Integrated single-cell and bulk RNA sequencing analysis identifies a cancer associated fibroblast-related signature for predicting prognosis and therapeutic responses in colorectal cancer

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

The Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma

BACKGROUND

The current understanding of the prognostic significance of B cells and their role in the tumor microenvironment (TME) in esophageal carcinoma (ESCA) is limited.

METHODS

We conducted a screening for B-cell-related genes through the analysis of single-cell transcriptome data. Subsequently, we developed a B-cell-related gene signature (BRGrisk) using LASSO regression analysis. Patients from The Cancer Genome Atlas cohort were divided into a training cohort and a test cohort. Patients were categorized into high- and low-risk groups based on their median BRGrisk scores. The overall survival was assessed using the Kaplan-Meier method, and a nomogram based on BRGrisk was constructed. Immune infiltration profiles between the risk groups were also compared.

RESULTS

The BRGrisk prognostic model indicated significantly worse outcomes for patients with high BRGrisk scores (p < 0.001). The BRGrisk-based nomogram exhibited good prognostic performance. Analysis of immune infiltration revealed that patients in the high-BRGrisk group had notably higher levels of immune cell infiltration and were more likely to be in an immunoresponsive state. Enrichment analysis showed a strong correlation between the prognostic gene signature and cancer-related pathways. IC50 results indicated that patients in the low-BRGrisk group were more responsive to common drugs compared to those in the high-BRGrisk group.

CONCLUSIONS

This study presents a novel BRGrisk that can be used to stratify the prognosis of ESCA patients and may offer guidance for personalized treatment strategies aimed at improving prognosis.

Consensus clustering and development of a risk signature based on trajectory differential genes of cancer-associated fibroblast subpopulations in colorectal cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, the impact of CAF subpopulation trajectory differentiation on CRC remains unclear.

METHODS

In this study, we first explored the trajectory differences of CAFs subpopulations using bulk and integrated single-cell sequencing data, and then performed consensus clustering of CRC samples based on the trajectory differential genes of CAFs subpopulations. Subsequently, we analyzed the heterogeneity of CRC subtypes using bioinformatics. Finally, we constructed relevant prognostic signature using machine learning and validated them using spatial transcriptomic data.

RESULTS

Based on the differential genes of CAFs subpopulation trajectory differentiation, we identified two CRC subtypes (C1 and C2) in this study. Compared to C1, C2 exhibited worse prognosis, higher immune evasion microenvironment and high CAF characteristics. C1 was primarily associated with metabolism, while C2 was primarily associated with cell metastasis and immune regulation. By combining 101 combinations of 10 machine learning algorithms, we developed a High-CAF risk signatures (HCAFRS) based on the C2 characteristic gene. HCAFRS was an independent prognostic factor for CRC and, when combined with clinical parameters, significantly predicted the overall survival of CRC patients. HCAFRS was closely associated with epithelial-mesenchymal transition, angiogenesis, and hypoxia. Furthermore, the risk score of HCAFRS was mainly derived from CAFs and was validated in the spatial transcriptomic data.

CONCLUSION

In conclusion, HCAFRS has the potential to serve as a promising prognostic indicator for CRC, improving the quality of life for CRC patients.

Targeting the devil: Strategies against cancer-associated fibroblasts in colorectal cancer

Cancer-associated fibroblasts (CAFs), as significant constituents of the tumor microenvironment (TME), play a pivotal role in the progression of cancers, including colorectal cancer (CRC). In this comprehensive review, we presented the origins and activation mechanisms of CAFs in CRC, elaborating on how CAFs drive tumor progression through their interactions with CRC cells, immune cells, vascular endothelial cells, and the extracellular matrix within the TME. We systematically outline the intricate web of interactions among CAFs, tumor cells, and other TME components, and based on this complex interplay, we summarize various therapeutic strategies designed to target CAFs in CRC. It is also essential to recognize that CAFs represent a highly heterogeneous group, encompassing various subtypes such as myofibroblastic CAF (myCAF), inflammatory CAF (iCAF), antigen-presenting CAF (apCAF), vessel-associated CAF (vCAF). Herein, we provide a summary of studies investigating the heterogeneity of CAFs in CRC and the characteristic expression patterns of each subtype. While the majority of CAFs contribute to the exacerbation of CRC malignancy, recent findings have revealed specific subtypes that exert inhibitory effects on CRC progression. Nevertheless, the comprehensive landscape of CAF heterogeneity still awaits exploration. We also highlight pivotal unanswered questions that need to be addressed before CAFs can be recognized as feasible targets for cancer treatment. In conclusion, the aim of our review is to elucidate the significance and challenges of advancing in-depth research on CAFs, while outlining the pathway to uncover the complex roles of CAFs in CRC and underscore their significant potential as therapeutic targets.

Construction of a novel cancer-associated fibroblast-related signature to predict clinical outcome and immune response in cervical cancer

This study developed a prognostic signature for cervical cancer using transcriptome profiling and clinical data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and TISCH database, focusing on cancer-associated fibroblasts (CAFs). Through LASSO Cox regression and integrated bioinformatics analyses, we identified 144 differentially expressed genes (DEGs) related to CAFs, from which an 11-gene CAF-related signature (CAFRSig) was constructed. The CAFRSig effectively stratified patients into high- and low-risk categories, demonstrating significant prognostic capability in predicting overall survival. Gene ontology (GO) and gene set variation analysis (GSVA) linked the DEGs to crucial pathways in tumor malignancy, immune response, and fatty acid metabolism. The immune landscape analysis, utilizing the TIMER platform and CIBERSORT algorithm, revealed a positive correlation between immune cell effector functions and CAFRSig scores, highlighting the model's potential to identify patients likely to respond to immune checkpoint blockade (ICB) therapies. Furthermore, neuropilin 1 (NRP1), a key gene in the CAFRSig, was upregulated in cervical cancer tissues and associated with disease progression and differentiation. The downregulation of NRP1 curbed cell proliferation and influenced the epithelial-mesenchymal transition (EMT), implicating the PI3K/AKT pathway and modulating PD-L1 expression. This comprehensive analysis establishes a robust prognostic signature based on CAF-related genes, offering valuable insights for optimizing therapeutic strategies in cervical cancer management.

Targeting CSF1R in myeloid-derived suppressor cells: insights into its immunomodulatory functions in colorectal cancer and therapeutic implications

OBJECTIVE

This study aimed to investigate the critical role of MDSCs in CRC immune suppression, focusing on the CSF1R and JAK/STAT3 signaling axis. Additionally, it assessed the therapeutic efficacy of LNCs@CSF1R siRNA and anti-PD-1 in combination.

METHODS

Single-cell transcriptome sequencing data from CRC and adjacent normal tissues identified MDSC-related differentially expressed genes. RNA-seq analysis comprehensively profiled MDSC gene expression in murine CRC tumors. LNCs@CSF1R siRNA nanocarriers effectively targeted and inhibited CSF1R. Flow cytometry quantified changes in MDSC surface markers post-CSF1R inhibition. RNA-seq and pathway enrichment analyses revealed the impact of CSF1R on MDSC metabolism and signaling. The effect of CSF1R inhibition on the JAK/STAT3 signaling axis was validated using Colivelin and metabolic assessments. Glucose and fatty acid uptake were measured via fluorescence-based flow cytometry. The efficacy of LNCs@CSF1R siRNA and anti-PD-1, alone and in combination, was evaluated in a murine CRC model with extensive tumor section analyses.

RESULTS

CSF1R played a significant role in MDSC-mediated immune suppression. LNCs@CSF1R siRNA nanocarriers effectively targeted MDSCs and inhibited CSF1R. CSF1R regulated MDSC fatty acid metabolism and immune suppression through the JAK/STAT3 signaling axis. Inhibition of CSF1R reduced STAT3 activation and target gene expression, which was rescued by Colivelin. Combined treatment with LNCs@CSF1R siRNA and anti-PD-1 significantly slowed tumor growth and reduced MDSC abundance within CRC tumors.

CONCLUSION

CSF1R via the JAK/STAT3 axis critically regulates MDSCs, particularly in fatty acid metabolism and immune suppression. Combined therapy with LNCs@CSF1R siRNA and anti-PD-1 enhances therapeutic efficacy in a murine CRC model, providing a strong foundation for future clinical applications.

Identification and validation of calcium extrusion-related genes prognostic signature in colon adenocarcinoma

Background

Disruptions in calcium homeostasis are associated with a wide range of diseases, and play a pivotal role in the development of cancer. However, the construction of prognostic models using calcium extrusion-related genes in colon adenocarcinoma (COAD) has not been well studied. We aimed to identify whether calcium extrusion-related genes serve as a potential prognostic biomarker in the COAD progression.

Methods

We constructed a prognostic model based on the expression of calcium extrusion-related genes (SLC8A1, SLC8A2, SLC8A3, SLC8B1, SLC24A2, SLC24A3 and SLC24A4) in COAD. Subsequently, we evaluated the associations between the risk score calculated by calcium extrusion-related genes and mutation signature, immune cell infiltration, and immune checkpoint molecules. Then we calculated the immune score, stromal score, tumor purity and estimate score using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm. The response to immunotherapy was assessed using tumor immune dysfunction and exclusion (TIDE). Finally, colorectal cancer cells migration, growth and colony formation assays were performed in RKO cells with the overexpression or knockdown SLC8A3, SLC24A2, SLC24A3, or SLC24A4.

Results

We found that patients with high risk score of calcium extrusion-related genes tend to have a poorer prognosis than those in the low-risk group. Additionally, patients in high-risk group had higher rates of KRAS mutations and lower MUC16 mutations, implying a strong correlation between KRAS and MUC16 mutations and calcium homeostasis in COAD. Moreover, the high-risk group showed a higher infiltration of regulatory T cells (Tregs) in the tumor microenvironment. Finally, our study identified two previously unreported model genes (SLC8A3 and SLC24A4) that contribute to the growth and migration of colorectal cancer RKO cells.

