicator both in the training and validation cohorts. Moreover, patients with TNBC with high Treg infiltration-related scores lacked the activation of immune activation pathways and exhibited resistance to anti-PD1 immunotherapy. Knocking down TK1 led to impaired proliferation, migration, and invasion of TNBC cells in vitro. In addition, specimens from patients with TNBC with high TK1 expression showed significantly higher Treg infiltration in tumors. Results of spatial transcriptome analysis showed that TK1 positive cells mainly localize in tumor area, and Treg cell infiltration in TNBC tissues was associated with high expression of TK1. Pan-cancer analysis also demonstrated that TK1 is associated with poor prognosis and activation of proliferation pathways in multiple cancers.

Discussion

We established a prognostic model related to Treg infiltration and this model can be used to establish a clinically relevant classification of TNBC progression. Additionally, our work revealed the underestimable potential of TK1 as a tumor biomarker and immunotherapeutic target.

Combining single-cell sequencing and spatial transcriptome sequencing to identify exosome-related features of glioblastoma and constructing a prognostic model to identify BARD1 as a potential therapeutic target for GBM patients

Background

Glioblastoma (GBM) is a malignant primary brain tumor. This study focused on exploring the exosome-related features of glioblastoma to better understand its cellular composition and molecular characteristics.

Methods

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptome RNA sequencing (stRNA-seq) were used to analyze the heterogeneity of glioblastomas. After data integration, cell clustering, and annotation, five algorithms were used to calculate scores for exosome-related genes(ERGs). Cell trajectory analysis and intercellular communication analysis were performed to explore exosome-related communication patterns. Spatial transcriptome sequencing data were analyzed to validate the findings. To further utilize exosome-related features to aid in clinical decision-making, a prognostic model was constructed using GBM's bulk RNA-seq.

Results

Different cell subpopulations were observed in GBM, with Monocytes/macrophages and malignant cells in tumor samples showing higher exosome-related scores. After identifying differentially expressed ERGs in malignant cells, pseudotime analysis revealed the cellular status of malignant cells during development. Intercellular communication analysis highlighted signaling pathways and ligand-receptor interactions. Spatial transcriptome sequencing confirmed the high expression of exosome-related gene features in the tumor core region. A prognostic model based on six ERGs was shown to be predictive of overall survival and immunotherapy outcome in GBM patients. Finally, based on the results of scRNA-seq and prognostic modeling as well as a series of cell function experiments, BARD1 was identified as a novel target for the treatment of GBM.

Conclusion

This study provides a comprehensive understanding of the exosome-related features of GBM in both scRNA-seq and stRNA-seq, with malignant cells with higher exosome-related scores exhibiting stronger communication with Monocytes/macrophages. In terms of spatial data, highly scored malignant cells were also concentrated in the tumor core region. In bulk RNA-seq, patients with a high exosome-related index exhibited an immunosuppressive microenvironment, which was accompanied by a worse prognosis as well as immunotherapy outcomes. Prognostic models constructed using ERGs are expected to be independent prognostic indicators for GBM patients, with potential implications for personalized treatment strategies for GBM. Knockdown of BARD1 in GBM cell lines reduces the invasive and value-added capacity of tumor cells, and thus BARD1-positively expressing malignant cells are a risk factor for GBM patients.

Identification of CHMP7 as a promising immunobiomarker for immunotherapy and chemotherapy and impact on prognosis of colorectal cancer patients

Introduction: ESCRT is a molecular machine involved in various important physiological processes, such as the formation of multivesicular bodies, cellular autophagy, and cellular membrane repair. CHMP7 is a regulatory subunit of ESCRT-III and is necessary for the proper functioning of ESCRT. In this study, public databases were exploited to explore the role of CHMP7 in tumors. Methods: The research on CHMP7 in oncology is rather limited. In this study, the differential expression of CHMP7 in multiple tumor tissues was analyzed with information from public databases and clinically collected colorectal cancer tissue samples. Subsequently, the mutational landscape of CHMP7, methylation levels, and the relationship between its expression levels and genomic instability were resolved. The immune microenvironment is a compelling emerging star in tumor research. The correlation of CHMP7 with various infiltrating immune cell types in TME was analyzed by online datasets and single-cell sequencing. In terms of clinical treatment, the impact of CHMP7 expression levels on chemotherapy and immunotherapy and the evaluation of small molecule drugs related to CHMP7 were assessed. Results: CHMP7 has a predictive value for the prognosis of patients with tumors and is highly involved in tumor immunity. The downregulation of CHMP7 may lead to genomic instability. A strong correlation between CHMP7 and TME immune cell infiltration has been observed, participating in the formation of suppressive TME and promoting tumor progression. The expression level of CHMP7 is significantly lower in the non-responder group of multiple chemotherapeutic agents. CHMP7 can potentially serve as a new biomarker for predicting the efficacy of tumor chemotherapy and immunotherapy. Conclusion: As a gene of interest, CHMP7 is expected to provide novel and promising targets for further treatment of patients with tumor.

