GPR171 restrains intestinal inflammation by suppressing FABP5-mediated Th17 cell differentiation and lipid metabolism

BACKGROUND

GPR171 suppresses T cell immune responses involved in antitumour immunity, while its role in inflammatory bowel disease (IBD) pathogenesis remains unclear.

OBJECTIVE

We aimed to investigate the role of GPR171 in modulating CD4+ T cell effector functions in IBD and evaluate its therapeutic potential.

DESIGN

We analysed GPR171 expression in colon biopsies and peripheral blood samples from patients with IBD and assessed the impact of GPR171 on CD4+ T cell differentiation through administration of its endogenous ligand (BigLEN). We further determined the role of GPR171 in dextran sulfate sodium (DSS)-induced colitis and CD45RBhighCD4+ T-cell transfer colitis model and deciphered the underlying mechanisms using RNA sequencing (RNA-seq) and lipidomics. We developed a novel BigLEN-based Fc fusion protein (BigLEN-Fc) and evaluated its potential in preventing and treating colitis.

RESULTS

GPR171 was markedly increased in inflamed mucosa and CD4+ T cells of patients with IBD compared with controls. BigLEN-triggered GPR171 activation inhibited Th17 cell differentiation in vitro. GPR171 deficiency exacerbated DSS- and CD45RBhighCD4+ T cell-induced colitis in mice, characterised by increased Th17 cell responses in intestinal mucosa. Mechanistically, GPR171 deficiency promoted Th17 cell differentiation and altered lipidome profile in Th17 cells via the cAMP-pCREB-FABP5 axis. Blockage of FABP5 reduced Th17 cell differentiation in vitro and ameliorated DSS-induced colitis in Gpr171 -/- mice. Furthermore, BigLEN-mutFc administration potently mitigated colitis in mice.

CONCLUSIONS

GPR171 deficiency promotes Th17 cell differentiation and causes lipid metabolism perturbation, contributing to intestinal inflammation in a FABP5-dependent manner. Target therapy (eg, BigLEN-Fc) represents a novel therapeutic approach for IBD treatment.

Alterations in PD-L1 succinylation shape anti-tumor immune responses in melanoma

Tumors undergo metabolic reprogramming to meet the energetic, synthetic and redox demands essential for malignancy, often characterized by increased glycolysis and lactate production. However, the role of mitochondrial metabolism in tumor immunity remains unclear. The present study integrates spatial transcriptomics, bulk transcriptomics and proteomics, revealing a strong link between the metabolite succinyl-CoA and tumor immunity as well as the efficacy of anti-programmed cell death protein-1 (PD-1) therapy in patients with melanoma. Elevated succinyl-CoA levels, through α-ketoglutarate or succinate supplementation, enhanced T cell-mediated tumor elimination, both in vitro and in vivo. Mechanistically, succinylation of the ligand of PD-1 (PD-L1) at lysine 129 led to its degradation. Increased carnitine palmitoyltransferase 1A (CPT1A), identified as a succinyltransferase for PD-L1, boosted anti-tumor activity. Preclinically, bezafibrate, a hyperlipidemia drug, upregulated CPT1A and synergized with CTLA-4 monoclonal antibody to inhibit tumor growth. Clinically, higher PD-L1 and lower CPT1A levels in tumors correlated with better anti-PD-1 therapy responses, suggesting potential biomarkers for prediction of treatment efficacy.

Immunogenic cell death signature predicts survival and reveals the role of VEGFA + Mast cells in lung adenocarcinoma

Lung cancer is prevalent worldwide and is a major cause of cancer-related mortality. Despite being the primary model for immunotherapy research, the response rates of lung cancer patients to immunotherapy are unsatisfactory. Furthermore, research on immunogenic cell death (ICD) in lung cancer is limited, which limits the development of strategies that combine ICD-related therapies with immunotherapy. In this study, we compiled and summarized 69 genes associated with ICD and developed an IRS. Across seven independent datasets, the IRS was identified as an independent prognostic factor. IRS was positively associated with multiple tumor proliferation pathways and negatively associated with immune-related pathways. Additionally, IRS negatively correlated with the infiltration of various immune cells, supporting its association with survival outcomes. Based on the correlation between IRS and immune activity, we validated the ability of IRS to predict immunotherapy efficacy across seven immunotherapy datasets and demonstrated that patients who respond to immunotherapy tend to have a lower IRS. Moreover, utilizing single-cell RNA sequencing, we revealed the role of mast cells in the TME with the highest IRS. Through interactions with various receptors on macrophages, endothelial cells, and tumor cells, mast cells promote tumor progression, providing a comprehensive explanation for poor prognosis and lack of response to immunotherapy in patients with high IRS. Our study offers new guidance for combination therapies in lung adenocarcinoma patients and elucidated the mechanism by which mast cells contribute to cancer development within the TME.

Molecular subtyping combined with multiomics analysis to study correlation between TACE refractoriness and tumor stemness in hepatocellular carcinoma

BACKGROUND

Transarterial chemoembolization (TACE) refractoriness is a significant challenge in treating intermediate to advanced-stage hepatocellular carcinoma (HCC). A few studies suggest that liver cancer stem cells (LCSCs) may be associated with TACE refractoriness. This study aims to explore the potential correlation between TACE refractoriness and HCC stemness, highlighting its clinical significance.

METHODS

This research encompassed the analysis of diverse HCC datasets, including RNA-sequencing, microarray, single-cell RNA-sequencing, and clinical cohorts. We identified common genes between TACE refractoriness and tumor stemness (TSGs). Unsupervised clustering was employed to classify HCC patients into different clusters based on TSGs (TRS clusters). The study explored the differences in clinical prognosis, biological characteristics, genomic variations, immune landscapes, and treatment responses among the TRS clusters.

RESULTS

Patients with TACE-refractoriness demonstrated significantly higher tumor stemness. Our study identified 33 TSGs and established two TRS clusters, including C1 and C2. C1 was associated with TACE refractoriness, elevated tumor stemness, and poorer prognosis. Genomic alterations were found to be significantly different between the TRS clusters. The C1 exhibited signs of immunosuppression and lower activity of immune effector cells, while the C2 had a more robust immune response and higher level of immune cell presence. Single-cell RNA-seq revealed distinct cell type characteristics in each subtypes, with the C1 showing a higher proportion of stem cells and malignant cells.

CONCLUSION

Our findings establish a connection between TACE refractoriness and tumor stemness in HCC, proposing a novel subtype classification to guide personalized treatment. Insights gained may facilitate overcoming TACE refractoriness and the development of innovative therapies.

Comprehensive Analyses of PANoptosome with Potential Implications in Cancer Prognosis and Immunotherapy

Cell death resistance significantly contributes to poor therapeutic outcomes in various cancers. PANoptosis, a unique inflammatory programmed cell death (PCD) pathway activated by specific triggers and regulated by the PANoptosome, possesses key features of apoptosis, pyroptosis, and necroptosis, but these cannot be accounted for by any of the three PCD pathways alone. While existing studies on PANoptosis have predominantly centered on infectious and inflammatory diseases, its role in cancer malignancy has been understudied. In this comprehensive investigation, we conducted pan-cancer analyses of PANoptosome component genes across 33 cancer types. We characterized the genetic, epigenetic, and transcriptomic landscapes, and introduced a PANoptosome-related potential index (PANo-RPI) for evaluating the intrinsic PANoptosome assembly potential in cancers. Our findings unveil PANo-RPI as a prognostic factor in numerous cancers, including KIRC, LGG, and PAAD. Crucially, we established a significant correlation between PANo-RPI and tumor immune responses, as well as the infiltration of diverse lymphoid and myeloid cell subsets across nearly all cancer types. Moreover, a high PANo-RPI was consistently associated with improved immunotherapy response and efficacy, as evidenced by re-analysis of multiple immunotherapy cohorts. In conclusion, our study suggests that targeting PANoptosome components and modulating PANoptosis may hold tremendous therapeutic potential in the context of cancer.

Improving Outcomes in Hepatocellular Carcinoma through Integration of Machine Learning: Development of a Tumor-Associated Macrophage Signature

INTRODUCTION

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors globally. Macrophages, as essential components of the immune system, play crucial roles in immune regulation, inflammation modulation, and antitumor activity. However, it remains unclear whether tumor-associated macrophages can serve as prognostic markers for HCC.

METHODS

First, we identified tumor-associated macrophages based on single-cell data from GSE140228. Then, using a machine learning approach with a combination of 101 module genes, we constructed an optimal prognostic model. Subsequently, we compared our constructed model with other published prognostic models for HCC. Finally, we utilized the generated model score to predict the response to chemotherapy and immune therapy.

RESULTS

First, we identified clusters of tumor-associated macrophages using single-cell data. Subsequently, we calculated the tumor-associated macrophage score based on module genes from the previous step. Compared to traditional clinical indicators, tumor-associated macrophage signature (TAMS) exhibits significant advantages. The TAMS C-index not only predicts overall survival, but also recurrence-free survival in HCC patients. Additionally, there was a higher prevalence of TP53 mutations in HCC patients with high TAMS. Furthermore, patients with low TAMS showed greater sensitivity to immunotherapy compared to those with high TAMS. Notably, the number and intensity of interactions between TAM and other T lymphocytes were significantly higher than those involving other cell populations. Interestingly, the high TAMS group exhibited significantly elevated levels of immune checkpoint markers and M2 macrophage markers.

CONCLUSION

TAMS can serve as a novel and potent tool, offering improved treatment options and prognostic assessment for patients with HCC.