Conclusions

Altogether, we developed a prognostic risk model for predicting the prognosis of COAD patients based on the expression profiles of calcium extrusion-related genes, Furthermore, we validated two previously unreported tumor suppressor genes (SLC8A3 and SLC24A4) involved in colorectal cancer progression.

MAPKAP1 orchestrates macrophage polarization and lipid metabolism in fatty liver-enhanced colorectal cancer

Various factors, including fatty liver and macrophage alterations, influence colorectal cancer (CRC). This study explores the mechanistic role of fatty liver in CRC progression, focusing on macrophage polarization and lipid metabolism. A murine fatty liver model was created with a high-fat diet (HFD), and CRC was induced using AOM and DSS. Single-cell transcriptome sequencing (scRNA-seq) identified MAPKAP1 as a critical gene promoting CRC via M2 macrophage polarization and lipid metabolism reprogramming. Prognosis analysis on the TCGA-CRC dataset confirmed MAPKAP1's significance. In vitro and in vivo experiments demonstrated that EVs from fatty liver cells enhanced MAPKAP1 expression, accelerating CRC development and metastasis. HFD exacerbated CRC, but fatty acid inhibitors delayed progression. Fatty liver upregulates MAPKAP1, driving M2 macrophage polarization and lipid metabolism changes, worsening CRC. These findings suggest potential therapeutic strategies for CRC, particularly targeting lipid metabolism and macrophage-mediated tumor promotion.

Multiparametric magnetic resonance imaging (MRI)-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter retrospective study

Background

Predicting the response to neoadjuvant chemoradiotherapy (nCRT) before initiating treatment is essential for tailoring therapeutic strategies and monitoring prognosis in locally advanced rectal cancer (LARC). In this study, we aimed to develop and validate radiomic-based models to predict clinical and pathological complete responses (cCR and pCR, respectively) by incorporating the Shapley Additive exPlanations (SHAP) method for model interpretation.

Methods

A total of 285 patients with complete pretreatment clinical characteristics and T1-weighted (T1W) and T2-weighted (T2W) magnetic resonance imaging (MRI) at 3 centers were retrospectively recruited. The features of tumor lesions were extracted by PyRadiomics and selected using least absolute shrinkage and selection operator (LASSO) algorithm. The selected features were used to build multilayer perceptron (MLP) models alone or combined with clinical features. Area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were applied to evaluate performance of models. The SHAP method was adopted to explain the prediction models.

Results

The radiomic-based models all showed better performances than clinical models. The clinical-radiomic models showed the best differentiation on cCR and pCR with mean AUCs of 0.718 and 0.810 in the validation set, respectively. The decision curves of the clinical-radiomic models showed its values in clinical application. The SHAP method powerfully interpreted the prediction models both at a holistic and individual levels.

Conclusions

Our study highlights that the radiomic-based prediction models have more excellent abilities than clinical models and can effectively predict treatment response and optimize therapeutic strategies for patients with LARCs.

Identified γ-glutamyl cyclotransferase (GGCT) as a novel regulator in the progression and immunotherapy of pancreatic ductal adenocarcinoma through multi-omics analysis and experiments

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is renowned for its formidable and lethal nature, earning it a notorious reputation among malignant tumors. Due to its challenging early diagnosis, high malignancy, and resistance to chemotherapy drugs, the treatment of pancreatic cancer has long been exceedingly difficult in the realm of oncology. γ-Glutamyl cyclotransferase (GGCT), a vital enzyme in glutathione metabolism, has been implicated in the proliferation and progression of several tumor types, while the biological function of GGCT in pancreatic ductal adenocarcinoma remains unknown.

METHODS

The expression profile of GGCT was validated through western blotting, immunohistochemistry, and RT-qPCR in both pancreatic cancer tissue samples and cell lines. Functional enrichment analyses including GSVA, ssGSEA, GO, and KEGG were conducted to explore the biological role of GGCT. Additionally, CCK8, Edu, colony formation, migration, and invasion assays were employed to evaluate the impact of GGCT on the proliferation and migration abilities of pancreatic cancer cells. Furthermore, the LASSO machine learning algorithm was utilized to develop a prognostic model associated with GGCT.

RESULTS

Our study revealed heightened expression of GGCT in pancreatic cancer tissues and cells, suggesting an association with poorer patient prognosis. Additionally, we explored the immunomodulatory effects of GGCT in both pan-cancer and pancreatic cancer contexts, found that GGCT may be associated with immunosuppressive regulation in various types of tumors. Specifically, in patients with high expression of GGCT in pancreatic cancer, there is a reduction in the infiltration of various immune cells, leading to poorer responsiveness to immunotherapy and worse survival rates. In vivo and in vitro assays indicate that downregulation of GGCT markedly suppresses the proliferation and metastasis of pancreatic cancer cells. Moreover, this inhibitory effect appears to be linked to the regulation of GGCT on c-Myc. A prognostic model was constructed based on genes derived from GGCT, demonstrating robust predictive ability for favorable survival prognosis and response to immunotherapy.

Action of circulating and infiltrating B cells in the immune microenvironment of colorectal cancer by single-cell sequencing analysis

BACKGROUND

The complexity of the immune microenvironment has an impact on the treatment of colorectal cancer (CRC), one of the most prevalent malignancies worldwide. In this study, multi-omics and single-cell sequencing techniques were used to investigate the mechanism of action of circulating and infiltrating B cells in CRC. By revealing the heterogeneity and functional differences of B cells in cancer immunity, we aim to deepen our understanding of immune regulation and provide a scientific basis for the development of more effective cancer treatment strategies.

AIM

To explore the role of circulating and infiltrating B cell subsets in the immune microenvironment of CRC, explore the potential driving mechanism of B cell development, analyze the interaction between B cells and other immune cells in the immune microenvironment and the functions of communication molecules, and search for possible regulatory pathways to promote the anti-tumor effects of B cells.

METHODS

A total of 69 paracancer (normal), tumor and peripheral blood samples were collected from 23 patients with CRC from The Cancer Genome Atlas database (https://portal.gdc.cancer.gov/). After the immune cells were sorted by multicolor flow cytometry, the single cell transcriptome and B cell receptor group library were sequenced using the 10X Genomics platform, and the data were analyzed using bioinformatics tools such as Seurat. The differences in the number and function of B cell infiltration between tumor and normal tissue, the interaction between B cell subsets and T cells and myeloid cell subsets, and the transcription factor regulatory network of B cell subsets were explored and analyzed.

RESULTS

Compared with normal tissue, the infiltrating number of CD20⁺B cell subsets in tumor tissue increased significantly. Among them, germinal center B cells (GCB) played the most prominent role, with positive clone expansion and heavy chain mutation level increasing, and the trend of differentiation into memory B cells increased. However, the number of plasma cells in the tumor microenvironment decreased significantly, and the plasma cells secreting IgA antibodies decreased most obviously. In addition, compared with the immune microenvironment of normal tissues, GCB cells in tumor tissues became more closely connected with other immune cells such as T cells, and communication molecules that positively regulate immune function were significantly enriched.

CONCLUSION

The role of GCB in CRC tumor microenvironment is greatly enhanced, and its affinity to tumor antigen is enhanced by its significantly increased heavy chain mutation level. Meanwhile, GCB has enhanced its association with immune cells in the microenvironment, which plays a positive anti-tumor effect.

Action of circulating and infiltrating B cells in the immune microenvironment of colorectal cancer by single-cell sequencing analysis

BACKGROUND

The complexity of the immune microenvironment has an impact on the treatment of colorectal cancer (CRC), one of the most prevalent malignancies worldwide. In this study, multi-omics and single-cell sequencing techniques were used to investigate the mechanism of action of circulating and infiltrating B cells in CRC. By revealing the heterogeneity and functional differences of B cells in cancer immunity, we aim to deepen our understanding of immune regulation and provide a scientific basis for the development of more effective cancer treatment strategies.

AIM

To explore the role of circulating and infiltrating B cell subsets in the immune microenvironment of CRC, explore the potential driving mechanism of B cell development, analyze the interaction between B cells and other immune cells in the immune microenvironment and the functions of communication molecules, and search for possible regulatory pathways to promote the anti-tumor effects of B cells.

METHODS

A total of 69 paracancer (normal), tumor and peripheral blood samples were collected from 23 patients with CRC from The Cancer Genome Atlas database (https://portal.gdc.cancer.gov/). After the immune cells were sorted by multicolor flow cytometry, the single cell transcriptome and B cell receptor group library were sequenced using the 10X Genomics platform, and the data were analyzed using bioinformatics tools such as Seurat. The differences in the number and function of B cell infiltration between tumor and normal tissue, the interaction between B cell subsets and T cells and myeloid cell subsets, and the transcription factor regulatory network of B cell subsets were explored and analyzed.

RESULTS

Compared with normal tissue, the infiltrating number of CD20+B cell subsets in tumor tissue increased significantly. Among them, germinal center B cells (GCB) played the most prominent role, with positive clone expansion and heavy chain mutation level increasing, and the trend of differentiation into memory B cells increased. However, the number of plasma cells in the tumor microenvironment decreased significantly, and the plasma cells secreting IgA antibodies decreased most obviously. In addition, compared with the immune microenvironment of normal tissues, GCB cells in tumor tissues became more closely connected with other immune cells such as T cells, and communication molecules that positively regulate immune function were significantly enriched.

CONCLUSION

The role of GCB in CRC tumor microenvironment is greatly enhanced, and its affinity to tumor antigen is enhanced by its significantly increased heavy chain mutation level. Meanwhile, GCB has enhanced its association with immune cells in the microenvironment, which plays a positive anti-tumor effect.