Reassessing Breast Cancer-Associated Fibroblasts (CAFs) Interactions with Other Stromal Components and Clinico-Pathologic Parameters by Using Immunohistochemistry and Digital Image Analysis (DIA)

BACKGROUND

Breast cancer (BC) stroma has CD34- and αSMA-positive cancer-associated fibroblasts (CAFs) differently distributed. During malignant transformation, CD34-positive fibroblasts decrease while αSMA-positive CAFs increase. The prevalence of αSMA-positive CAFs in BC stroma makes microscopic examination difficult without digital image analysis processing (DIA). DIA was used to compare CD34- and αSMA-positive CAFs among breast cancer molecular subgroups. DIA-derived data were linked to age, survival, tumor stroma vessels, tertiary lymphoid structures (TLS), invasion, and recurrence.

METHODS

Double immunostaining for CD34 and αSMA showed different CAF distribution patterns in normal and BC tissues. Single CD34 immunohistochemistry on supplemental slides quantified tumor stroma CD34_CAFs. Digital image analysis (DIA) data on CAF density, intensity, stromal score, and H-score were correlated with clinico-pathologic factors.

RESULTS

CD34/αSMA CAF proportion was significantly related to age in Luminal A (LA), Luminal B (LB), and HER2 subtypes. CD34_CAF influence on survival, invasion, and recurrence of LA, LB-HER2, and TNBC subtypes was found to be significant. The CD34/αSMA-expressing CAFs exhibited a heterogeneous impact on stromal vasculature and TLS.

CONCLUSION

BC stromal CD34_CAFs/αSMA_CAFs have an impact on survival, invasion, and recurrence differently between BC molecular subtypes. The tumor stroma DIA assessment may have predictive potential to prognosis and long-term follow-up of patients with breast cancer.

The Landscape of Small Leucine-Rich Proteoglycan Impact on Cancer Pathogenesis with a Focus on Biglycan and Lumican

Cancer development is a multifactorial procedure that involves changes in the cell microenvironment and specific modulations in cell functions. A tumor microenvironment contains tumor cells, non-malignant cells, blood vessels, cells of the immune system, stromal cells, and the extracellular matrix (ECM). The small leucine-rich proteoglycans (SLRPs) are a family of nineteen proteoglycans, which are ubiquitously expressed among mammalian tissues and especially abundant in the ECM. SLRPs are divided into five canonical classes (classes I-III, containing fourteen members) and non-canonical classes (classes IV-V, including five members) based on their amino-acid structural sequence, chromosomal organization, and functional properties. Variations in both the protein core structure and glycosylation status lead to SLRP-specific interactions with cell membrane receptors, cytokines, growth factors, and structural ECM molecules. SLRPs have been implicated in the regulation of cancer growth, motility, and invasion, as well as in cancer-associated inflammation and autophagy, highlighting their crucial role in the processes of carcinogenesis. Except for the class I SLRP decorin, to which an anti-tumorigenic role has been attributed, other SLPRs' roles have not been fully clarified. This review will focus on the functions of the class I and II SLRP members biglycan and lumican, which are correlated to various aspects of cancer development.

Integrated analysis of single-cell and Bulk RNA sequencing reveals a malignancy-related signature in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD), the most common histotype of lung cancer, may have variable prognosis due to molecular variations. The research strived to establish a prognostic model based on malignancy-related risk score (MRRS) in LUAD.

Methods

We applied the single-cell RNA sequencing (scRNA-seq) data from Tumor Immune Single Cell Hub database to recognize malignancy-related geneset. Meanwhile, we extracted RNA-seq data from The Cancer Genome Atlas database. The GSE68465 and GSE72094 datasets from the Gene Expression Omnibus database were downloaded to validate the prognostic signature. Random survival forest analysis screened MRRS with prognostic significance. Multivariate Cox analysis was leveraged to establish the MRRS. Furthermore, the biological functions, gene mutations, and immune landscape were investigated to uncover the underlying mechanisms of the malignancy-related signature. In addition, we used qRT-PCR to explore the expression profile of MRRS-constructed genes in LUAD cells.