The role of FOXK2-FBXO32 in breast cancer tumorigenesis: Insights into ribosome-associated pathways

OBJECTIVE

To search for a new biomarker that can predict the efficacy and prognosis of tumor immunotherapy.

METHOD

FOXK2 genes were analyzed using single-cell sequencing in pan-cancer bulk RNA-seq from the TCGA database. We used algorithms to predict their immune infiltration. Functional enrichment and ChIP-seq identified potential downstream gene, FBXO32. FBXO32's role in cancer immune response was explored through analysis.

RESULTS

Significant up-regulation of FOXK2 was observed in prostate adenocarcinoma (PRAD), uterine corpus endometrial carcinoma (UCEC), bladder urothelial carcinoma (BLCA), colorectal cancer (CRC), pancreatic ductal adenocarcinoma (PDAC), and stomach adenocarcinoma (STAD), while no such increase was found in lung cancer (lung adenocarcinoma [LUAD], lung squamous cell carcinoma [LUSC]) or thyroid carcinoma (THCA) tumor and adjacent tissues. FOXK2 expression correlated with patient prognosis, with lower expression associated with better immune response and survival and higher expression of its downstream gene FBXO32 linked to worse overall survival (OS) and immune infiltration. FOXK2 has the potential to be used as a prognostic indicator and target for treatment in individuals with cancer.

CONCLUSION

Our research provides insights into the significance of FOXK2 in cancer and indicates its potential as both a prognostic indicator and target for treatment. The ribosome-associated pathways involving FOXK2 and FBXO32 could be pivotal in the advancement of tumors, offering possible avenues for targeted and individualized immunotherapy approaches. Additional research is required to completely understand the mechanisms that are responsible for the participation of FOXK2 and its subsequent gene FBXO32 in cancer, as well as to explore the possible advantages of focusing on FOXK2 for cancer treatment.

Advances in therapeutic cancer vaccines: Harnessing immune adjuvants for enhanced efficacy and future perspectives

Preventive cancer vaccines are highly effective in preventing viral infection-induced cancer, but advances in therapeutic cancer vaccines with a focus on eliminating cancer cells through immunotherapy are limited. To develop therapeutic cancer vaccines, the integration of optimal adjuvants is a potential strategy to enhance or complement existing therapeutic approaches. However, conventional adjuvants do not satisfy the criteria of clinical trials for therapeutic cancer vaccines. To improve the effects of adjuvants in therapeutic cancer vaccines, effective vaccination strategies must be formulated and novel adjuvants must be identified. This review offers an overview of the current advancements in therapeutic cancer vaccines and highlights in situ vaccination approaches that can be synergistically combined with other immunotherapies by harnessing the adjuvant effects. Additionally, the refinement of adjuvant systems using cutting-edge technologies and the elucidation of molecular mechanisms underlying immunogenic cell death to facilitate the development of innovative adjuvants have been discussed.

Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis

Background

Inflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis.

Methods

We retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm.

Findings

The model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line.

Conclusion

In this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.

Investigating the role of senescence biomarkers in colorectal cancer heterogeneity by bulk and single-cell RNA sequencing

Colorectal cancer (CRC) is one of most common tumors worldwide, causing a prominent global health burden. Cell senescence is a complex physiological state, characterized by proliferation arrest. Here, we investigated the role of cellular senescence in the heterogeneity of CRC. Based on senescence-associated genes, CRC samples were classified into different senescence patterns with different survival, cancer-related biological processes and immune cell infiltrations. A senescence-related model was then developed to calculate the senescence-related score to comprehensively explore the heterogeneity of each CRC sample such as stromal activities, immunoreactivities and drug sensitivity. Single-cell analysis revealed there were different immune cell infiltrations between low and high senescence-related model genes enrichment groups, which was confirmed by multiplex immunofluorescence staining. Pseudotime analysis indicated model genes play a pivotal role in the evolution of B cells. Besides, intercellular communication modeled by NicheNet showed tumor cells with higher enrichment of senescence-related model genes highly expressed CXCL2/3 and CCL3/4, which attracted immunosuppressive cell infiltration and promoted tumor metastasis. Finally, top 6 hub genes were identified from senescence-related model genes by PPI analysis. And RT-qPCR revealed the expression differences of hub genes between normal and CRC cell lines, indicating to some extent the clinical practicability of senescence-related model. To sum up, our study explores the impact of cellular senescence on the prognosis, TME and treatment of CRC based on senescence patterns. This provides a new perspective for CRC treatment.

Establish TIIC signature score based the machine learning fusion in bladder cancer

BACKGROUND

Bladder cancer is a prevalent malignant tumor with high heterogeneity. Current treatments, such as transurethral resection of bladder tumor (TURBT) and intravesical Bacillus Calmette-Guérin (BCG) therapy, still have limitations, with approximately 30% of non-muscle-invasive bladder cancer (NMIBC) progressing to muscle-invasive bladder cancer (MIBC), and a substantial number of MIBC patients experiencing recurrence after surgery. Immunotherapy has shown potential benefits, but accurate prediction of its prognostic effects remains challenging.

METHODS

We analyzed bladder cancer RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and used various machine learning algorithms to screen for feature RNAs related to tumor-infiltrating immune cells (TIICs) from single-cell data. Based on these RNAs, we established a TIIC signature score and evaluated its relationship with overall survival (OS) and immunotherapy response in bladder cancer patients.

RESULTS

The study identified 171 TIIC-RNAs and selected 11 TIIC-RNAs with prognostic value through survival analysis. The TIIC signature score established using a machine learning fusion method was significantly associated with OS and showed good predictive performance in different datasets. Additionally, the signature score was negatively correlated with immunotherapy response, with patients with low TIIC feature scores showing better survival outcomes after immunotherapy. Further biological functional analysis revealed a close association between the TIIC signature score and immune regulation processes, cellular metabolism, and genetic variations.

CONCLUSION

This study successfully constructed and validated an RNA signature scoring system based on tumor-infiltrating immune cell (TIIC) features, which can effectively predict OS and the effectiveness of immunotherapy in bladder cancer patients.

Bioinformatics analysis of the tumor microenvironment in melanoma - Constructing a prognostic model based on CD8+ T cell-related genes: An observational study

This research endeavor seeks to explore the microenvironment of melanoma tumors and construct a prognostic model by focusing on genes specific to CD8+ T cells. The single-cell sequencing data of melanoma underwent processing with the Seurat package, subsequent to which cell communication network analysis was conducted using the iTALK package and transcription factor analysis was performed using the SCENIC package. Univariate COX and LASSO regression analyses were utilized to pinpoint genes linked to the prognosis of melanoma patients, culminating in the creation of a prognostic model through multivariate COX analysis. The model was validated using the GSE65904 and GSE35640 datasets. Multi-omics analysis was conducted utilizing the maftools, limma, edgeR, ChAMP, and clusterProfiler packages. The examination of single-cell sequencing data revealed the presence of 8 cell types, with the transcription factors RFXAP, CLOCK, MGA, RBBP, and ZNF836 exhibiting notably high expression levels in CD8+ T cells as determined by the SCENIC package. Utilizing these transcription factors and their associated target genes, a prognostic model was developed through COX and LASSO analyses, incorporating the genes GPR171, FAM174A, and BPI. This study validated the model with independent datasets and conducted additional analysis involving multi-omics and immune infiltration to identify a more favorable prognosis for patients in the low-risk group. The findings provide valuable insights into the tumor microenvironment of melanoma and establish a reliable prognostic model. The integration of multi-omics and immune infiltration analyses enhances our understanding of the pathogenesis of melanoma. The identification of specific genes holds promise as potential biomarkers for individuals with melanoma, serving as important indicators for predicting patient outcomes and determining their response to immunotherapy.

Next-generation cancer vaccines and emerging immunotherapy combinations

Therapeutic cancer vaccines have been a subject of research for several decades as potential new weapons to tackle malignancies. Their goal is to induce a long-lasting and efficient antitumour-directed immune response, capable of mediating tumour regression, preventing tumour progression, and eradicating minimal residual disease, while avoiding major adverse effects. Development of new vaccine technologies and antigen prediction methods has led to significant improvements in cancer vaccine efficacy. However, for their successful clinical application, certain obstacles still need to be overcome, especially tumour-mediated immunosuppression and escape mechanisms. In this review, we introduce therapeutic cancer vaccines and subsequently discuss combination approaches of next-generation cancer vaccines and existing immunotherapies, particularly immune checkpoint inhibitors (ICIs) and adoptive cell transfer/cell-based immunotherapies.

Identification and validation of tryptophan-related gene signatures to predict prognosis and immunotherapy response in lung adenocarcinoma reveals a critical role for PTTG1

Introduction

Tryptophan metabolism is strongly associated with immunosuppression and may influence lung adenocarcinoma prognosis as well as tumor microenvironment alterations.

Methods

Sequencing datasets were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Two different clusters were identified by consensus clustering, and prognostic models were established based on differentially expressed genes (DEGs) in the two clusters. We investigated differences in mutational landscapes, enrichment pathways, immune cell infiltration, and immunotherapy between high- and low-risk scoring groups. Single-cell sequencing data from Bischoff et al. were used to identify and quantify tryptophan metabolism, and model genes were comprehensively analyzed. Finally, PTTG1 was analyzed at the pan-cancer level by the pan-TCGA cohort.

Results

Risk score was defined as an independent prognostic factor for lung adenocarcinoma and was effective in predicting immunotherapy response in patients with lung adenocarcinoma. PTTG1 is one of the key genes, and knockdown of PTTG1 in vitro decreases lung adenocarcinoma cell proliferation and migration and promotes apoptosis and down-regulation of tryptophan metabolism regulators in lung adenocarcinoma cells.