SETBP1 activation upon MDM4-enhanced ubiquitination of NR3C1 triggers dissemination of colorectal cancer cells

Colorectal cancer (CRC) presents a growing concern globally, marked by its escalating incidence and mortality rates, thus imposing a substantial health burden. This investigation delves into the role of nuclear receptor subfamily 3 group C member 1 (NR3C1) in CRC metastasis and explores the associated mechanism. Through a comprehensive bioinformatics analysis, NR3C1 emerged as a gene with diminished expression levels in CRC. This finding was corroborated by observations of a low-expression pattern of NR3C1 in both CRC tissues and cells. Furthermore, experiments involving NR3C1 knockdown revealed an exacerbation of proliferation, migration, and invasion of CRC cells in vitro. Subsequent assessments in mouse xenograft tumor models, established by injecting human HCT116 cells either through the tail vein or at the cecum termini, demonstrated a reduction in tumor metastasis to the lung and liver, respectively, upon NR3C1 knockdown. Functionally, NR3C1 (glucocorticoid receptor) suppressed SET binding protein 1 (SETBP1) transcription by binding to its promoter region. Notably, mouse double minute 4 (MDM4) was identified as an upstream regulator of NR3C1, orchestrating its downregulation via ubiquitination-dependent proteasomal degradation. Further investigations unveiled that SETBP1 knockdown suppressed migration and invasion, and epithelial to mesenchymal transition of CRC cells, consequently impeding in vivo metastasis in murine models. Conversely, upregulation of MDM4 exacerbated the metastatic phenotype of CRC cells, a propensity mitigated upon additional upregulation of NR3C1. In summary, this study elucidates a cascade wherein MDM4-mediated ubiquitination of NR3C1 enables the transcriptional activation of SETBP1, thereby propelling the dissemination of CRC cells.

Comprehensive analysis of macrophage-related genes in prostate cancer by integrated analysis of single-cell and bulk RNA sequencing

Macrophages, as essential components of the tumor immune microenvironment (TIME), could promote growth and invasion in many cancers. However, the role of macrophages in tumor microenvironment (TME) and immunotherapy in PCa is largely unexplored at present. Here, we investigated the roles of macrophage-related genes in molecular stratification, prognosis, TME, and immunotherapeutic response in PCa. Public databases provided single-cell RNA sequencing (scRNA-seq) and bulk RNAseq data. Using the Seurat R package, scRNA-seq data was processed and macrophage clusters were identified automatically and manually. Using the CellChat R package, intercellular communication analysis revealed that tumor-associated macrophages (TAMs) interact with other cells in the PCa TME primarily through MIF - (CD74+CXCR4) and MIF - (CD74+CD44) ligand-receptor pairs. We constructed coexpression networks of macrophages using the WGCNA to identify macrophage-related genes. Using the R package ConsensusClusterPlus, unsupervised hierarchical clustering analysis identified two distinct macrophage-associated subtypes, which have significantly different pathway activation status, TIME, and immunotherapeutic efficacy. Next, an 8-gene macrophage-related risk signature (MRS) was established through the LASSO Cox regression analysis with 10-fold cross-validation, and the performance of the MRS was validated in eight external PCa cohorts. The high-risk group had more active immune-related functions, more infiltrating immune cells, higher HLA and immune checkpoint gene expression, higher immune scores, and lower TIDE scores. Finally, the NCF4 gene has been identified as the hub gene in MRS using the "mgeneSim" function.

Machine learning developed a fibroblast-related signature for predicting clinical outcome and drug sensitivity in ovarian cancer

Ovarian cancer (OC) is the leading cause of gynecological cancer death. Cancer-associated fibroblasts (CAF) is involved in wound healing and inflammatory processes, tumor occurrence and progression, and chemotherapy resistance in OC. GSE184880 dataset was used to identify CAF-related genes in OC. CAF-related signature (CRS) was constructed using integrative 10 machine learning methods with the datasets from the Cancer Genome Atlas, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082. The performance of CRS in predicting immunotherapy benefits was verified using 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210) and several immune calculating scores. The Lasso + StepCox[forward] method-based predicting model having a highest average C index of 0.69 was referred as the optimal CRS and it had a stable and powerful performance in predicting clinical outcome of OC patients, with the 1-, 3-, and 5-year area under curves were 0.699, 0.708, and 0.767 in the Cancer Genome Atlas cohort. The C index of CRS was higher than that of tumor grade, clinical stage, and many developed signatures. Low CRS score demonstrated lower tumor immune dysfunction and exclusion score, lower immune escape score, higher PD1&CTLA4 immunophenoscore, higher tumor mutation burden score, higher response rate and better prognosis in OC, suggesting a better immunotherapy response. OC patients with low CRS score had a lower half maximal inhibitory concentration value of some drugs (Gemcitabine, Tamoxifen, and Nilotinib, etc) and lower score of some cancer-related hallmarks (Notch signaling, hypoxia, and glycolysis, etc). The current study developed an optimal CRS in OC, which acted as an indicator for the prognosis, stratifying risk and guiding treatment for OC patients.

Extracellular cancer‑associated fibroblasts: A novel subgroup in the cervical cancer microenvironment that exhibits tumor‑promoting roles and prognosis biomarker functions

Tumor invasion and metastasis are the processes that primarily cause adverse outcomes in patients with cervical cancer. Cancer-associated fibroblasts (CAFs), which participate in cancer progression and metastasis, are novel targets for the treatment of tumors. The present study aimed to assess the heterogeneity of CAFs in the cervical cancer microenvironment through single-cell RNA sequencing. After collecting five cervical cancer samples and obtaining the CAF-associated gene sets, the CAFs in the cervical cancer microenvironment were divided into myofibroblastic CAFs and extracellular (ec)CAFs. The ecCAFs appeared with more robust pro-tumorigenic effects than myCAFs according to enrichment analysis. Subsequently, through combining the ecCAF hub genes and bulk gene expression data for cervical cancer obtained from The Cancer Genome Atlas and Gene Ontology databases, univariate Cox regression and least absolute shrinkage and selection operator analyses were performed to establish a CAF-associated risk signature for patients with cancer. The established risk signature demonstrated a stable and strong prognostic capability in both the training and validation cohorts. Subsequently, the association between the risk signature and clinical data was evaluated, and a nomogram to facilitate clinical application was established. The risk score was demonstrated to be associated with both the tumor immune microenvironment and the therapeutic responses. Moreover, the signature also has predictive value for the prognosis of head and neck squamous cell carcinoma, and bladder urothelial carcinoma, which were also associated with human papillomavirus infection. In conclusion, the present study assessed the heterogeneity of CAFs in the cervical cancer microenvironment, and a subgroup of CAFs that may be closely associated with tumor progression was defined. Moreover, a signature based on the hub genes of ecCAFs was shown to have biomarker functionality in terms of predicting survival rates, and therefore this CAF subgroup may become a therapeutic target for cervical cancer in the future.

Cancer-associated fibroblasts: protagonists of the tumor microenvironment in gastric cancer

Enhanced knowledge of the interaction of cancer cells with their environment elucidated the critical role of tumor microenvironment in tumor progression and chemoresistance. Cancer-associated fibroblasts act as the protagonists of the tumor microenvironment, fostering the metastasis, stemness, and chemoresistance of cancer cells and attenuating the anti-cancer immune responses. Gastric cancer is one of the most aggressive cancers in the clinic, refractory to anti-cancer therapies. Growing evidence indicates that cancer-associated fibroblasts are the most prominent risk factors for a poor tumor immune microenvironment and dismal prognosis in gastric cancer. Therefore, targeting cancer-associated fibroblasts may be central to surpassing resistance to conventional chemotherapeutics, molecular-targeted agents, and immunotherapies, improving survival in gastric cancer. However, the heterogeneity in cancer-associated fibroblasts may complicate the development of cancer-associated fibroblast targeting approaches. Although single-cell sequencing studies started dissecting the heterogeneity of cancer-associated fibroblasts, the research community should still answer these questions: "What makes a cancer-associated fibroblast protumorigenic?"; "How do the intracellular signaling and the secretome of different cancer-associated fibroblast subpopulations differ from each other?"; and "Which cancer-associated fibroblast subtypes predominate specific cancer types?". Unveiling these questions can pave the way for discovering efficient cancer-associated fibroblast targeting strategies. Here, we review current knowledge and perspectives on these questions, focusing on how CAFs induce aggressiveness and therapy resistance in gastric cancer. We also review potential therapeutic approaches to prevent the development and activation of cancer-associated fibroblasts via inhibition of CAF inducers and CAF markers in cancer.

Construction of an immune predictive model and identification of TRIP6 as a prognostic marker and therapeutic target of CRC by integration of single-cell and bulk RNA-seq data

BACKGROUND

Investigations elucidating the complex immunological mechanisms involved in colorectal cancer (CRC) and accurately predicting patient outcomes via bulk RNA-Seq analysis have been notably limited. This study aimed to identify the immune status of CRC patients, construct a prognostic model, and identify prognostic signatures via bulk RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq).

METHODS

The scRNA-seq data of CRC were downloaded from Gene Expression Omnibus (GEO). The UCSC Xena database was used to obtain bulk RNA-seq data. Differentially expressed gene (DEG), functional enrichment, and random forest analyses were conducted in order to identify core genes associated with colorectal cancer (CRC) that were relevant to prognosis. A molecular immune prediction model was developed using logistic regression after screening features using the least absolute shrinkage and selection operator (LASSO). The differences in immune cell infiltration, mutation, chemotherapeutic drug sensitivity, cellular senescence, and communication between patients who were at high and low risk of CRC according to the predictive model were investigated. The prognostic genes that were closely associated with CRC were identified by random survival forest (RSF) analysis. The expression levels and clinical significance of the hub genes were analyzed in vitro. The LoVo cell line was employed to ascertain the biological role of thyroid hormone receptor-interacting protein 6 (TRIP6).

RESULTS

A total of seven main cell subtypes were identified by scRNA-seq analysis. A molecular immune predictive model was constructed based on the risk scores. The risk score was significantly associated with OS, stage, mutation burden, immune cell infiltration, response to immunotherapy, key pathways, and cell-cell communication. The functions of the six hub genes were determined and further utilized to establish a regulatory network. Our findings unequivocally confirmed that TRIP6 upregulation was verified in the CRC samples. After knocking down TRIP6, cell proliferation, migration, and invasion of LoVo cells were inhibited, and apoptosis was promoted.