Results

The scRNA-seq analysis revealed the markers genes of malignant celltype. The MRRS composed of 7 malignancy-related genes was constructed for each patient, which was shown to be an independent prognostic factor. The results of the GSE68465 and GSE72094 datasets validated MRRS's prognostic value. Further analysis demonstrated that MRRS was involved in oncogenic pathways, genetic mutations, and immune functions. Moreover, the results of qRT-PCR were consistent with bioinformatics analysis.

Conclusion

Our research recognized a novel malignancy-related signature for predicting the prognosis of LUAD patients and highlighted a promising prognostic and treatment marker for LUAD patients.

Effect of surgical treatment on patients with stage T3 or T4 triple-negative breast cancer: a SEER-based retrospective observational study

Background

The use of surgery is controversial in patients with stage T3 or T4 triple-negative breast cancer (TNBC). We aimed to explore the effect of surgical treatment on overall survival (OS) of these patients.

Methods

A total of 2,041 patients were selected and divided into the surgical and non-surgical groups based on the Surveillance, Epidemiology, and End Results database from 2010 to 2018. Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were applied to balance covariates between different groups. The OS of the two groups were assessed by Kaplan-Meier survival curves and Cox proportional hazards regression models.

Results

A total of 2,041 patients were included in the study. After PSM and IPTW, baseline characteristics of the matched variables were fully balanced. Kaplan-Meier survival curves showed that the median survival time and OS of TNBC patients with stage T3 or T4 in the surgical group were significantly improved compared with those in the non-surgical group. Multivariate Cox proportional hazards regression analysis showed that surgery was a protective factor for prognosis.

Conclusion

Our study found that surgery prolonged the median survival and improved OS compared with the non-surgical group of TNBC patients with stage T3 or T4.

The circadian rhythm key gene ARNTL2: a novel prognostic biomarker for immunosuppressive tumor microenvironment identification and immunotherapy outcome prediction in human cancers

Background

Aryl hydrocarbon receptor nuclear translocator-like 2 (ARNTL2) belongs to the b HLH- PAS domain transcription factor family and is one of the key clock genes that control the circadian rhythm. ARNTL2 plays an important role in human biological functions. However, its role in various tumors, especially in the tumor immune microenvironment (TIME) and immunotherapy, remains unclear.

Methods

We integrated data from cancer patients from multiple databases, including the Cancer Genome Atlas, Cancer Cell Lineage Encyclopedia, Genotype Tissue Expression, Human Protein Atlas, cBioPortal, TIMER, and ImmuCellAI, with data from a large clinical study, three immunotherapy cohorts, and in vitro experiments to investigate the involvement of ARNTL2 expression in cancer prognosis and immune response.

Results

ARNTL2 displayed abnormal expression within most malignant tumors, and is significantly associated with poorer survival and pathologic staging. Through gene-set enrichment analysis (GSEA) and gene-set variation analysis (GSVA), we found that ARNTL2 not only regulates cell cycle-related functions to promote cell proliferation but also regulates autoimmunity-related functions of the innate and adaptive immune systems, and other immune-related signaling pathways. In addition, ARNTL2 overexpression contributes to an immunosuppressive tumor microenvironment that plays a key role in immunosuppression-related features, such as the expression of immunosuppression-related genes and pathways and the number of immunosuppressive-infiltrating cells, including regulatory T cells (Tregs), tumor-associated macrophages (TAMs), and cancer-associated fibroblasts (CAFs). The group of patients with low ARNTL2 expression who received immune checkpoint inhibitors (ICI) therapy had better response rates and longer survival when compared to those with high ARNTL2 expression.

Conclusion

The findings of this study suggest that ARNTL2 is a potential human oncogene that plays an important role in tumorigenesis and cancer immunity. Elevated ARNTL2 expression indicates an immunosuppressive tumor microenvironment. Targeting ARNTL2 in combination with ICI therapy could bring more significant therapeutic benefits to patients with cancer. Our study sheds light on the remarkable potential of ARNTL2 in tumor immunity and provides a novel perspective for anti-tumor strategies.