Discussion

Our study revealed the pattern and molecular features of tryptophan metabolism in lung adenocarcinoma patients, established a model of tryptophan metabolism-associated lung adenocarcinoma prognosis, and explored the roles of PTTG1 in lung adenocarcinoma progression, EMT process, and tryptophan metabolism.

Comprehensive Analysis of a Six-Gene Signature Predicting Survival and Immune Infiltration of Liposarcoma Patients and Deciphering Its Therapeutic Significance

BACKGROUND

As a common soft tissue sarcoma, liposarcoma (LPS) is a heterogeneous malignant tumor derived from adipose tissue. Due to the high risk of metastasis and recurrence, the prognosis of LPS remains unfavorable. To improve clinical treatment, a robust risk prediction model is essential to evaluate the prognosis of LPS patients.

METHODS

By comprehensive analysis of data derived from GEO datasets, differentially expressed genes (DEGs) were obtained. Univariate and Lasso Cox regressions were subsequently employed to reveal distant recurrence-free survival (DRFS)-associated DEGs and develop a prognostic gene signature, which was assessed by Kaplan-Meier survival and ROC curve. GSEA and immune infiltration analyses were conducted to illuminate molecular mechanisms and immune correlations of this model in LPS progression. Furthermore, a correlation analysis was involved to decipher the therapeutic significance of this model for LPS.

RESULTS

A six-gene signature was developed to predict DRFS of LPS patients and showed higher precision performance in more aggressive LPS subtypes. Then, a nomogram was further established for clinical application based on this risk model. Via GSEA, the high-risk group was significantly enriched in cell cycle-related pathways. In the LPS microenvironment, neutrophils, memory B cells and resting mast cells exhibited significant differences in cell abundance between high-risk and low-risk patients. Moreover, this model was significantly correlated with therapeutic targets.

CONCLUSION

A prognostic six-gene signature was developed and significantly associated with cell cycle pathways and therapeutic target genes, which could provide new insights into risk assessment of LPS progression and therapeutic strategies for LPS patients to improve their prognosis.

LRP1 induces anti-PD-1 resistance by modulating the DLL4-NOTCH2-CCL2 axis and redirecting M2-like macrophage polarisation in bladder cancer

The tumour microenvironment (TME) drives bladder cancer (BLCA) progression. Targeting the TME has emerged as a promising strategy for BLCA treatment in recent years. Furthermore, checkpoint blockade therapies are only beneficial for a minority of patients with BLCA, and drug resistance is a barrier to achieving significant clinical effects of anti-programmed cell death protein-1 (PD-1)/programmed death protein ligand-1 (PD-L1) therapy. In this study, higher low-density lipoprotein receptor-related protein 1 (LRP1) levels were related to a poorer prognosis for patients with various cancers, including those with higher grades and later stages of BLCA. Enrichment analysis demonstrated that LRP1 plays a role in the epithelial-mesenchymal transition (EMT), NOTCH signalling pathway, and ubiquitination. LRP1 knockdown in BLCA cells delayed BLCA progression both in vivo and in vitro. Furthermore, LRP1 knockdown suppressed EMT, reduced DLL4-NOTCH2 signalling activity, and downregulated M2-like macrophage polarisation. Patients with BLCA and higher LRP1 levels responded weakly to anti-PD-1 therapy in the IMvigor210 cohort. Moreover, LRP1 knockdown enhanced the therapeutic effects of anti-PD-1 in mice. Taken together, our findings suggest that LRP1 is a potential target for improving the efficacy of anti-PD-1/PD-L1 therapy by preventing EMT and M2-like macrophage polarisation by blocking the DLL4-NOTCH2 axis.

TimiGP-Response: the pan-cancer immune landscape associated with response to immunotherapy

Accumulating evidence suggests that the tumor immune microenvironment (TIME) significantly influences the response to immunotherapy, yet this complex relationship remains elusive. To address this issue, we developed TimiGP-Response (TIME Illustration based on Gene Pairing designed for immunotherapy Response), a computational framework leveraging single-cell and bulk transcriptomic data, along with response information, to construct cell-cell interaction networks associated with responders and estimate the role of immune cells in treatment response. This framework was showcased in triple-negative breast cancer treated with immune checkpoint inhibitors targeting the PD-1:PD-L1 interaction, and orthogonally validated with imaging mass cytometry. As a result, we identified CD8+ GZMB+ T cells associated with responders and its interaction with regulatory T cells emerged as a potential feature for selecting patients who may benefit from these therapies. Subsequently, we analyzed 3,410 patients with seven cancer types (melanoma, non-small cell lung cancer, renal cell carcinoma, metastatic urothelial carcinoma, hepatocellular carcinoma, breast cancer, and esophageal cancer) treated with various immunotherapies and combination therapies, as well as several chemo- and targeted therapies as controls. Using TimiGP-Response, we depicted the pan-cancer immune landscape associated with immunotherapy response at different resolutions. At the TIME level, CD8 T cells and CD4 memory T cells were associated with responders, while anti-inflammatory (M2) macrophages and mast cells were linked to non-responders across most cancer types and datasets. Given that T cells are the primary targets of these immunotherapies and our TIME analysis highlights their importance in response to treatment, we portrayed the pan-caner landscape on 40 T cell subtypes. Notably, CD8+ and CD4+ GZMK+ effector memory T cells emerged as crucial across all cancer types and treatments, while IL-17-producing CD8+ T cells were top candidates associated with immunotherapy non-responders. In summary, this study provides a computational method to study the association between TIME and response across the pan-cancer immune landscape, offering resources and insights into immune cell interactions and their impact on treatment efficacy.

Deciphering lung adenocarcinoma evolution: Integrative single-cell genomics identifies the prognostic lung progression associated signature

We employed single-cell analysis techniques, specifically the inferCNV method, to dissect the complex progression of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) through minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IAC). This approach enabled the identification of Cluster 6, which was significantly associated with LUAD progression. Our comprehensive analysis included intercellular interaction, transcription factor regulatory networks, trajectory analysis, and gene set variation analysis (GSVA), leading to the development of the lung progression associated signature (LPAS). Interestingly, we discovered that the LPAS not only accurately predicts the prognosis of LUAD patients but also forecasts genomic alterations, distinguishes between 'cold' and 'hot' tumours, and identifies potential candidates suitable for immunotherapy. PSMB1, identified within Cluster 6, was experimentally shown to significantly enhance cancer cell invasion and migration, highlighting the clinical relevance of LPAS in predicting LUAD progression and providing a potential target for therapeutic intervention. Our findings suggest that LPAS offers a novel biomarker for LUAD patient stratification, with significant implications for improving prognostic accuracy and guiding treatment decisions.

Deciphering the heterogeneity dominated by tumor-associated macrophages for survival prognostication and prediction of immunotherapy response in lung adenocarcinoma

Tumor-associated macrophages (TAMs) are a specific subset of macrophages that reside inside the tumor microenvironment. The dynamic interplay between TAMs and tumor cells plays a crucial role in the treatment response and prognosis of lung adenocarcinoma (LUAD). The study aimed to examine the association between TAMs and LUAD to advance the development of targeted strategies and immunotherapeutic approaches for treating this type of lung cancer. The study employed single-cell mRNA sequencing data to characterize the immune cell composition of LUAD and delineate distinct subpopulations of TAMs. The "BayesPrism" and "Seurat" R packages were employed to examine the association between these subgroups and immunotherapy and clinical features to identify novel immunotherapy biomarkers. Furthermore, a predictive signature was generated to forecast patient prognosis by examining the gene expression profile of immunotherapy-associated TAMs subsets and using 104 machine-learning techniques. A comprehensive investigation has shown the existence of a hitherto unidentified subgroup of TAMs known as RGS1 + TAMs, which has been found to have a strong correlation with the efficacy of immunotherapy and the occurrence of tumor metastasis in LUAD patients. CD83 was identified CD83 as a distinct biomarker for the expression of RGS1 + TAMs, showcasing its potential utility as an indicator for immunotherapeutic interventions. Furthermore, the prognostic capacity of the RTMscore signature, encompassing three specific mRNA (NR4A2, MMP14, and NPC2), demonstrated enhanced robustness when contrasted against the comprehensive collection of 104 features outlined in the published study. CD83 has potential as an immunotherapeutic biomarker. Meanwhile, The RTMscore signature established in the present study might be beneficial for survival prognostication.

Purine metabolism in lung adenocarcinoma: A single-cell analysis revealing prognostic and immunotherapeutic insights

Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer, yet the contribution of purine metabolism (PM) to its pathogenesis remains poorly elucidated. PM, a critical component of intracellular nucleotide synthesis and energy metabolism, is hypothesized to exert a significant influence on LUAD development. Herein, we employed single-cell analysis to investigate the role of PM within the tumour microenvironment (TME) of LUAD. PM scoring (PMS) across distinct cell types was determined using AUCell, UCell, singscore and AddModuleScore algorithms. Subsequently, we explored communication networks among cells within high- and low-PMS groups, establishing a robust PM-associated signature (PAS) utilizing a comprehensive dataset comprising LUAD samples from TCGA and five GEO datasets. Our findings revealed that the high-PMS group exhibited intensified cell interactions, while the PAS, constructed using PM-related genes, demonstrated precise prognostic predictive capability. Notably, analysis across the TCGA dataset and five GEO datasets indicated that low-PAS patients exhibited a superior prognosis. Furthermore, the low-PAS group displayed increased immune cell infiltration and elevated CD8A expression, coupled with reduced PD-L1 expression. Moreover, data from eight publicly available immunotherapy cohorts suggested enhanced immunotherapy outcomes in the low-PAS group. These results underscore a close association between PAS and tumour immunity, offering predictive insights into genomic alterations, chemotherapy drug sensitivity and immunotherapy responses in LUAD. The newly established PAS holds promise as a valuable tool for selecting LUAD populations likely to benefit from future clinical stratification efforts.