CONCLUSIONS

The molecular predictive model reliably distinguished the immune status of CRC patients. We further revealed that TRIP6 may act as an oncogene in CRC, making it a promising candidate for targeted therapy and as a prognostic marker for CRC.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND

Pyroptosis impacts the development of malignant tumors, yet its role in colorectal cancer (CRC) prognosis remains uncertain.

AIM

To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.

METHODS

Gene expression data were obtained from The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus (GEO). Pyroptosis-related gene expression in cell clusters was analyzed, and enrichment analysis was conducted. A pyroptosis-related risk model was developed using the LASSO regression algorithm, with prediction accuracy assessed through K-M and receiver operating characteristic analyses. A nomogram predicting survival was created, and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations. Finally, the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.

RESULTS

An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B, SDHB, BST2, UBE2D2, GJA1, AIM2, PDCD6IP, and SEZ6L2 (P < 0.05). Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis (P < 0.05). Patients with higher risk scores demonstrated increased death risk and reduced overall survival (P < 0.05). Significant differences in immune infiltration were observed between low- and high-risk groups, correlating with pyroptosis-related gene expression.

CONCLUSION

We developed a pyroptosis-related prognostic model for CRC, affirming its correlation with immune infiltration. This model may prove useful for CRC prognostic evaluation.

IL-15-secreting CAR natural killer cells directed toward the pan-cancer target CD70 eliminate both cancer cells and cancer-associated fibroblasts

BACKGROUND

It remains challenging to obtain positive outcomes with chimeric antigen receptor (CAR)-engineered cell therapies in solid malignancies, like colorectal cancer (CRC) and pancreatic ductal adenocarcinoma (PDAC). A major obstacle is the lack of targetable surface antigens that are not shared by healthy tissues. CD70 emerges as interesting target, due to its stringent expression pattern in healthy tissue and its apparent role in tumor progression in a considerable amount of malignancies. Moreover, CD70 is also expressed on cancer-associated fibroblasts (CAFs), another roadblock for treatment efficacy in CRC and PDAC. We explored the therapeutic potential of CD70 as target for CAR natural killer (NK) cell therapy in CRC, PDAC, focusing on tumor cells and CAFs, and lymphoma.

METHODS

RNA-seq data and immunohistochemical analysis of patient samples were used to explore CD70 expression in CRC and PDAC patients. In addition, CD70-targeting CAR NK cells were developed to assess cytotoxic activity against CD70+ tumor cells and CAFs, and the effect of cytokine stimulation on their efficacy was evaluated. The in vitro functionality of CD70-CAR NK cells was investigated against a panel of tumor and CAF cell lines with varying CD70 expression. Lymphoma-bearing mice were used to validate in vivo potency of CD70-CAR NK cells. Lastly, to consider patient variability, CD70-CAR NK cells were tested on patient-derived organoids containing CAFs.

RESULTS

In this study, we identified CD70 as a target for tumor cells and CAFs in CRC and PDAC patients. Functional evaluation of CD70-directed CAR NK cells indicated that IL-15 stimulation is essential to obtain effective elimination of CD70+ tumor cells and CAFs, and to improve tumor burden and survival of mice bearing CD70+ tumors. Mechanistically, IL-15 stimulation resulted in improved potency of CD70-CAR NK cells by upregulating CAR expression and increasing secretion of pro-inflammatory cytokines, in a mainly autocrine or intracellular manner.

CONCLUSIONS

We disclose CD70 as an attractive target both in hematological and solid tumors. IL-15 armored CAR NK cells act as potent effectors to eliminate these CD70+ cells. They can target both tumor cells and CAFs in patients with CRC and PDAC, and potentially other desmoplastic solid tumors.

Identification of novel T cell proliferation patterns, potential biomarkers and therapeutic drugs in colorectal cancer

Background: T cells are crucial components of antitumor immunity. A list of genes associated with T cell proliferation was recently identified; however, the impact of T cell proliferation-related genes (TRGs) on the prognosis and therapeutic responses of patients with colorectal cancer (CRC) remains unclear. Methods: 33 TRG expression information and clinical information of patients with CRC gathered from multiple datasets were subjected to bioinformatic analysis. Consensus clustering was used to determine the molecular subtypes associated with T cell proliferation. Utilizing the Lasso-Cox regression, a predictive signature was created and verified in external cohorts. A tumor immune environment analysis was conducted, and potential biomarkers and therapeutic drugs were identified and confirmed via in vitro and in vivo studies. Results: CRC patients were separated into two TRG clusters, and differentially expressed genes (DEGs) were identified. Patient information was divided into three different gene clusters, and the determined molecular subtypes were linked to patient survival, immune cells, and immune functions. Prognosis-associated DEGs in the three gene clusters were used to evaluate the risk score, and a predictive signature was developed. The ability of the risk score to predict patient survival and treatment response has been successfully validated using multiple datasets. To discover more possible biomarkers for CRC, the weighted gene co-expression network analysis algorithm was utilized to screen key TRG variations between groups with high- and low-risk. CDK1, BATF, IL1RN, and ITM2A were screened out as key TRGs, and the expression of key TRGs was confirmed using real-time reverse transcription polymerase chain reaction. According to the key TRGs, 7,8-benzoflavone was identified as the most significant drug molecule, and MTT, colony formation, wound healing, transwell assays, and in vivo experiments indicated that 7,8-benzoflavone significantly suppressed the proliferation and migration of CRC cells. Conclusion: T cell proliferation-based molecular subtypes and predictive signatures can be utilized to anticipate patient results, immunological landscape, and treatment response in CRC. Novel biomarker candidates and potential therapeutic drugs for CRC were identified and verified using in vitro and in vivo tests.

A novel oxidative stress-related gene signature as an indicator of prognosis and immunotherapy responses in HNSCC

PURPOSE

To identify molecular subtypes of oxidative stress-related genes in head and neck squamous cell carcinoma (HNSCC) and to construct a scoring model of oxidative stress-related genes.

METHODS

R language based scRNA-seq and bulk RNA-seq analyses were used to identify molecular isoforms of oxidative stress-related genes in HNSCC. An oxidative stress-related gene scoring (OSRS) model was constructed, which were verified through online data and immunohistochemical staining of clinical samples.

RESULTS

Using TCGA-HNSCC datasets, nine predictive genes for overall patient survival, rarely reported in previous similar studies, were screened. AREG and CES1 were identified as prognostic risk factors. CSTA, FDCSP, JCHAIN, IFFO2, PGLYRP4, SPOCK2 and SPINK6 were identified as prognostic factors. Collectively, all genes formed a prognostic risk signature model for oxidative stress in HNSCC, which were validated in GSE41613, GSE103322 and PRJEB23709 datasets. Immunohistochemical staining of SPINK6 in nasopharyngeal cancer samples validated the gene panel. Subsequent analysis indicated that subgroups of the oxidative stress prognostic signature played important roles during cellular communication, the immune microenvironment, the differential activation of transcription factors, oxidative stress and immunotherapeutic responses.

CONCLUSIONS

The risk model might predict HNSCC prognosis and immunotherapeutic responses.

Integrated single-cell and bulk RNA sequencing analysis identifies a neoadjuvant chemotherapy-related gene signature for predicting survival and therapy in breast cancer

Neoadjuvant chemotherapy (NAC) is a well-established treatment modality for locally advanced breast cancer (BC). However, it can also result in severe toxicities while controlling tumors. Therefore, reliable predictive biomarkers are urgently needed to objectively and accurately predict NAC response. In this study, we integrated single-cell and bulk RNA-seq data to identify nine genes associated with the prognostic response to NAC: NDRG1, CXCL14, HOXB2, NAT1, EVL, FBP1, MAGED2, AR and CIRBP. Furthermore, we constructed a prognostic risk model specifically linked to NAC. The clinical independence and generalizability of this model were effectively demonstrated. Additionally, we explore the underlying cancer hallmarks and microenvironment features of this NAC response-related risk score, and further assess the potential impact of risk score on drug response. In summary, our study constructed and validated a nine-gene signature associated with NAC prognosis, which was accomplished through the integration of single-cell and bulk RNA data. The results of our study are of crucial significance in the prediction of the efficacy of NAC in BC, and may have implications for the clinical management of this disease.

Single-cell and spatial architecture of primary liver cancer

Primary liver cancer (PLC) poses a leading threat to human health, and its treatment options are limited. Meanwhile, the investigation of homogeneity and heterogeneity among PLCs remains challenging. Here, using single-cell RNA sequencing, spatial transcriptomic and bulk multi-omics, we elaborated a molecular architecture of 3 PLC types, namely hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular-cholangiocarcinoma (CHC). Taking a high-resolution perspective, our observations revealed that CHC cells exhibit internally discordant phenotypes, whereas ICC and HCC exhibit distinct tumor-specific features. Specifically, ICC was found to be the primary source of cancer-associated fibroblasts, while HCC exhibited disrupted metabolism and greater individual heterogeneity of T cells. We further revealed a diversity of intermediate-state cells residing in the tumor-peritumor junctional zone, including a congregation of CPE+ intermediate-state endothelial cells (ECs), which harbored the molecular characteristics of tumor-associated ECs and normal ECs. This architecture offers insights into molecular characteristics of PLC microenvironment, and hints that the tumor-peritumor junctional zone could serve as a targeted region for precise therapeutical strategies.

scCURE identifies cell types responding to immunotherapy and enables outcome prediction

A deep understanding of immunotherapy response/resistance mechanisms and a highly reliable therapy response prediction are vital for cancer treatment. Here, we developed scCURE (single-cell RNA sequencing [scRNA-seq] data-based Changed and Unchanged cell Recognition during immunotherapy). Based on Gaussian mixture modeling, Kullback-Leibler (KL) divergence, and mutual nearest-neighbors criteria, scCURE can faithfully discriminate between cells affected or unaffected by immunotherapy intervention. By conducting scCURE analyses in melanoma and breast cancer immunotherapy scRNA-seq data, we found that the baseline profiles of specific CD8+ T and macrophage cells (identified by scCURE) can determine the way in which tumor microenvironment immune cells respond to immunotherapy, e.g., antitumor immunity activation or de-activation; therefore, these cells could be predictive factors for treatment response. In this work, we demonstrated that the immunotherapy-associated cell-cell heterogeneities revealed by scCURE can be utilized to integrate the therapy response mechanism study and prediction model construction.