The role of peroxisome proliferator-activated receptors in the tumor microenvironment, tumor cell metabolism, and anticancer therapy

Peroxisome proliferator-activated receptors (PPARs) have been extensively studied for over 3 decades and consist of three isotypes, including PPARα, γ, and β/δ, that were originally considered key metabolic regulators controlling energy homeostasis in the body. Cancer has become a leading cause of human mortality worldwide, and the role of peroxisome proliferator-activated receptors in cancer is increasingly being investigated, especially the deep molecular mechanisms and effective cancer therapies. Peroxisome proliferator-activated receptors are an important class of lipid sensors and are involved in the regulation of multiple metabolic pathways and cell fate. They can regulate cancer progression in different tissues by activating endogenous or synthetic compounds. This review emphasizes the significance and knowledge of peroxisome proliferator-activated receptors in the tumor microenvironment, tumor cell metabolism, and anti-cancer treatment by summarizing recent research on peroxisome proliferator-activated receptors. In general, peroxisome proliferator-activated receptors either promote or suppress cancer in different types of tumor microenvironments. The emergence of this difference depends on various factors, including peroxisome proliferator-activated receptor type, cancer type, and tumor stage. Simultaneously, the effect of anti-cancer therapy based on drug-targeted PPARs differs or even opposes among the three peroxisome proliferator-activated receptor homotypes and different cancer types. Therefore, the current status and challenges of the use of peroxisome proliferator-activated receptors agonists and antagonists in cancer treatment are further explored in this review.

Epigenetic regulation of cancer-associated fibroblast heterogeneity

Cancer-associated fibroblasts (CAFs), a significant component of the tumor microenvironment (TME), contribute to cancer progression through the secretion of extracellular matrix (ECM), growth factors, and metabolites. It is now well recognized that CAFs are a heterogenous population with ablation experiments leading to reduced tumor growth and single-cell RNA sequencing demonstrating CAF subgroups. CAFs lack genetic mutations yet substantially differ from their normal stromal precursors. Here, we review epigenetic changes in CAF maturation, focusing on DNA methylation and histone modifications. DNA methylation changes in CAFs have been demonstrated globally, while roles of methylation at specific genes affect tumor growth. Further, loss of CAF histone methylation and gain of histone acetylation has been shown to promote CAF activation and tumor promotion. Many CAF activating factors, such as transforming growth factor β (TGFβ), lead to these epigenetic changes. MicroRNAs (miRNAs) serve as targets and orchestrators of epigenetic modifications that influence gene expression. Bromodomain and extra-terminal domain (BET), an epigenetic reader, recognizes histone acetylation and activates the transcription of genes leading to the pro-tumor phenotype of CAFs.

Significance of Cancer-Associated Fibroblasts in the Interactions of Cancer Cells with the Tumor Microenvironment of Heterogeneous Tumor Tissue

The tumor microenvironment (TME) plays a key role in cancer development and progression, as well as contributes to the therapeutic resistance and metastasis of cancer cells. The TME is heterogeneous and consists of multiple cell types, including cancer-associated fibroblasts (CAFs), endothelial cells, and immune cells, as well as various extracellular components. Recent studies have revealed cross talk between cancer cells and CAFs as well as between CAFs and other TME cells, including immune cells. Signaling by transforming growth factor-β, derived from CAFs, has recently been shown to induce remodeling of tumor tissue, including the promotion of angiogenesis and immune cell recruitment. Immunocompetent mouse cancer models that recapitulate interactions of cancer cells with the TME have provided insight into the TME network and support the development of new anticancer therapeutic strategies. Recent studies based on such models have revealed that the antitumor action of molecularly targeted agents is mediated in part by effects on the tumor immune environment. In this review, we focus on cancer cell-TME interactions in heterogeneous tumor tissue, and we provide an overview of the basis for anticancer therapeutic strategies that target the TME, including immunotherapy.

Machine learning-based integration develops an immune-related risk model for predicting prognosis of high-grade serous ovarian cancer and providing therapeutic strategies

Background

High-grade serous ovarian cancer (HGSOC) is a highly lethal gynecological cancer that requires accurate prognostic models and personalized treatment strategies. The tumor microenvironment (TME) is crucial for disease progression and treatment. Machine learning-based integration is a powerful tool for identifying predictive biomarkers and developing prognostic models. Hence, an immune-related risk model developed using machine learning-based integration could improve prognostic prediction and guide personalized treatment for HGSOC.