Cuproptosis in glioblastoma: unveiling a novel prognostic model and therapeutic potential

Glioblastoma, a notably aggressive brain tumor, is characterized by a brief survival period and resistance to conventional therapeutic approaches. With the recent identification of "Cuproptosis," a copper-dependent apoptosis mechanism, this study aimed to explore its role in glioblastoma prognosis and potential therapeutic implications. A comprehensive methodology was employed, starting with the identification and analysis of 65 cuproptosis-related genes. These genes were subjected to differential expression analyses between glioblastoma tissues and normal counterparts. A novel metric, the "CP-score," was devised to quantify the cuproptosis response in glioblastoma patients. Building on this, a prognostic model, the CP-model, was developed using Cox regression techniques, designed to operate on both bulk and single-cell data. The differential expression analysis revealed 31 genes with distinct expression patterns in glioblastoma. The CP-score was markedly elevated in glioblastoma patients, suggesting an intensified cuproptosis response. The CP-model adeptly stratified patients into distinct risk categories, unveiling intricate associations between glioblastoma prognosis, immune response pathways, and the tumor's immunological environment. Further analyses indicated that high-risk patients, as per the CP-model, exhibited heightened expression of certain immune checkpoints, suggesting potential therapeutic targets. Additionally, the model hinted at the possibility of personalized therapeutic strategies, with certain drugs showing increased efficacy in high-risk patients. The CP-model offers a promising tool for glioblastoma prognosis and therapeutic strategy development, emphasizing the potential of Cuproptosis in cancer treatment.

Single-cell RNA sequencing integrated with bulk RNA sequencing analysis identifies a tumor immune microenvironment-related lncRNA signature in lung adenocarcinoma

BACKGROUND

Recently, long non-coding RNAs (lncRNAs) have been demonstrated as essential roles in tumor immune microenvironments (TIME). Nevertheless, researches on the clinical significance of TIME-related lncRNAs are limited in lung adenocarcinoma (LUAD).

METHODS

Single-cell RNA sequencing and bulk RNA sequencing data are integrated to identify TIME-related lncRNAs. A total of 1368 LUAD patients are enrolled from 6 independent datasets. An integrative machine learning framework is introduced to develop a TIME-related lncRNA signature (TRLS).

RESULTS

This study identified TIME-related lncRNAs from integrated analysis of single‑cell and bulk RNA sequencing data. According to these lncRNAs, a TIME-related lncRNA signature was developed and validated from an integrative procedure in six independent cohorts. TRLS exhibited a robust and reliable performance in predicting overall survival. Superior prediction performance barged TRLS to the forefront from comparison with general clinical features, molecular characters, and published signatures. Moreover, patients with low TRLS displayed abundant immune cell infiltration and active lipid metabolism, while patients with high TRLS harbored significant genomic alterations, high PD-L1 expression, and elevated DNA damage repair (DDR) relevance. Notably, subclass mapping analysis of nine immunotherapeutic cohorts demonstrated that patients with high TRLS were more sensitive to immunotherapy.

CONCLUSIONS

This study developed a promising tool based on TIME-related lncRNAs, which might contribute to tailored treatment and prognosis management of LUAD patients.

Multiomics characterization of pyroptosis in the tumor microenvironment and therapeutic relevance in metastatic melanoma

BACKGROUND

Pyroptosis, mediated by gasdermins with the release of multiple inflammatory cytokines, has emerged as playing an important role in targeted therapy and immunotherapy due to its effectiveness at inhibiting tumor growth. Melanoma is one of the most commonly used models for immunotherapy development, though an inadequate immune response can occur. Moreover, the development of pyroptosis-related therapy and combinations with other therapeutic strategies is limited due to insufficient understanding of the role of pyroptosis in the context of different tumor immune microenvironments (TMEs).

METHODS

Here, we present a computational model (pyroptosis-related gene score, PScore) to assess the pyroptosis status. We applied PScore to 1388 melanoma samples in our in-house cohort and eight other publicly available independent cohorts and then calculated its prognostic power of and potential as a predictive marker of immunotherapy efficacy. Furthermore, we performed association analysis for PScore and the characteristics of the TME by using bulk, single-cell, and spatial transcriptomics and assessed the association of PScore with mutation status, which contributes to targeted therapy.

RESULTS

Pyroptosis-related genes (PRGs) showed distinct expression patterns and prognostic predictive ability in melanoma. Most PRGs were associated with better survival in metastatic melanoma. Our PScore model based on genes associated with prognosis exhibits robust performance in survival prediction in multiple metastatic melanoma cohorts. We also found PScore to be associated with BRAF mutation and correlate positively with multiple molecular signatures, such as KRAS signaling and the IFN gamma response pathway. Based on our data, melanoma with an immune-enriched TME had a higher PScore than melanoma with an immune-depleted or fibrotic TME. Additionally, monocytes had the highest PScore and malignant cells and fibroblasts the lowest PScore based on single-cell and spatial transcriptome analyses. Finally, a higher PScore was associated with better therapeutic efficacy of immune checkpoint blockade, suggesting the potential of pyroptosis to serve as a marker of immunotherapy response.

CONCLUSIONS

Collectively, our findings indicate that pyroptosis is a prognostic factor and is associated with the immune response in metastatic melanoma, as based on multiomics data. Our results provide a theoretical basis for drug combination and reveal potential immunotherapy response markers.

Construction of an ER stress-related prognostic signature for predicting prognosis and screening the effective anti-tumor drug in osteosarcoma

BACKGROUND

Osteosarcoma is the most common malignant primary bone tumor in infants and adolescents. The lack of understanding of the molecular mechanisms underlying osteosarcoma progression and metastasis has contributed to a plateau in the development of current therapies. Endoplasmic reticulum (ER) stress has emerged as a significant contributor to the malignant progression of tumors, but its potential regulatory mechanisms in osteosarcoma progression remain unknown.

METHODS

In this study, we collected RNA sequencing and clinical data of osteosarcoma from The TCGA, GSE21257, and GSE33382 cohorts. Differentially expressed analysis and the least absolute shrinkage and selection operator regression analysis were conducted to identify prognostic genes and construct an ER stress-related prognostic signature (ERSRPS). Survival analysis and time dependent ROC analysis were performed to evaluate the predictive performance of the constructed prognostic signature. The "ESTIMATE" package and ssGSEA algorithm were utilized to evaluate the differences in immune cells infiltration between the groups. Cell-based assays, including CCK-8, colony formation, and transwell assays and co-culture system were performed to assess the effects of the target gene and small molecular drug in osteosarcoma. Animal models were employed to assess the anti-osteosarcoma effects of small molecular drug.

RESULTS

Five genes (BLC2, MAGEA3, MAP3K5, STC2, TXNDC12) were identified to construct an ERSRPS. The ER stress-related gene Stanniocalcin 2 (STC2) was identified as a risk gene in this signature. Additionally, STC2 knockdown significantly inhibited osteosarcoma cell proliferation, migration, and invasion. Furthermore, the ER stress-related gene STC2 was found to downregulate the expression of MHC-I molecules in osteosarcoma cells, and mediate immune responses through influencing the infiltration and modulating the function of CD8+ T cells. Patients categorized by risk scores showed distinct immune status, and immunotherapy response. ISOX was subsequently identified and validated as an effective anti-osteosarcoma drug through a combination of CMap database screening and in vitro and in vivo experiments.

CONCLUSION

The ERSRPS may guide personalized treatment decisions for osteosarcoma, and ISOX holds promise for repurposing in osteosarcoma treatment.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Single-cell transcriptomics reveals heterogeneity in esophageal squamous epithelial cells and constructs models for predicting patient prognosis and immunotherapy

Background

Esophageal squamous cell carcinoma (ESCC), characterized by its high invasiveness and malignant potential, has long been a formidable challenge in terms of treatment.

Methods

A variety of advanced analytical techniques are employed, including single-cell RNA sequencing (scRNA-seq), cell trajectory inference, transcription factor regulatory network analysis, GSVA enrichment analysis, mutation profile construction, and the inference of potential immunotherapeutic drugs. The purpose is to conduct a more comprehensive exploration of the heterogeneity among malignant squamous epithelial cell subgroups within the ESCC microenvironment and establish a model for predicting the prognosis and immunotherapy outcomes of ESCC patients.

Results

An analysis was conducted through scRNA-seq, and three Cluster of malignant epithelial cells were identified using the infer CNV method. Cluster 0 was found to exhibit high invasiveness, whereas Cluster 1 displayed prominent characteristics associated with epithelial-mesenchymal transition. Confirmation of these findings was provided through cell trajectory analysis, which positioned Cluster 0 at the initiation stage of development and Cluster 1 at the final developmental stage. The abundance of Cluster 0-2 groups in TCGA-LUAD samples was assessed using ssGSEA and subsequently categorized into high and low-expression groups. Notably, it was observed that Cluster 0-1 had a significant impact on survival (p<0.05). Furthermore, GSVA enrichment analysis demonstrated heightened activity in hallmark pathways for Cluster 0, whereas Cluster 1 exhibited notable enrichment in pathways related to cell proliferation. It is noteworthy that a prognostic model was established utilizing feature genes from Cluster 0-1, employing the Lasso and stepwise regression methods. The results revealed that in TCGA and GSE53624 cohorts, the low-risk group demonstrated significantly higher overall survival and increased levels of immune infiltration. An examination of four external immunotherapy cohorts unveiled that the low-risk group exhibited improved immunotherapeutic efficacy. Additionally, more meaningful treatment options were identified for the low-risk group.