Natural killer cell-related prognosis signature predicts immune response in colon cancer patients

Background: Natural killer (NK) cells are crucial components of the innate immune system that fight tumors and viral infections. Patients with colorectal cancer (CRC) have a poor prognosis, and immunotherapeutic tools play a key role in the treatment of CRC. Methods: Public data on CRC patients was collected from the TCGA and the GEO databases. Tissue data of CRC patients were collected from Guangxi Medical University Affiliated Cancer Hospital. An NK-related prognostic model was developed by the least absolute shrinkage and selection operator (LASSO) and Cox regression method. Validation data were collected from different clinical subgroups and an external independent validation cohort to verify the model's accuracy. In addition, multiple external independent immunotherapy datasets were collected to further examine the value of NK-related risk scores (NKRS) in the prediction of immunotherapy response. Potential biological functions of key genes were examined by methods of cell proliferation, apoptosis and Western blotting. Results: A novel prognostic model for CRC patients based on NK-related genes was developed and NKRS was generated. There was a significantly poorer prognosis among the high-NKRS group. Based on immune response prediction, patients with low NKRS may be more suitable for immunotherapy and they are more sensitive to immunotherapy. The proliferation rate of CRC cells was significantly reduced and apoptosis of CRC cells was increased after SLC2A3 was knocked down. SLC2A3 was also found to be associated with the TGF-β signaling pathway. Conclusion: NKRS has potential applications for predicting prognostic status and response to immunotherapy in CRC patients. SLC2A3 has potential as a therapeutic target for CRC.

Implications of single-cell immune landscape of tumor microenvironment for the colorectal cancer diagnostics and therapy

Colorectal cancer (CRC) originates from the polyps lining the colon and is among the most common types of cancer. With the increasing popularity of single-cell sequencing technologies, researchers have been able to better understand the immune landscape of colorectal cancer, by analyzing their expression and interactions in detail with the tumor microenvironment (TME) at single-cell level. Since the tumor-immune cell interactions play a critical part in the advancement as well as treatment response in colorectal cancer, the release of inhibitory factors such as T cells are important for recognizing and destroying cancer cells. Such information is vital to identify immunotherapeutic targets for cure and monitoring response to treatments. Therefore, a comprehensive single-cell studies-based overview of key immunogenic agents regulating the TME of CRC is provided in this review. Tumor-associated macrophages can promote tumor growth and resistance to treatment by releasing factors that inhibit the function of other immune cells. Additionally, colorectal cancer cells can express programmed cell death protein 1 and its ligand, which can also inhibit T-cell function. Researchers have found that certain types of immune cells, prominently T cells, natural killer, and dendritic cells, can have a positive impact on the prognosis of colorectal cancer patients. Treatments like immune checkpoint inhibitors and CAR-T therapies that help to release the inhibitory signals from the cancer cells allow the immune cells to function more effectively.

CDKN2A was a cuproptosis-related gene in regulating chemotherapy resistance by the MAGE-A family in breast cancer: based on artificial intelligence (AI)-constructed pan-cancer risk model

BACKGROUND

Before the discovery of cuproptosis, copper-loaded nanoparticle is a wildly applied strategy for enhancing the tumor-cell-killing effect of chemotherapy. Although copper(ii)-related researches are wide, details of cuproptosis-related bioprocess in pan-cancer are not clear yet now, especially for prognosis and drug sensitivity prediction yet now.

METHODS

In this study, VOSviewer is used for the literature review, and R4.2.0 is used for data analysis. Public data are collected from TCGA and GEO, local breast cancer cohort is collected to verify the expression level of CDKN2A.

RESULTS

7036 published articles exhibited a time-dependent linear relationship (R=0.9781, p<0.0001), and breast cancer (33.4%) is the most researched topic. Cuproptosis-related-genes (CRGs)-based unsupervised clustering divides pan-cancer subgroups into four groups (CRG subgroup) with differences in prognosis and tumor immunity. 44 tumor-driver-genes (TDGs)-based prediction model of drug sensitivity and prognosis is constructed by artificial intelligence (AI). Based on TDGs and clinical features, a nomogram is (C- index: 0.7, p= 6.958e- 12) constructed to predict the prognosis of breast cancer. Importance analysis identifies CDKN2A has a pivotal role in AI modeling, whose higher expression indicates worse prognosis in breast cancer. Furthermore, inhibition of CDKN2A down-regulates decreases Snail1, Twist1, Zeb1, vimentin and MMP9, while E-cadherin is increased. Besides, inhibition of CDKN2A also decreases the expression of MEGEA4, phosphorylated STAT3, PD-L1, and caspase3, while cleaved-caspase3 is increased. Finally, we find down-regulation of CDKN2A or MAGEA inhibits cell migration and wound healing, respectively.

CONCLUSIONS

AI identified CRG subgroups in pan-cancer based on CRGs-related TDGs, and 44-gene-based AI modeling is a novel tool to identify chemotherapy sensitivity in breast cancer, in which CDKN2A/MAGEA4 pathway played the most important role.

Dual effects of radiotherapy on tumor microenvironment and its contribution towards the development of resistance to immunotherapy in gastrointestinal and thoracic cancers

Successful clinical methods for tumor elimination include a combination of surgical resection, radiotherapy, and chemotherapy. Radiotherapy is one of the crucial components of the cancer treatment regimens which allow to extend patient life expectancy. Current cutting-edge radiotherapy research is focused on the identification of methods that should increase cancer cell sensitivity to radiation and activate anti-cancer immunity mechanisms. Radiation treatment activates various cells of the tumor microenvironment (TME) and impacts tumor growth, angiogenesis, and anti-cancer immunity. Radiotherapy was shown to regulate signaling and anti-cancer functions of various TME immune and vasculature cell components, including tumor-associated macrophages, dendritic cells, endothelial cells, cancer-associated fibroblasts (CAFs), natural killers, and other T cell subsets. Dual effects of radiation, including metastasis-promoting effects and activation of oxidative stress, have been detected, suggesting that radiotherapy triggers heterogeneous targets. In this review, we critically discuss the activation of TME and angiogenesis during radiotherapy which is used to strengthen the effects of novel immunotherapy. Intracellular, genetic, and epigenetic mechanisms of signaling and clinical manipulations of immune responses and oxidative stress by radiotherapy are accented. Current findings indicate that radiotherapy should be considered as a supporting instrument for immunotherapy to limit the cancer-promoting effects of TME. To increase cancer-free survival rates, it is recommended to combine personalized radiation therapy methods with TME-targeting drugs, including immune checkpoint inhibitors.

Cross talk between tumor stemness and microenvironment for prognosis and immunotherapy of uveal melanoma

PURPOSE

Tumor stem cells have emerged as a crucial focus of investigation and a therapeutic target in the context of cancer metastasis and drug resistance. They represent a promising novel approach to address the treatment of uveal melanoma (UVM).

METHODS

According to the one-class logistic regression (OCLR) approach, we first estimated two stemness indices (mDNAsi and mRNAsi) in a cohort of UVM (n = 80). The prognostic value of stemness indices among four subtypes of UVM (subtype A-D) was investigated. Moreover, univariate Cox regression and Lasso-penalized algorithms were conducted to identify a stemness-associated signature and verify in several independent cohorts. Besides, UVM patients classified into subgroups based on the stemness-associated signature. The differences in clinical outcomes, tumor microenvironment, and probability of immunotherapeutic response were investigated further.

RESULTS

We observed that mDNAsi was significantly linked with overall survival (OS) time of UVM, but no association was discovered between mRNAsi and OS. Stratification analysis indicated that the prognostic value of mDNAsi was only limited in subtype D of UVM. Besides, we established and verified a prognostic stemness-associated gene signature which can classify UVM patients into subgroups with distinct clinical outcomes, tumor mutation, immune microenvironment, and molecular pathways. The high risk of UVM is more sensitive to immunotherapy. Finally, a well-performed nomogram was constructed to predict the mortality of UVM patients.

CONCLUSIONS

This study offers a comprehensive examination of UVM stemness characteristics. We discovered mDNAsi-associated signatures improved the prediction capacity of individualized UVM prognosis and indicated prospective targets for stemness-regulated immunotherapy. Analysis of the interaction between stemness and tumor microenvironment may shed light on combinational treatment that targets both stem cell and the tumor microenvironment.

Advancing translational research for colorectal immuno-oncology

Colorectal cancer (CRC) is a common and deadly disease. Unfortunately, immune checkpoint inhibitors (ICIs) fail to elicit effective anti-tumour responses in the vast majority of CRC patients. Patients that are most likely to respond are those with DNA mismatch repair deficient (dMMR) and microsatellite instability (MSI) disease. However, reliable predictors of ICI response are lacking, even within the dMMR/MSI subtype. This, together with identification of novel mechanisms to increase response rates and prevent resistance, are ongoing and vitally important unmet needs. To address the current challenges with translation of early research findings into effective therapeutic strategies, this review summarises the present state of preclinical testing used to inform the development of immuno-regulatory treatment strategies for CRC. The shortfalls and advantages of commonly utilised mouse models of CRC, including chemically induced, transplant and transgenic approaches are highlighted. Appropriate use of existing models, incorporation of patient-derived data and development of cutting-edge models that recapitulate important features of human disease will be key to accelerating clinically relevant research in this area.

A WGCNA-based cancer-associated fibroblast risk signature in colorectal cancer for prognosis and immunotherapy response

Background

Cancer-associated fibroblasts (CAFs) are notably involved in colorectal cancer (CRC) tumorigenesis, progression, and treatment failure. In this article, we report the in silico development of a CAF-related prognostic signature for CRC.

Methods

We separately downloaded CRC transcription data from The Cancer Genome Atlas and the Gene Expression Omnibus database. Deconvolution algorithms, including Estimating the Proportions of Immune and Cancer Cells and the Microenvironment Cell Population-counter, were used to calculate CAF abundance, while the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression algorithm was used to calculate the stromal score. Weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator algorithm were used to identify CAF-related genes and prognostic signatures.