Methods

During the bioinformatic study in HGSOC, we performed (i) consensus clustering to identify immune subtypes based on signatures of immune and stromal cells, (ii) differentially expressed genes and univariate Cox regression analysis to derive TME- and prognosis-related genes, (iii) machine learning-based procedures constructed by ten independent machine learning algorithms to screen and construct a TME-related risk score (TMErisk), and (iv) evaluation of the effect of TMErisk on the deconstruction of TME, indication of genomic instability, and guidance of immunotherapy and chemotherapy.

Results

We identified two different immune microenvironment phenotypes and a robust and clinically practicable prognostic scoring system. TMErisk demonstrated superior performance over most clinical features and other published signatures in predicting HGSOC prognosis across cohorts. The low TMErisk group with a notably favorable prognosis was characterized by BRCA1 mutation, activation of immunity, and a better immune response. Conversely, the high TMErisk group was significantly associated with C-X-C motif chemokine ligands deletion and carcinogenic activation pathways. Additionally, low TMErisk group patients were more responsive to eleven candidate agents.

Conclusion

Our study developed a novel immune-related risk model that predicts the prognosis of ovarian cancer patients using machine learning-based integration. Additionally, the study not only depicts the diversity of cell components in the TME of HGSOC but also guides the development of potential therapeutic techniques for addressing tumor immunosuppression and enhancing the response to cancer therapy.

Integrating single-cell analysis and machine learning to create glycosylation-based gene signature for prognostic prediction of uveal melanoma

BACKGROUND

Increasing evidence suggests a correlation between glycosylation and the onset of cancer. However, the clinical relevance of glycosylation-related genes (GRGs) in uveal melanoma (UM) is yet to be fully understood. This study aimed to shed light on the impact of GRGs on UM prognosis.

METHODS

To identify the most influential genes in UM, we employed the AUCell and WGCNA algorithms. The GRGs signature was established by integrating bulk RNA-seq and scRNA-seq data. UM patients were separated into two groups based on their risk scores, the GCNS_low and GCNS_high groups, and the differences in clinicopathological correlation, functional enrichment, immune response, mutational burden, and immunotherapy between the two groups were examined. The role of the critical gene AUP1 in UM was validated through in vitro and in vivo experiments.

RESULTS

The GRGs signature was comprised of AUP1, HNMT, PARP8, ARC, ALG5, AKAP13, and ISG20. The GCNS was a significant prognostic factor for UM, and high GCNS correlated with poorer outcomes. Patients with high GCNS displayed heightened immune-related characteristics, such as immune cell infiltration and immune scores. In vitro experiments showed that the knockdown of AUP1 led to a drastic reduction in the viability, proliferation, and invasion capability of UM cells.

CONCLUSION

Our gene signature provides an independent predictor of UM patient survival and represents a starting point for further investigation of GRGs in UM. It offers a novel perspective on the clinical diagnosis and treatment of UM.

Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework

Background

Bladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs) on the prognosis of BLCA, identified various DRG clusters, and developed a risk model to assess patient prognosis, immunological profile, and treatment response.

Methods

The expression and mutational characteristics of four DRGs were first analyzed in bulk RNA-Seq and single-cell RNA sequencing data, IHC staining identified the role of DRGs in BLCA progression, and two DRG clusters were identified by consensus clustering. Using the differentially expressed genes (DEGs) from these two clusters, we transformed ten machine learning algorithms into more than 80 combinations and finally selected the best algorithm to construct a disulfidptosis-related prognostic signature (DRPS). We based this selection on the mean C-index of three BLCA cohorts. Furthermore, we explored the differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between high and low-risk groups. To visually depict the clinical value of DRPS, we employed nomograms. Additionally, we verified whether DRPS predicts response to immunotherapy in BLCA patients by utilizing the Tumour Immune Dysfunction and Rejection (TIDE) and IMvigor 210 cohorts.

Results

In the integrated cohort, we identified several DRG clusters and DRG gene clusters that differed significantly in overall survival (OS) and tumor microenvironment. After the integration of clinicopathological features, DRPS showed robust predictive power. Based on the median risk score associated with disulfidptosis, BLCA patients were divided into low-risk (LR) and high-risk (HR) groups, with patients in the LR group having a better prognosis, a higher tumor mutational load and being more sensitive to immunotherapy and chemotherapy.

Conclusion

Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of BLCA patients, offering new insights into individualized treatment.