Conclusion

The findings revealed distinct interactions between malignant epithelial cells of ESCC and subgroups within the tumor microenvironment. Two cell clusters, strongly linked to survival, were pinpointed, and a signature was formulated. This signature is expected to play a crucial role in identifying and advancing precision medicine approaches for the treatment of ESCC.

Combination of tumor asphericity and an extracellular matrix-related prognostic gene signature in non-small cell lung cancer patients

One important aim of precision oncology is a personalized treatment of patients. This can be achieved by various biomarkers, especially imaging parameters and gene expression signatures are commonly used. So far, combination approaches are sparse. The aim of the study was to independently validate the prognostic value of the novel positron emission tomography (PET) parameter tumor asphericity (ASP) in non small cell lung cancer (NSCLC) patients and to investigate associations between published gene expression profiles and ASP. This was a retrospective evaluation of PET imaging and gene expression data from three public databases and two institutional datasets. The whole cohort comprised 253 NSCLC patients, all treated with curative intent surgery. Clinical parameters, standard PET parameters and ASP were evaluated in all patients. Additional gene expression data were available for 120 patients. Univariate Cox regression and Kaplan-Meier analysis was performed for the primary endpoint progression-free survival (PFS) and additional endpoints. Furthermore, multivariate cox regression testing was performed including clinically significant parameters, ASP, and the extracellular matrix-related prognostic gene signature (EPPI). In the whole cohort, a significant association with PFS was observed for ASP (p < 0.001) and EPPI (p = 0.012). Upon multivariate testing, EPPI remained significantly associated with PFS (p = 0.018) in the subgroup of patients with additional gene expression data, while ASP was significantly associated with PFS in the whole cohort (p = 0.012). In stage II patients, ASP was significantly associated with PFS (p = 0.009), and a previously published cutoff value for ASP (19.5%) was successfully validated (p = 0.008). In patients with additional gene expression data, EPPI showed a significant association with PFS, too (p = 0.033). The exploratory combination of ASP and EPPI showed that the combinatory approach has potential to further improve patient stratification compared to the use of only one parameter. We report the first successful validation of EPPI and ASP in stage II NSCLC patients. The combination of both parameters seems to be a very promising approach for improvement of risk stratification in a group of patients with urgent need for a more personalized treatment approach.

Multivalent tyrosine kinase inhibition promotes T cell recruitment to immune-desert gastric cancers by restricting epithelial-mesenchymal transition via tumour-intrinsic IFN-γ signalling

OBJECTIVE

Gastric cancer (GC) ranks fifth in incidence and fourth for mortality worldwide. The response to immune checkpoint blockade (ICB) therapy in GC is heterogeneous due to tumour-intrinsic and acquired immunotherapy resistance. We developed an immunophenotype-based subtyping of human GC based on immune cells infiltration to develop a novel treatment option.

DESIGN

A algorithm was developed to reclassify GC into immune inflamed, excluded and desert subtypes. Bioinformatics, human and mouse GC cell lines, syngeneic murine gastric tumour model, and CTLA4 blockade were used to investigate the immunotherapeutic effects by restricting receptor tyrosine kinase (RTK) signalling in immune desert (ICB-resistant) type GC.

RESULTS

Our algorithm restratified subtypes of human GC in public databases and showed that immune desert-type and excluded-type tumours are ICB-resistant compared with immune-inflamed GC. Moreover, epithelial-mesenchymal transition (EMT) signalling was highly enriched in immune desert-type GC, and syngeneic murine tumours exhibiting mesenchymal-like, compared with epithelial-like, properties are T cell-excluded and resistant to CTLA4 blockade. Our analysis further identified a panel of RTKs as potential druggable targets in the immune desert-type GC. Dovitinib, an inhibitor of multiple RTKs, strikingly repressed EMT programming in mesenchymal-like immune desert syngeneic GC models. Dovitinib activated the tumour-intrinsic SNAI1/2-IFN-γ signalling axis and impeded the EMT programme, converting immune desert-type tumours to immune inflamed-type tumours, sensitising these mesenchymal-like 'cold' tumours to CTLA4 blockade.

CONCLUSION

Our findings identified potential druggable targets relevant to patient groups, especially for refractory immune desert-type/ 'cold' GC. Dovitinib, an RTK inhibitor, sensitised desert-type immune-cold GC to CTLA4 blockade by restricting EMT and recruiting T cells.

Identifying immune checkpoint-related lncRNA biomarkers for immunotherapy response and prognosis in cancers

Long non-coding RNAs (lncRNAs) could modulate expression of immune checkpoints (ICPs) in tumor-immune. However, precise functions in immunity and potential for predicting ICP inhibitors (ICI) response have been described for only a few lncRNAs. Here, a multiple-step pipeline was developed to identify cancer- and immune-context ICP and lncRNA cooperative regulation pairs (ICPaLncCRPs) across cancers. Immune-related ICPs and lncRNAs were extracted follow immune cell lines and immunologic constant of rejection groups. ICPaLncCRP networks were constructed, which likely to modulate tumor-immune by specific patterns. Common and specific hub ICPaLncs such as MIR155HG, TRG-AS1 and PCED1B-AS1 maybe play central roles in prognosis and circulating. Moreover, these hub ICPaLncs were significantly correlated with immune cell infiltration based on bulk and single-cell RNA sequencing data. Some ICPaLncCRPs such as IDO1-MIR155HG could predict three- and five-year prognosis of melanoma in two independent datasets. We also validated that some ICPaLncCRPs could effectively predict ICI-response follow six independent datasets. Collectively, this study will enhance our understanding of lncRNA functions and accelerate discovery of lncRNA-based biomarkers in ICI treatment.

Machine learning-derived identification of tumor-infiltrating immune cell-related signature for improving prognosis and immunotherapy responses in patients with skin cutaneous melanoma

BACKGROUND

Immunoblockade therapy based on the PD-1 checkpoint has greatly improved the survival rate of patients with skin cutaneous melanoma (SKCM). However, existing anti-PD-1 therapeutic efficacy prediction markers often exhibit a poor situation of poor reliability in identifying potential beneficiary patients in clinical applications, and an ideal biomarker for precision medicine is urgently needed.

METHODS

10 multicenter cohorts including 4 SKCM cohorts and 6 immunotherapy cohorts were selected. Through the analysis of WGCNA, survival analysis, consensus clustering, we screened 36 prognostic genes. Then, ten machine learning algorithms were used to construct a machine learning-derived immune signature (MLDIS). Finally, the independent data sets (GSE22153, GSE54467, GSE59455, and in-house cohort) were used as the verification set, and the ROC index standard was used to evaluate the model.

RESULTS

Based on computing framework, we found that patients with high MLDIS had poor overall survival and has good prediction performance in all cohorts and in-house cohort. It is worth noting that MLDIS performs better in each data set than almost all models which from 51 prognostic signatures for SKCM. Meanwhile, high MLDIS have a positive prognostic impact on patients treated with anti-PD-1 immunotherapy by driving changes in the level of infiltration of immune cells in the tumor microenvironment. Additionally, patients suffering from SKCM with high MLDIS were more sensitive to immunotherapy.

CONCLUSIONS

Our study identified that MLDIS could provide new insights into the prognosis of SKCM and predict the immunotherapy response in patients with SKCM.

A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers

Background: The tumor-associated endothelial cell (TAE) component plays a vital role in tumor immunity. However, systematic tumor-associated endothelial-related gene assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers have not been explored. Herein, we investigated a TAE gene risk model to predict CIT responses and patient survival in a pan-cancer analysis. Methods: We analyzed publicly available datasets of tumor samples with gene expression and clinical information, including gastric cancer, metastatic urothelial cancer, metastatic melanoma, non-small cell lung cancer, primary bladder cancer, and renal cell carcinoma. We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. Results: The model demonstrated a high predictive accuracy in both training and validation cohorts. The response rate of the high score group to immunotherapy in the training cohort was significantly higher than that of the low score group, with CIT response rates of 51% and 27%, respectively. The survival analysis showed that the prognosis of the high score group was significantly better than that of the low score group (all p < 0·001). Tumor-associated endothelial gene signature scores positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may benefit patients in the high score group. The analysis of TAE scores across 33 human cancers revealed that the TAE model could reflect immune cell infiltration and predict the survival of cancer patients. Conclusion: The TAE signature model could represent a CIT response prediction model with a prognostic value in multiple cancer types.