Results

We identified a three-gene, prognostic, CAF-related signature and defined risk groups based on the Riskscores. Multidimensional validations were applied to evaluate the robustness of the signature and its correlation with clinical parameters. We utilized Tumor Immune Dysfunction and Exclusion (TIDE) and oncoPredict algorithms to predict therapy responses and found that patients in low-risk groups are more sensitive to immunotherapy and chemotherapy drugs such as 5-fluorouracil and oxaliplatin. Finally, we used the Cancer Cell Line Encyclopedia and Human Protein Atlas databases to evaluate the mRNA and protein levels encoded by the signature genes.

Conclusions

This novel CAF-related three-gene signature is expected to become a potential prognostic biomarker in CRC and predict chemotherapy and immunotherapy responses. It may be of considerable value for studying the tumor microenvironment in CRC.

Predicting Survival Signature of Bladder Cancer Related to Cancer-Associated Fibroblast (CAF) Constructed by Intersecting Genes in TCGA and GEO

Bladder cancer was one of the most common carcinomas around the world. However, the mechanism of the disease still remained to be investigated. We expected to establish a prognostic survival model with 9 prognostic genes to predict overall survival (OS) in patients of bladder cancer. The gene expression data of bladder cancer were obtained from TCGA and GEO datasets. TCGA and GEO datasets were used for screening prognostic genes along with developing and validating a 9-gene prognostic survival model by method of weighted gene co-expression network analysis (WGCNA) and LASSO with Cox regression. The relative analysis of evaluate tumor burden mutation (TBM), GO, KEGG, chemotherapy drug and functional pathway were also performed based on CAF-related mRNAs. 151 Overlapping CAF-related genes were distinguished after intersecting differentially expressed genes from 945 genes in TCGA and 491 genes in GEO dataset. 9 Prognostic genes (MSRB2, AGMAT, KLF6, DDAH2, GADD45B, SERPINE2, STMN3, TEAD2, and COMP) were used for construction of prognostic model after LASSO with Cox regression. Receiver operating characteristic (ROC) curves showed a good survival prediction by this model. Functional analysis indicated chemokine, cytokine, ECM interaction, oxidative stress and apoptosis were highly appeared. Potential drugs targeted different CAF-related genes like vemurafenib, irofulven, ghiotepa, and idarubicin were found as well. We constructed a novel 9 CAF-related mRNAs prognostic model and explored the gene expression and potential functional information of related genes, which might be worthy of clinical application. In addition, potential chemotherapy drugs could provide useful insights into the potential clinical treatment of bladder cancer.

Prognosis and immunoinfiltration analysis of angiogene-related genes in grade 4 diffuse gliomas

Although angiogenesis critically influences the progression of solid tumors, its contribution to highly malignant, grade 4 diffuse gliomas remains unclear. After analyzing 506 angiogenesis-related genes differentially expressed in grade 4 diffuse gliomas via LASSO and univariate and multivariate COX regression analyses, we constructed a nomogram based on COL22A1, IGFBP2, and MPO that accurately predicted patient survival. The nomogram's performance was validated in an external patient cohort, and a risk score based on the formula COL22A1*0.148+IGFBP2*0.234+MPO*0.145 was used to distinguish high-risk from low-risk patients. Based on differentially expressed genes among risk groups, functional enrichment and drug sensitivity analyses were conducted, and the association between COL22A1, IGFBP2, and MPO expression and infiltrating immune cells and immune checkpoint genes was investigated. We next focused on COL22A1, and verified its overexpression in both glioma cell lines and clinical samples. A pro-oncogenic role for COL22A1, evidenced by impaired proliferation, migration, and invasion capacities, was evidenced upon shRNA-mediated COL22A1 silencing in glioma U87 and LN18 cells. In summary, we present a novel nomogram based on the angiogenesis-related genes COL22A1, IGFBP2, and MPO that allows survival prediction in patients with grade 4 diffuse gliomas. Furthermore, our cellular assays support a pro-oncogenic role for COL22A1 in these tumors.

Deciphering colorectal cancer progression features and prognostic signature by single-cell RNA sequencing pseudotime trajectory analysis

Colorectal cancer is the third most common cancer and second cancer with the highest mortality rate in the world. Progression, which leads to metastasis, is one of the biggest challenges in cancer treatment, and despite improvement in screening and treatment techniques, 5 years of survival of colorectal cancer patients drop from 91% in stage I to 12% in stage IV. Single-cell RNA sequencing is one of the most powerful tools to study complex diseases such as cancer, and despite its recent emergence, it's rapidly growing. In contrast to bulk RNA sequencing, which averages out expression of thousands of cells, single-cell RNA sequencing can capture intra-tumor heterogeneity. Moreover, cellular dynamic events like progression can be studied by pseudotime trajectory analysis of single-cell RNA sequencing data. Herein we used Samsung Medical Center (SMC) colorectal cancer single-cell RNA sequencing dataset to find important tumor epithelial cells subtypes. Subsequently, we've found important genes with a dynamic pattern along cancer progression by using pseudo-time trajectory analysis. Also, we found TGFB1 and IL1B as effective ligands and several transcription factors which may regulate the expression of pseudo-time related genes. In the end, we've constructed a LASSO cox regression using 20 psudotime genes, which can predict 3-year survival of colorectal cancer patients with AUC >0.7.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and immunotherapy response in bladder cancer

BACKGROUND

T cells have been proven to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in bladder cancer (BLCA).

METHODS

Single-cell RNA-sequencing (scRNA-seq) data were downloaded from the gene expression omnibus (GEO) database to screen T-cell marker genes. Bulk RNA-sequencing data and clinical information from BLCA patients were downloaded from the cancer genome atlas (TCGA) database to develop a prognosis signature. We analyzed the association of different risk groups with survival analysis, gene set enrichment analysis (GSEA), tumor mutational burden (TMB), and immunotherapy response.

RESULTS

Based on 192 T-cell marker genes identified by scRNA-seq analysis, we constructed a prognostic signature containing 7 genes in the training cohort, which was further validated in the testing cohort and GEO cohort. The areas under the receiver operating characteristic curve at 1-, 3-, and 5 years were 0.734, 0.742 and 0.726 in the training cohort, 0.697, 0.671 and 0.670 in the testing cohort, 0.702, 0.665 and 0.629 in the GEO cohort, respectively. In addition, we constructed a nomogram based on clinical factors and the risk score of the signature. The low-risk group exhibited higher immune-related pathways, immune cell infiltration and TMB levels. Importantly, immunophenotype score and immunotherapy cohort (IMvigor210) analyses showed that the low-risk group had better immunotherapy response and prognosis.

CONCLUSIONS

Our study reveals a novel prognostic signature based on T-cell marker genes, which provides a new target and theoretical support for BLCA patients.

Single-cell and bulk RNA sequencing reveal cancer-associated fibroblast heterogeneity and a prognostic signature in prostate cancer

Cancer-associated fibroblasts (CAFs), the central players in the tumor microenvironment (TME), can promote tumor progression and metastasis via various functions. However, the properties of CAFs in prostate cancer (PCa) have not been fully assessed. Therefore, we aimed to examine the CAF characteristics in PCa and construct a CAF-derived signature to predict PCa prognosis. CAFs were identified using single-cell RNA sequencing (scRNA-seq) data from 3 studies. We performed the FindAllMarkers function to extract CAF marker genes and constructed a signature to predict the biochemical relapse-free survival (bRFS) of PCa in the Cancer Genome Atlas (TCGA) cohort. Subsequently, different algorithms were applied to reveal the differences of the TME, immune infiltration, treatment responses in the high- and low-risk groups. Additionally, the CAF heterogeneity was assessed in PCa, which were confirmed by the functional enrichment analysis, gene set enrichment analysis (GSEA), and AUCell method. The scRNA-seq analysis identified a CAF cluster with 783 cells and determined 183 CAF marker genes. Cell-cell communication revealed extensive interactions between fibroblasts and immune cells. A CAF-related prognostic model, containing 7 genes (ASPN, AEBP1, ALDH1A1, BGN, COL1A1, PAGE4 and RASD1), was developed to predict bRFS and validated by 4 independent bulk RNA-seq cohorts. Moreover, the high-risk group of the signature score connected with an immunosuppressive TME, such as a higher level of M2 macrophages and lower levels of plasma cells and CD8+ T cells, and a reduced reaction rate for immunotherapy compared with low-risk group. After re-clustering CAFs via unsupervised clustering, we revealed 3 biologically distinct CAF subsets, namely myofibroblast-like CAFs (myCAFs), immune and inflammatory CAFs (iCAFs) and antigen-presenting CAFs (apCAFs). In conclusion, the CAF-derived signature, the first of its kind, can effectively predict PCa prognosis and serve as an indicator for immunotherapy. Furthermore, our study identified 3 CAF subpopulations with distinct functions in PCa.

Deep immunophenotyping reveals clinically distinct cellular states and ecosystems in large-scale colorectal cancer

Determining the diverse cell types in the tumor microenvironment (TME) and their organization into cellular communities, is critical for understanding the biological heterogeneity and therapy of cancer. Here, we deeply immunophenotype the colorectal cancer (CRC) by integrative analysis of large-scale bulk and single cell transcriptome of 2350 patients and 53,137 cells. A rich landscape of 42 cellular states and 7 ecosystems in TMEs is uncovered and extend the previous immune classifications of CRC. Functional pathways and potential transcriptional regulators analysis of cellular states and ecosystems reveal cancer hallmark-related pathways and several critical transcription factors in CRC. High-resolution characterization of the TMEs, we discover the potential utility of cellular states (i.e., Monocytes/Macrophages and CD8 T cell) and ecosystems for prognosis and clinical therapy selection of CRC. Together, our results expand our understanding of cellular organization in TMEs of CRC, with potential implications for the development of biomarkers and precision therapies.