Artificial intelligence learning landscape of triple-negative breast cancer uncovers new opportunities for enhancing outcomes and immunotherapy responses

Triple-negative breast cancer (TNBC) is a relatively aggressive breast cancer subtype due to tumor relapse, drug resistance, and multi-organ metastatic properties. Identifying reliable biomarkers to predict prognosis and precisely guide TNBC immunotherapy is still an unmet clinical need. To address this issue, we successfully constructed a novel 25 machine learning (ML) algorithms-based immune infiltrating cell (IIC) associated signature of TNBC (MLIIC), achieved by multiple transcriptome data of purified immune cells, TNBC cell lines, and TNBC entities. The TSI index was employed to determine IIC-RNAs that were accompanied by an expression pattern of upregulation in immune cells and downregulation in TNBC cells. LassoLR, Boruta, Xgboost, SVM, RF, and Pamr were utilized for further obtaining the optimal IIC-RNAs. Following univariate Cox regression analysis, LassoCox, CoxBoost, and RSF were utilized for the dimensionality reduction of IIC-RNAs from a prognostic perspective. RSF, Ranger, ObliqueRSF, Rpart, CoxPH, SurvivalSVM, CoxBoost, GlmBoost, SuperPC, StepwiseCox, Enet, LassoCox, CForest, Akritas, BlackBoost, PlsRcox, SurvReg, GBM, and CTree were used for determining the most potent MLIIC signature. Consequently, this MLIIC signature was correlated significantly with survival status validated by four independent TNBC cohorts. Also, the MLIIC signature had a superior predictive capability for TNBC prognosis, compared with 148 previously reported signatures. In addition, MLIIC signature scores developed by immunofluorescent staining of tissue arrays from TNBC patients showed a substantial prognostic value. In TNBC immunotherapy, the low MLIIC profile demonstrated significant immune-responsive efficacy in a dataset of multiple cancer types. MLIIC signature could also predict m6A epigenetic regulation which controls T cell homeostasis. Therefore, this well-established MLIIC signature is a robust predictive indicator for TNBC prognosis and the benefit of immunotherapy, thus providing an efficient tool for combating TNBC.

Immune-related interaction perturbation networks unravel biological peculiars and clinical significance of glioblastoma

The immune system is an interacting network of plentiful molecules that could better characterize the relationship between immunity and cancer. This study aims to investigate the behavioral patterns of immune-related interaction perturbation networks in glioblastoma. An immune-related interaction-perturbation framework was introduced to characterize four heterogeneous subtypes using RNA-seq data of TCGA/CGGA glioblastoma tissues and GTEx normal brain tissues. The stability and robustness of the four subtypes were validated in public datasets and our in-house cohort. In the four subtypes, C1 was an inflammatory subtype with high immune infiltration, low tumor purity, and potential response to immunotherapy; C2, an invasive subtype, was featured with dismal prognosis, telomerase reverse transcriptase promoter mutations, moderate levels of immunity, and stromal constituents, as well as sensitivity to receptor tyrosine kinase signaling inhibitors; C3 was a proliferative subtype with high tumor purity, immune-desert microenvironment, sensitivity to phosphatidylinositol 3'-kinase signaling inhibitor and DNA replication inhibitors, and potential resistance to immunotherapy; C4, a synaptogenesis subtype with the best prognosis, exhibited high synaptogenesis-related gene expression, prevalent isocitrate dehydrogenase mutations, and potential sensitivity to radiotherapy and chemotherapy. Overall, this study provided an attractive platform from the perspective of immune-related interaction perturbation networks, which might advance the tailored management of glioblastoma.

Identification of the relationship between single-cell N6-methyladenosine regulators and the infiltrating immune cells in esophageal carcinoma

Background

N6-methyladenosine (m⁶A) RNA methylation plays a crucial role in important genomic processes in a variety of malignancies. However, the characterization of m⁶A with infiltrating immune cells in the tumor microenvironment (TME) in esophageal squamous carcinoma (ESCC) remains unknown.

Methods

The single-cell transcriptome data from five ESCC patients in our hospital were analyzed, and TME clusters associated with prognosis and immune checkpoint genes were investigated. Cell isolation and qPCR were conducted to validate the gene characterization in different cells.

Results

According to distinct biological processes and marker genes, macrophages, T cells, and B cells clustered into three to four different subgroups. In addition, we demonstrated that m⁶A RNA methylation regulators were strongly related to the clinical and biological features of ESCC. Analysis of transcriptome data revealed that m⁶A-mediated TME cell subsets had high predictive value and showed a close relationship with immune checkpoint genes. The validation results from qPCR demonstrated the characteristics of essential genes. CellChat analysis revealed that RNA from TME cells m⁶A methylation-associated cell subtypes had substantial and diversified interactions with cancer cells. Further investigation revealed that MIF- (CD74+CXCR4) and MIF- (CD74⁺CD44) ligand-receptor pairings facilitated communication between m⁶A-associated subtypes of TME cells and cancer cells.

Conclusion

Overall, our study demonstrated for the first time the function of m⁶A methylation-mediated intercellular communication in the microenvironment of tumors in controlling tumor development and anti-tumor immune regulation in ESCC.

Single-cell transcriptome sequencing of B-cell heterogeneity and tertiary lymphoid structure predicts breast cancer prognosis and neoadjuvant therapy efficacy

BACKGROUND

Breast cancer (BC) is a highly heterogeneous disease, and although immunotherapy has recently increased patient survival in a number of solid and hematologic malignancies, most BC subtypes respond poorly to immune checkpoint blockade therapy (ICB). B cells, particularly those that congregate in tertiary lymphoid structures (TLS), play a significant role in antitumour immunity. However, B-cell heterogeneity at single-cell resolution and its clinical significance with TLS in BC need to be explored further.

METHODS

Primary tumour lesions and surrounding normal tissues were taken from 14 BC patients, totaling 124,587 cells, for single-cell transcriptome sequencing and bioinformatics analysis.

RESULTS

Based on the usual markers, the single-cell transcriptome profiles were classified into various clusters. A thorough single-cell study was conducted with a focus on tumour-infiltrating B cells (TIL-B) and tumour-associated neutrophils (TAN). TIL-B was divided into five clusters, and unusual cell types, such as follicular B cells, which are strongly related to immunotherapy efficacy, were identified. In BC, TAN and TIL-B infiltration are positively correlated, and at the same time, compared with TLS-high, TAN and TIL-B in TLS-low group are significantly positively correlated.

CONCLUSIONS

In conclusion, our study highlights the heterogeneity of B cells in BC, explains how B cells and TLS contribute significantly to antitumour immunity at both the single-cell and clinical level, and offers a straightforward marker for TLS called CD23. These results will offer more pertinent information on the applicability and effectiveness of tumour immunotherapy for BC.

Aggrephagy-related patterns in tumor microenvironment, prognosis, and immunotherapy for acute myeloid leukemia: a comprehensive single-cell RNA sequencing analysis

Acute myeloid leukemia (AML) is a complex mixed entity composed of malignant tumor cells, immune cells and stromal cells, with intra-tumor and inter-tumor heterogeneity. Single-cell RNA sequencing enables a comprehensive study of the highly complex tumor microenvironment, which is conducive to exploring the evolutionary trajectory of tumor cells. Herein, we carried out comprehensive analyses of aggrephagy-related cell clusters based on single-cell sequencing for patients with acute myeloid leukemia. A total of 11 specific cell types (T, NK, CMP, Myeloid, GMP, MEP, Promono, Plasma, HSC, B, and Erythroid cells) using t-SNE dimension reduction analysis. Several aggrephagy-related genes were highly expressed in the 11 specific cell types. Using Monocle analysis and NMF clustering analysis, six aggrephagy-related CD8+ T clusters, six aggrephagy-related NK clusters, and six aggrephagy-related Mac clusters were identified. We also evaluated the ligand-receptor links and Cell-cell communication using CellChat package and CellChatDB database. Furthermore, the transcription factors (TFs) of aggrephagy-mediated cell clusters for AML were assessed through pySCENIC package. Prognostic analysis of the aggrephagy-related cell clusters based on R package revealed the differences in prognosis of aggrephagy-mediated cell clusters. Immunotherapy of the aggrephagy-related cell clusters was investigated using TIDE algorithm and public immunotherapy cohorts. Our study revealed the significance of aggrephagy-related patterns in tumor microenvironment, prognosis, and immunotherapy for AML.

Identification of a single cell-based signature for predicting prognosis risk and immunotherapy response in patients with glioblastoma

This study constructed a novel gene pair signature based on bulk and single-cell sequencing samples in relative expression order within the samples. The subsequent analysis included glioma samples from Xiangya Hospital. Gene pair signatures possessed a solid ability to predict the prognosis of glioblastoma and pan-cancer. Samples having different malignant biological hallmarks were distinguished by the algorithm, with the high gene pair score group featuring classic copy number variations, oncogenic mutations, and extensive hypomethylation, mediating poor prognosis. The increased gene pair score group with a poorer prognosis demonstrated significant enrichment in tumor and immune-related signaling pathways while presenting immunological diversity. The remarkable infiltration of M2 macrophages in the high gene pair score group was validated by multiplex immunofluorescence, suggesting that combination therapies targeting adaptive and innate immunity may serve as a therapeutic option. Overall, a gene pair signature applicable to predict prognosis hopefully provides a reference to guide clinical practice.

Nasopharyngeal Carcinoma Subtype Discovery via Immune Cell Scores from Tumor Microenvironment

BACKGROUND

Nasopharyngeal carcinoma (NPC) is one of the most prevalent cancers with a poor prognosis. Immunotherapy, especially immune checkpoint blockade (ICB), is becoming a potential therapeutic choice for NPC patients. Thus, the identification of patients who could benefit from immunotherapy is clinically significant.

METHODS

The NPC expression profiles from GSE102349 were used to calculate the cell scores of the tumor microenvironment (TME). The consensus clustering method was utilized to identify the potential molecular subtypes among NPC samples. The hub genes were selected from subtype-specific genes by bioinformatics analysis. Machine learning models, including random forest (RF) and support vector machine (SVM) algorithms, were constructed to predict the immune subtype.

RESULTS

In the present study, we identified two TME subtypes among NPC patients. Patients with the S1 subtype have higher levels of immune cells, immune checkpoint genes, and prognosis. Using expression data profiles of NPC patients, we constructed machine learning models for predicting TME subtypes of NPC patients. This model consists of 8 genes (LCK, CD247, FYN, ZAP70, SH2D1A, CD3D, CD3E, and CD3G). Among them, LCK, FYN, SH2D1A, and CD3D were associated with better prognoses. Among the two constructed models, SVM exhibited a higher area under curve (AUC) of 0.977, when compared with RF (AUC = 0.966). The web server based on the constructed machine learning models will contribute to the identification of NPC patients likely to benefit from ICB therapies.

CONCLUSIONS

This study identified NPC subtypes and provided an accurate model to select individuals who are most likely to respond to ICB.

Epigenetic state determines the in vivo efficacy of STING agonist therapy

While STING-activating agents have shown limited efficacy in early-phase clinical trials, multiple lines of evidence suggest the importance of tumor cell-intrinsic STING function in mediating antitumor immune responses. Although STING signaling is impaired in human melanoma, its restoration through epigenetic reprogramming can augment its antigenicity and T cell recognition. In this study, we show that reversal of methylation silencing of STING in murine melanoma cell lines using a clinically available DNA methylation inhibitor can improve agonist-induced STING activation and type-I IFN induction, which, in tumor-bearing mice, can induce tumor regression through a CD8⁺ T cell-dependent immune response. These findings not only provide mechanistic insight into how STING signaling dysfunction in tumor cells can contribute to impaired responses to STING agonist therapy, but also suggest that pharmacological restoration of STING signaling through epigenetic reprogramming might improve the therapeutic efficacy of STING agonists.

Clinical significance and immune landscape of a novel ferroptosis-related prognosis signature in osteosarcoma

BACKGROUND

Osteosarcoma is a malignant tumor that usually occurs in adolescents aged 10-20 years and is associated with poor prognosis. Ferroptosis is an iron-dependent cell death mechanism that plays a vital role in cancer.

METHODS

Osteosarcoma transcriptome data were downloaded from the public database TARGET and from previous studies. A prognostic risk score signature was constructed using bioinformatics analysis, and its efficacy was determined by analyzing typical clinical features. The prognostic signature was then validated with external data. Differences in immune cell infiltration between high- and low-risk groups were analyzed. The potential of the prognostic risk signature as a predictor of immunotherapy response was evaluated using the GSE35640 (melanoma) dataset. Five key genes expression were measured by real-time PCR and western blot in human normal osteoblasts and osteosarcoma cells. Moreover, malignant biological behaviors of osteosarcoma cells were tested by modulating gene expression level.

RESULTS

We obtained 268 ferroptosis-related genes from the online database FerrDb and published articles. Transcriptome data and clinical information of 88 samples in the TARGET database were used to classify genes into two categories using clustering analysis, and significant differences in survival status were identified. Differential ferroptosis-related genes were screened, and functional enrichment showed that they were associated with HIF-1, T cells, IL17, and other inflammatory signaling pathways. Prognostic factors were identified by univariate Cox regression and LASSO analysis, and a 5-factor prognostic risk score signature was constructed, which was also applicable for external data validation. Experimental validation indicated that the mRNA and protein expression level of MAP3K5, LURAP1L, HMOX1 and BNIP3 decreased significantly, though meanwhile MUC1 increased in MG-63 and SAOS-2 cells compared with hFOB1.19 cells. Cell proliferation and migration ability of SAOS-2 were affected based on alterations of signature genes.

CONCLUSIONS

Significant differences in immune cell infiltration between high- and low-risk groups indicated that the five ferroptosis-related prognostic signature was constructed and could be used to predict the response to immunotherapy in osteosarcoma.

A comprehensive analysis of PANoptosome to prognosis and immunotherapy response in pan-cancer

PANoptosis, a programmed cell death, shares key characteristics of apoptosis, pyroptosis, and necroptosis. Accumulating evidence suggests that PANoptosis plays a crucial role in tumorigenesis. However, the respective regulation mechanisms in cancer are so far unclear. Using various bioinformatic approaches, we comprehensively analyzed the expression patterns, genetic alterations, prognostic value, and immunological role of PANoptosis genes in pan-cancer. Expression of the PANoptosis gene, PYCARD, was validated based on the Human Protein Atlas database and real-time quantitative reverse transcription polymerase chain reaction (RT-PCR). We found that PANoptosis genes were aberrantly expressed in most cancer types, which was consistent with the validation of PYCARD expression. Concurrently, PANoptosis genes and PANoptosis scores were significantly associated with patient survival in 21 and 14 cancer types, respectively. Pathway analysis showed that PANoptosis score was positively correlated with pathways linked to immune and inflammatory responses in pan-cancer, such as IL6-JAK-STAT3 signaling, the interferon-gamma response, and IL2-STAT5 signaling. In addition, the PANoptosis score was significantly correlated with the tumor microenvironment, the infiltration levels of most immune cells (i.e.NK cells, CD8+ T cells, CD4+ T cells, DC cells), and immune-related genes. Furthermore, it was a predictive indicator of immunotherapy response in patients with tumors. These insights substantially improve our understanding of PANoptosis components in cancers and may inspire the discovery of novel prognostic and immunotherapy response biomarkers.

Cytotoxic Lymphocyte-Related Gene Signature in Triple-Negative Breast Cancer

To curate the signature genes of cytotoxic lymphocytes (CLs) and explore the heterogeneity based on the CL-related (CLR) gene signature, we analyzed the gene expression of 592 patients with histologically diagnosed triple-negative breast cancer. Based on the 13-gene panel, CLR signatures were curated and associated with the stage of tumor size. Patients in the CLR-low group exhibited the worse overall survival (OS) (median OS, 75.23 months vs. 292.66 months, p < 0.0001) and were characterized by the upregulation of the NF-κB, Wnt, and p53 pathways, the positive regulation of angiogenesis, and a higher expression of immune checkpoints including CTLA4, LAG3, CD86, ICOS, ICOSLG, and TNFSF9. In cancer immunotherapy cohorts (GSE157284, GSE35640, IMvigor210), a higher CLR signature score was remarkably associated with greater tumor shrinkage and immune characteristics consisting of higher PD-L1 and neoantigen expression, as well as an inflamed tumor microenvironment. In the pan-cancer atlas, the CLR signature was notably associated with patient survival and revealed a profound heterogeneity across the malignancy types. In sum, the CLR signature is a promising indicator for immune characteristics, tumor shrinkage, and survival outcomes following cancer immunotherapy in addition to the prognostic heterogeneity in the pan-cancer atlas.

An artificial intelligence network-guided signature for predicting outcome and immunotherapy response in lung adenocarcinoma patients based on 26 machine learning algorithms

The immune cells play an increasingly vital role in influencing the proliferation, progression, and metastasis of lung adenocarcinoma (LUAD) cells. However, the potential of immune cells' specific genes-based model remains largely unknown. In the current study, by analysing single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing data, the tumour-infiltrating immune cell (TIIC) associated signature was developed based on a total of 26 machine learning (ML) algorithms. As a result, the TIIC signature score could predict survival outcomes of LUAD patients across five independent datasets. The TIIC signature score showed superior performance to 168 previously established signatures in LUAD. Moreover, the TIIC signature score developed by the immunofluorescence staining of the tissue array of LUAD patients showed a prognostic value. Our research revealed a solid connection between TIIC signature score and tumour immunity as well as metabolism. Additionally, it has been discovered that the TIIC signature score can forecast genomic change, chemotherapeutic drug susceptibility, and-most significantly-immunotherapeutic response. As a newly demonstrated biomarker, the TIIC signature score facilitated the selection of the LUAD population who would benefit from future clinical stratification.

An Immunogenic Cell Death-Related Gene Signature Reflects Immune Landscape and Predicts Prognosis in Melanoma Independently of BRAF V600E Status

Immunogenic cell death (ICD) is a type of regulated cell death that can activate adaptive immune response, and its ability to reshape the tumor microenvironment via multiple mechanisms may contribute to immunotherapy. The treatment options for patients with skin cutaneous melanoma (SKCM) vary based on BRAF V600E statuses. However, all standard treatments include immunotherapy. Therefore, it is critical to identify ICD-associated signatures that can help classify patients according to benefits from ICD immunotherapy. In this study, data on melanoma samples with BRAF V600E mutation (BRAF V600E-mutant melanoma) and melanoma samples with wild-type BRAF V600E alleles (BRAF V600E WT melanoma) were collected from The Cancer Genome Atlas (TCGA) database. The ICD-related (ICD-high and ICD-low) subgroups of patients with BRAF V600E WT melanoma were established via consensus clustering. The analyses of survival, differentially expressed genes (DEGs), functional annotation, and immune landscape were performed in these two subgroups. Results showed that ICD-high subgroup was correlated with a positive overall survival (OS) and active tumor immune landscape. A model comprising seven prognosis ICD-related gene biomarkers was developed. Survival analysis and receiver operating characteristic (ROC) curve evaluation in both cohorts with BRAF V600E WT and BRAF V600E-mutant melanoma showed an accurate prognostic estimation of ICD-related risk signature. There was a correlation between immune cell infiltration and immunotherapy response and risk score. Thus, the ICD risk signature was closely associated with the tumor's immune microenvironment. Our results may provide insights to further individualize and improve precision therapeutic decision-making in BRAF V600E-mutant and WT melanoma.