A novel nomogram based on cell cycle-related genes for predicting overall survival in early-onset colorectal cancer

BACKGROUND

Although the incidence of late-onset colorectal cancer (LOCRC) has decreased, the incidence of early-onset colorectal cancer (EOCRC) is still rising dramatically. Heterogeneity in the genomic, biological, and clinicopathological characteristics between EOCRC and LOCRC has been revealed. Therefore, the previous prognostic models based on the total CRC patient population might not be suitable for EOCRC patients. Here, we constructed a prognostic classifier to enhance the precision of individualized treatment and management of EOCRC patients.

METHODS

EOCRC expression data were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The regulatory pathways were explored by gene set enrichment analysis (GSEA). The prognostic model was developed by univariate Cox-LASSO-multivariate Cox regression analyses of GEO samples. TCGA samples were used to verify the model. The expression and mutation profiles and immune landscape of the high-risk and low-risk cohorts were analyzed and compared. Finally, the expression and prognostic value of the model genes were verified by immunohistochemistry and qRT‒PCR analysis.

RESULTS

The cell cycle was identified as the most significantly enriched oncological signature of EOCRC. Then, a 4-gene prognostic signature comprising MCM2, INHBA, CGREF1, and KLF9 was constructed. The risk score was an independent predictor of overall survival. The area under the curve values of the classifier for 1-, 3-, and 5-year survival were 0.856, 0.893, and 0.826, respectively, in the training set and 0.749, 0.858, and 0.865, respectively, in the validation set. Impaired DNA damage repair capability (p < 0.05) and frequent PIK3CA mutations (p < 0.05) were found in the high-risk cohort. CD8 T cells (p < 0.05), activated memory CD4 T cells (p < 0.01), and activated dendritic cells (p < 0.05) were clustered in the low-risk group. Finally, we verified the expression of MCM2, INHBA, CGREF1, and KLF9. Their prognostic value was closely related to age.

CONCLUSION

In this study, a robust prognostic classifier for EOCRC was established and validated. The findings may provide a reference for individualized treatment and medical decision-making for patients with EOCRC.

An Analysis Regarding the Association Between the Nuclear Pore Complex (NPC) and Hepatocellular Carcinoma (HCC)

Background

The nuclear pore complex (NPC) is the main mediator of nuclear and cytoplasmic communication, and delaying or blocking nuclear RNA export and protein shuttling can inhibit cell proliferation and induce apoptosis. Although NPC is a research hotspot in structural biology, relevant studies in hepatocellular carcinoma are scarce, especially in terms of translation into clinical practice.

Methods

This study used a bioinformatics approach combining validation experiments to investigate the biological mechanisms that may be related with NPC. A series of experiments performed to explore the function of the Targeting protein for Xenopus kinesin-like protein 2 (TPX2) in HCC.

Results

Patients with HCC can be divided into two NPC clusters. Patients with high NPC levels (C1) had a shorter survival time than those with low NPC levels (C2) and are characterised by high levels of proliferative signals. We demonstrated that TPX2 regulates HCC growth and inhibits apoptosis in an NPC-dependent manner and contributes to the maintenance of HCC stemness. We developed the NPCScore to predict the prognosis and degree of differentiation in HCC patients.

Conclusion

NPC plays an important role in the malignant proliferation of HCC. Assessing NPC expression patterns could help enhance our understanding of tumor cell proliferation and could guide more effective chemotherapeutic strategies.

Exploring the Correlation Between GPR176, a Potential Target Gene of Gastric Cancer, and Immune Cell Infiltration

Introduction

GPR176, an orphan G protein-coupled receptor (GPCR), is essential for the progression of gastrointestinal cancers. However, it is still unclear how GPR176 affects tumor immunity and patient prognosis in gastric cancer (GC).

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were searched in this investigation to assess the expression patterns of GPR176 in GC tissues and normal gastric mucosa. The findings were further verified using immunohistochemical tests and quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). The Kaplan-Meier method, univariate logistic regression, and Cox regression were then used to investigate the relationship between GPR176 and clinical traits. Additionally, the potential correlation between GPR176, immune checkpoint genes, and immune cell infiltration levels was investigated.

Results

As per the research findings, GC tissues had higher levels of GPR176 than normal tissues. Additionally, individuals with high expression of GPR176 had a worse 10-year overall survival (OS), in contrast with those having a low expression of GPR176 (p < 0.001). The OS of GC can be predicted using a validated nomogram model. The expression of GPR176 demonstrated a negative correlation with CD8+ T cells. When compared to the low-expression group of GPR176, Tumor Immune Dysfunction and Exclusion (TIDE) analysis demonstrated that the high-expression group had a considerably higher risk of immune evasion. A remarkable difference (variation) was observed in the levels of GPR176 expression across both groups, ie, low and high-risk groups, as determined by the immune phenomenon scores (IPS) immunotherapy assessment.

Conclusion

By examining GPR176 from various biological perspectives, it was determined that GPR176 can act as a predictive biomarker for poor patient prognosis in GC. Additionally, it was observed that GPR176 is capable of suppressing the proliferation of CD8+ T cells and facilitating immune evasion.

A T-cell-related signature for prognostic stratification and immunotherapy response in hepatocellular carcinoma based on transcriptomics and single-cell sequencing

BACKGROUND

Hepatocellular carcinoma (HCC) is the fifth most frequently diagnosed malignancy and the third leading cause of cancer death globally. T cells are significantly correlated with the progression, therapy and prognosis of cancer. Limited systematic studies regarding the role of T-cell-related markers in HCC have been performed.

METHODS

T-cell markers were identified with single-cell RNA sequencing (scRNA-seq) data from the GEO database. A prognostic signature was developed with the LASSO algorithm in the TCGA cohort and verified in the GSE14520 cohort. Another three eligible immunotherapy datasets, GSE91061, PRJEB25780 and IMigor210, were used to verify the role of the risk score in the immunotherapy response.

RESULTS

With 181 T-cell markers identified by scRNA-seq analysis, a 13 T-cell-related gene-based prognostic signature (TRPS) was developed for prognostic prediction, which divided HCC patients into high-risk and low-risk groups according to overall survival, with AUCs of 1 year, 3 years, and 5 years of 0.807, 0.752, and 0.708, respectively. TRPS had the highest C-index compared with the other 10 established prognostic signatures, suggesting a better performance of TRPS in predicting the prognosis of HCC. More importantly, the TRPS risk score was closely correlated with the TIDE score and immunophenoscore. The high-risk score patients had a higher percentage of SD/PD, and CR/PR occurred more frequently in patients with low TRPS-related risk scores in the IMigor210, PRJEB25780 and GSE91061 cohorts. We also constructed a nomogram based on the TRPS, which had high potential for clinical application.

CONCLUSION

Our study proposed a novel TRPS for HCC patients, and the TRPS could effectively indicate the prognosis of HCC. It also served as a predictor for immunotherapy.

Unraveling the heterogeneity of cholangiocarcinoma and identifying biomarkers and therapeutic strategies with single-cell sequencing technology

Cholangiocarcinoma (CCA) is a common malignant tumor of the biliary tract that carries a high burden of morbidity and a poor prognosis. Due to the lack of precise diagnostic methods, many patients are often diagnosed at advanced stages of the disease. The current treatment options available are of varying efficacy, underscoring the urgency for the discovery of more effective biomarkers for early diagnosis and improved treatment. Recently, single-cell sequencing (SCS) technology has gained popularity in cancer research. This technology has the ability to analyze tumor tissues at the single-cell level, thus providing insights into the genomics and epigenetics of tumor cells. It also serves as a practical approach to study the mechanisms of cancer progression and to explore therapeutic strategies. In this review, we aim to assess the heterogeneity of CCA using single-cell sequencing technology, with the ultimate goal of identifying possible biomarkers and potential treatment targets.

Integrated multiomics analyses unveil the implication of a costimulatory molecule score on tumor aggressiveness and immune evasion in breast cancer: A large-scale study through over 8,000 patients

BACKGROUND

Although immunotherapy has revolutionised cancer management, reliable genomic biomarkers for identifying eligible patient subpopulations are lacking. Costimulatory molecules play a crucial role in mounting anti-tumour responses, and clinical trials targeting these novel biomarkers are underway. However, whether these molecules can determine tumour aggressiveness and the risk of tumour evasion in breast cancer (BC) remains largely unknown.

METHODS

The whole-tissue transcriptomic data of 8236 patients with BC from 15 independent cohorts were extracted. An integrated scoring system named 'costimulatory molecule score' (CMS) was constructed and sufficient validated using least absolute shrinkage and selection operator regression (1000 iterations) and the random survival forest algorithm (1000 trees). The correlation among CMSs, cancer genotypes and clinicopathological characteristics was examined. Extensive multiomics and immunogenomic analyses were performed to investigate and verify the association among CMSs, enriched pathways, potential intrinsic and extrinsic immune escape mechanisms, immunotherapy response and therapeutic options.

RESULTS

The predictive role of CMS model that relies on expression pattern of merely 5 costimulatory genes for prognosis is almost universally applicable to BC patients in a platform-independent manner. Through internal and external in silico validation, high CMS was characterized by favorable genotypes but decreased tumor immunogenicity, activation of stroma, immune-suppressive states and potential immunotherapeutic resistance. Similar results were observed in a real-world immunotherapy cohort and Pan-Cancer analysis.

CONCLUSION

This comprehensive characterization indicates CMS model may be complemented for predicting tumor aggressiveness and immune evasion in BC patients, underlining the future clinical potential for further exploration of resistance mechanisms and optimization of immunotherapeutic strategies.

Oxaliplatin inhibits colorectal cancer progression by inhibiting CXCL11 secreted by cancer-associated fibroblasts and the CXCR3/PI3K/AKT pathway

PURPOSE

Colorectal cancer (CRC) is a malignant tumor. Oxaliplatin (OXA) can inhibit cancer-associated fibroblasts (CAFs)-induced cancer progression. This study sought to explore the mechanism of OXA in CAFs-induced CRC development.