Epigenome-wide DNA methylation in leukocytes and toenail metals: The normative aging study

BACKGROUND

Environmental metal exposures have been associated with multiple deleterious health endpoints. DNA methylation (DNAm) may provide insight into the mechanisms underlying these relationships. Toenail metals are non-invasive biomarkers, reflecting a medium-term time exposure window.

OBJECTIVES

This study examined variation in leukocyte DNAm and toenail arsenic (As), cadmium (Cd), lead (Pb), manganese (Mn), and mercury (Hg) among elderly men in the Normative Aging Study, a longitudinal cohort.

METHODS

We repeatedly collected samples of blood and toenail clippings. We measured DNAm in leukocytes with the Illumina HumanMethylation450 K BeadChip. We first performed median regression to evaluate the effects of each individual toenail metal on DNAm at three levels: individual cytosine-phosphate-guanine (CpG) sites, regions, and pathways. Then, we applied a Bayesian kernel machine regression (BKMR) to assess the joint and individual effects of metal mixtures on DNAm. Significant CpGs were identified using a multiple testing correction based on the independent degrees of freedom approach for correlated outcomes. The approach considers the effective degrees of freedom in the DNAm data using the principal components that explain >95% variation of the data.

RESULTS

We included 564 subjects (754 visits) between 1999 and 2013. The numbers of significantly differentially methylated CpG sites, regions, and pathways varied by metals. For example, we found six significant pathways for As, three for Cd, and one for Mn. The As-associated pathways were associated with cancer (e.g., skin cancer) and cardiovascular disease, whereas the Cd-associated pathways were related to lung cancer. Metal mixtures were also associated with 47 significant CpG sites, as well as pathways, mainly related to cancer and cardiovascular disease.

CONCLUSIONS

This study provides an approach to understanding the potential epigenetic mechanisms underlying observed relations between toenail metals and adverse health endpoints.

Comprehensive analysis of immune-related lncRNAs and their clinical relevance in gastric adenocarcinoma

Increasing evidence has demonstrated that lncRNA plays a significant role in the immunity regulation of gastric adenocarcinoma. However, the immune-related lncRNAs and the prognostic value in immunotherapies remain largely unexplored. We collected immune-related lncRNA and the associated pathways of gastric cancer from the ImmLnc database. The cox regression model is used to analyze the prognostic value of these lncRNAs. Gastric cancer is further divided into different subtypes based on these lncRNAs. Tumor microenvironment analysis, functional enrichment analysis, and genomic alteration analysis are performed for different subtypes. Furthermore, chemotherapeutic and immunotherapeutic sensitivity are also analyzed among different subtypes. Nine lncRNAs are identified as significant regulators of the immune pathway of gastric cancer. Gastric cancer can be classified into 5 subtypes based on these lncRNAs. Tumor microenvironment analysis shows that cluster C3 has the highest immune score and C5 has the lowest score. Functional analysis shows that these subtypes are enriched with distinct biological processes. Genomic analysis shows that LAMA2 mutation is a protective factor in C3 but a risk factor in C5. Furthermore, these subtypes are found to respond distinctly to the same chemotherapeutic and immunotherapeutic drugs. In this study, we analyzed the immune-related lncRNA and identified the crucial role in the regulation of immune properties, biological processes, and immunotherapeutic sensitivity. These findings can improve our understanding of the epigenetic immunoregulation of lncRNA and advance the research of immunotherapy.

Immune Pathways with Aging Characteristics Improve Immunotherapy Benefits and Drug Prediction in Human Cancer

(1) Background: Perturbation of immune-related pathways can make substantial contributions to cancer. However, whether and how the aging process affects immune-related pathways during tumorigenesis remains largely unexplored. (2) Methods: Here, we comprehensively investigated the immune-related genes and pathways among 25 cancer types using genomic and transcriptomic data. (3) Results: We identified several pathways that showed aging-related characteristics in various cancers, further validated by conventional aging-related gene sets. Genomic analysis revealed high mutation burdens in cytokines and cytokines receptors pathways, which were strongly correlated with aging in diverse cancers. Moreover, immune-related pathways were found to be favorable prognostic factors in melanoma. Furthermore, the expression level of these pathways had close associations with patient response to immune checkpoint blockade therapy in melanoma and non-small cell lung cancer. Applying a net-work-based method, we predicted immune- and aging-related genes in pan-cancer and utilized these genes for potential immunotherapy drug discovery. Mapping drug target data to our top-ranked genes identified potential drug targets, FYN, JUN, and SRC. (4) Conclusions: Taken together, our systematic study helped interpret the associations among immune-related pathways, aging, and cancer and could serve as a resource for promoting clinical treatment.

Comprehensive analysis of a novel RNA modifications-related model in the prognostic characterization, immune landscape and drug therapy of bladder cancer

Background: Bladder cancer (BCa) is the leading reason for death among genitourinary malignancies. RNA modifications in tumors closely link to the immune microenvironment. Our study aimed to propose a promising model associated with the "writer" enzymes of five primary RNA adenosine modifications (including m⁶A, m⁶A_m, m¹A, APA, and A-to-I editing), thus characterizing the clinical outcome, immune landscape and therapeutic efficacy of BCa. Methods: Unsupervised clustering was employed to categorize BCa into different RNA modification patterns based on gene expression profiles of 34 RNA modification "writers". The RNA modification "writers" score (RMS) signature composed of RNA phenotype-associated differentially expressed genes (DEGs) was established using the least absolute shrinkage and selection operator (LASSO), which was evaluated in meta-GEO (including eight independent GEO datasets) training cohort and the TCGA-BLCA validation cohort. The hub genes in the RMS model were determined via weighted gene co-expression network analysis (WGCNA) and were further validated using human specimen. The potential applicability of the RMS model in predicting the therapeutic responsiveness was assessed through the Genomics of Drug Sensitivity in Cancer database and multiple immunotherapy datasets. Results: Two distinct RNA modification patterns were determined among 1,410 BCa samples from a meta-GEO cohort, showing radically varying clinical outcomes and biological characteristics. The RMS model comprising 14 RNA modification phenotype-associated prognostic DEGs positively correlated with the unsatisfactory outcome of BCa patients in meta-GEO training cohort (HR = 3.00, 95% CI = 2.19-4.12) and TCGA-BLCA validation cohort (HR = 1.53, 95% CI = 1.13-2.09). The infiltration of immunosuppressive cells and the activation of EMT, angiogenesis, IL-6/JAK/STAT3 signaling were markedly enriched in RMS-high group. A nomogram exhibited high prognostic prediction accuracy, with a concordance index of 0.785. The therapeutic effect of chemotherapeutic agents and antibody-drug conjugates was significantly different between RMS-low and -high groups. The combination of the RMS model and conventional characteristics (TMB, TNB and PD-L1) achieved an optimal AUC value of 0.828 in differentiating responders from non-responders to immunotherapy. Conclusion: We conferred the first landscape of five forms of RNA modifications in BCa and emphasized the excellent power of an RNA modifications-related model in evaluating BCa prognosis and immune landscape.

The immune landscape of solid pediatric tumors

Background

Large immunogenomic analyses have demonstrated the prognostic role of the functional orientation of the tumor microenvironment in adult solid tumors, this variable has been poorly explored in the pediatric counterpart.

Methods

We performed a systematic analysis of public RNAseq data (TARGET) for five pediatric tumor types (408 patients): Wilms tumor (WLM), neuroblastoma (NBL), osteosarcoma (OS), clear cell sarcoma of the kidney (CCSK) and rhabdoid tumor of the kidney (RT). We assessed the performance of the Immunologic Constant of Rejection (ICR), which captures an active Th1/cytotoxic response. We also performed gene set enrichment analysis (ssGSEA) and clustered more than 100 well characterized immune traits to define immune subtypes and compared their outcome.

Results

A higher ICR score was associated with better survival in OS and high risk NBL without MYCN amplification but with poorer survival in WLM. Clustering of immune traits revealed the same five principal modules previously described in adult tumors (TCGA). These modules divided pediatric patients into six immune subtypes (S1-S6) with distinct survival outcomes. The S2 cluster showed the best overall survival, characterized by low enrichment of the wound healing signature, high Th1, and low Th2 infiltration, while the reverse was observed in S4. Upregulation of the WNT/Beta-catenin pathway was associated with unfavorable outcomes and decreased T-cell infiltration in OS.

Conclusions

We demonstrated that extracranial pediatric tumors could be classified according to their immune disposition, unveiling similarities with adults’ tumors. Immunological parameters might be explored to refine diagnostic and prognostic biomarkers and to identify potential immune-responsive tumors.

Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma

Long noncoding ribonucleic acids (RNAs; lncRNAs) have been associated with cancer immunity regulation. However, the roles of immune cell-specific lncRNAs in glioblastoma (GBM) remain largely unknown. In this study, a novel computational framework was constructed to screen the tumor-infiltrating immune cell-associated lncRNAs (TIIClnc) for developing TIIClnc signature by integratively analyzing the transcriptome data of purified immune cells, GBM cell lines and bulk GBM tissues using six machine learning algorithms. As a result, TIIClnc signature could distinguish survival outcomes of GBM patients across four independent datasets, including the Xiangya in-house dataset, and more importantly, showed superior performance than 95 previously established signatures in gliomas. TIIClnc signature was revealed to be an indicator of the infiltration level of immune cells and predicted the response outcomes of immunotherapy. The positive correlation between TIIClnc signature and CD8, PD-1 and PD-L1 was verified in the Xiangya in-house dataset. As a newly demonstrated predictive biomarker, the TIIClnc signature enabled a more precise selection of the GBM population who would benefit from immunotherapy and should be validated and applied in the near future.