METHODS

CRC cell lines (Caco-2, SW620), normal fibroblasts (NFs), and CAFs were treated with OXA. NFs and CAFs were cultured. CAFs were treated with/without OXA (0.4 mM), and the supernatant was extracted as the conditioned medium (CM) to culture CRC cells. Cell malignant episodes, E-cadherin and Vimentin levels, CXCL1, CXCL2, CXCL3, CXCL8, and CXCL11 mRNA levels, CXCL11 protein level, and extracellular release were assessed. CAFs were transfected with interfering RNA sh-CXCL11 to silence CXCL11 or transfected with CXCL11 overexpression plasmids and treated with OXA to explore the role of CXCL11 in OXA-mediated CRC cells through CAFs. CXCL11 receptor CXCR3 levels in CRC cells and the PI3K/AKT pathway changes were examined. The xenogeneic tumor was transplanted in nude mice. CXCL11 and CXCR3 levels in tumor tissues, tumor volume, shape, size, weight, and Ki67 positive expressions were assessed.

RESULTS

CRC cell growths and epithelial-mesenchymal transformation were stimulated after culture with CAFs-CM, while OXA averted these trends. CXCL11 mRNA level was elevated most significantly, and its protein and extracellular secretion levels were raised, while OXA diminished the levels. CXCL11 silencing weakened the effects of CAFs-CM on promoting CRC proliferation and malignant episodes and CXCL11 overexpression averted OXA property on inhibiting CAFs-promoted CRC cell growth. CXCR3 and PI3K and AKT1 phosphorylation levels were raised in the CAFs-CM group but diminished by OXA. CXCL11 overexpression in CAFs averted OXA property on inhibiting CAFs-activated CXCR3/PI3K/AKT in CRC cells. OXA also inhibited the progression of xenograft tumors by limiting CAFs-secreted CXCL11.

CONCLUSIONS

OXA repressed CRC progression by inhibiting CAFs-secreted CXCL11 and the CXCR3/PI3K/AKT pathway.

Ecological niches for colorectal cancer stem cell survival and thrival

To date, colorectal cancer is still ranking top three cancer types severely threatening lives. According to cancer stem cell hypothesis, malignant colorectal lumps are cultivated by a set of abnormal epithelial cells with stem cell-like characteristics. These vicious stem cells are derived from intestinal epithelial stem cells or transformed by terminally differentiated epithelial cells when they accumulate an array of transforming genomic alterations. Colorectal cancer stem cells, whatever cell-of-origin, give rise to all morphologically and functionally heterogenous tumor daughter cells, conferring them with overwhelming resilience to intrinsic and extrinsic stresses. On the other hand, colorectal cancer stem cells and their daughter cells continuously participate in constructing ecological niches for their survival and thrival by communicating with adjacent stromal cells and circulating immune guardians. In this review, we first provide an overview of the normal cell-of-origin populations contributing to colorectal cancer stem cell reservoirs and the niche architecture which cancer stem cells depend on at early stage. Then we survey recent advances on how these aberrant niches are fostered by cancer stem cells and their neighbors. We also discuss recent research on how niche microenvironment affects colorectal cancer stem cell behaviors such as plasticity, metabolism, escape of immune surveillance as well as resistance to clinical therapies, therefore endowing them with competitive advantages compared to their normal partners. In the end, we explore therapeutic strategies available to target malignant stem cells.

Prognostic value of genes related to cancer-associated fibroblasts in lung adenocarcinoma

BACKGROUND

Although it has been established that cancer-associated fibroblasts (CAFs) facilitate tumor development, the relationship between CAFs and the prognosis of patients with lung adenocarcinoma (LUAD) has not been extensively explored.

OBJECTIVE

This study was formulated to investigate the prognostic value of CAF-related genes in LUAD.

METHODS

Differential analysis was carried out with TCGA-LUAD dataset as the training set. By overlapping differentially expressed genes (DEGs) with genes associated with CAF, CAF-related DEGs specific to LUAD were obtained. A prognostic risk model was constructed by Lasso and Cox regression analysis, and samples were grouped according to median risk score. The efficacy of the model was accessed through survival curve and receiver operating characteristic curve (ROC) analyses, with the validation set for verification. Risk score combined with clinical factors was utilized for Cox analysis to verify the independence of the model, and a nomogram was drawn. GSEA was performed on different risk groups. Immunologic infiltration and tumor mutational burden were assessed in different risk groups.

RESULTS

Eleven feature genes including DLGAP5, KCNE2, UPK2, NPAS2, ARHGAP11A, ANGPTL4, ANLN, DKK1, SMUG1, C16orf74, and ACAD8 were identified, based on which a prognostic model was constructed. Risk score could predict the prognosis of LUAD patients and could be an independent prognostic factor for LUAD patients. GSEA outcomes displayed significant enrichment of genes in the high-risk group in the P53 SIGNALING PATHWAY. In comparison to the low-risk group, the high-risk group exhibited a decreased degree of immune infiltration and an elevated level of tumor mutational burden.

CONCLUSION

An 11-gene model was constructed based on CAF-related genes to predict LUAD prognosis. This model represented an independent prognostic factor for LUAD.

Immune-related gene-based prognostic index for predicting survival and immunotherapy outcomes in colorectal carcinoma

Introduction

Colorectal cancer shows high incidence and mortality rates. Immune checkpoint blockade can be used to treat colorectal carcinoma (CRC); however, it shows limited effectiveness in most patients.

Methods

To identify patients who may benefit from immunotherapy using immune checkpoint inhibitors, we constructed an immune-related gene prognostic index (IRGPI) for predicting the efficacy of immunotherapy in patients with CRC. Transcriptome datasets and clinical information of patients with CRC were used to identify differential immune-related genes between tumor and para-carcinoma tissue. Using weighted correlation network analysis and Cox regression analysis, the IRGPI was constructed, and Kaplan-Meier analysis was used to evaluate its predictive ability. We also analyzed the molecular and immune characteristics between IRGPI high-and low-risk subgroups, performed sensitivity analysis of ICI treatment, and constructed overall survival-related receiver operating characteristic curves to validate the IRGPI. Finally, IRGPI genes and tumor immune cell infiltration in CRC model mice with orthotopic metastases were analyzed to verify the results.

Results

The IRGPI was constructed based on the following 11 hub genes: ADIPOQ, CD36, CCL24, INHBE, UCN, IL1RL2, TRIM58, RBCK1, MC1R, PPARGC1A, and LGALS2. Patients with CRC in the high-risk subgroup showed longer overall survival than those in the low-risk subgroup, which was confirmed by GEO database. Clinicopathological features associated with cancer progression significantly differed between the high- and low-risk subgroups. Furthermore, Kaplan-Meier analysis of immune infiltration showed that the increased infiltration of naïve B cells, macrophages M1, and regulatory T cells and reduced infiltration of resting dendritic cells and mast cells led to a worse overall survival in patients with CRC. The ORC curves revealed that IRGPI predicted patient survival more sensitive than the published tumor immune dysfunction and rejection and tumor inflammatory signature.

Discussion

Thus, the low-risk subgroup is more likely to benefit from ICIs than the high-risk subgroup. CRC model mice showed higher proportions of Tregs, M1 macrophages, M2 macrophages and lower proportions of B cells, memory B cell immune cell infiltration, which is consistent with the IRGPI results. The IRGPI can predict the prognosis of patients with CRC, reflect the CRC immune microenvironment, and distinguish patients who are likely to benefit from ICI therapy.

Predictive biomarkers of colon cancer immunotherapy: Present and future

Immunotherapy has revolutionized colon cancer treatment. Immune checkpoint inhibitors (ICIs) have shown clinical benefits for colon cancer patients, especially those with high microsatellite instability (MSI-H). In 2020, the US Food and Drug Administration (FDA)-approved ICI pembrolizumab as the first-line treatment for metastatic MSI-H colon cancer patients. Additionally, neoadjuvant immunotherapy has presented efficacy in treating early-stage colon cancer patients. Although MSI has been thought of as an effective predictive biomarker for colon cancer immunotherapy, only a small proportion of colon cancer patients were MSI-H, and certain colon cancer patients with MSI-H presented intrinsic or acquired resistance to immunotherapy. Thus, further search for predictive biomarkers to stratify patients is meaningful in colon cancer immunotherapy. Except for MSI, other biomarkers, such as PD-L1 expression level, tumor mutation burden (TMB), tumor-infiltrating lymphocytes (TILs), certain gut microbiota, ctDNA, and circulating immune cells were also proposed to be correlated with patient survival and ICI efficacy in some colon cancer clinical studies. Moreover, developing new diagnostic techniques helps identify accurate predictive biomarkers for colon cancer immunotherapy. In this review, we outline the reported predictive biomarkers in colon cancer immunotherapy and further discuss the prospects of technological changes for biomarker development in colon cancer immunotherapy.

Identification of Prognostic Gene Signatures by Developing a scRNA-Seq-Based Integration Approach to Predict Recurrence and Chemotherapy Benefit in Stage II–III Colorectal Cancer

Prospective identification of robust biomarkers related to prognosis and adjuvant chemotherapy has become a necessary and critical step to predict the benefits of adjuvant therapy for patients with stage II–III colorectal cancer (CRC) before clinical treatment. We proposed a single-cell-based prognostic biomarker recognition approach to identify and construct CRC up- and down-regulated prognostic signatures (CUPsig and CDPsig) by integrating scRNA-seq and bulk datasets. We found that most genes in CUPsig and CDPsig were known disease genes, and they had good prognostic abilities in CRC validation datasets. Multivariate analysis confirmed that they were two independent prognostic factors of disease-free survival (DFS). Significantly, CUPsig and CDPsig could effectively predict adjuvant chemotherapy benefits in drug-treated validation datasets. Additionally, they also performed well in patients with CMS4 subtype. Subsequent analysis of drug sensitivity showed that expressions of these two signatures were significantly associated with the sensitivities of CRC cell lines to multiple drugs. In summary, we proposed a novel prognostic biomarker identification approach, which could be used to identify novel prognostic markers for stage II–III CRC patients who will undergo adjuvant chemotherapy and facilitate their further personalized treatments.