A comprehensive investigation of associations between cell death pathways and molecular and clinical features in pan-cancer.

BACKGROUND: Regulated cell death (RCD) pathways play significant roles in tumorigenesis. However, systematic investigation into correlations between RCD and various molecular and clinical features, particularly anti-tumor immunity and immunotherapy response in pan-cancer remains lacking. METHODS: Using the single-sample gene set enrichment analysis, we quantified the activities of six RCD pathways (apoptosis, autophagy, ferroptosis, cuproptosis, necroptosis, and pyroptosis) in each cancer specimen. Then, we explored associations of these six RCD pathways with tumor immunity, genomic instability, tumor phenotypes and clinical features, and responses to immunotherapy and targeted therapies in pan-cancer by statistical analyses. RESULTS: Our results showed that the RCD (except autophagy) activities were oncogenic signatures, as evidenced by their hyperactivation in late stage or metastatic cancer patients, positive correlations with tumor proliferation, stemness, genomic instability and intratumor heterogeneity, and correlation with worse survival outcomes in cancer. In contrast, autophagy was a tumor suppressive signature as its associations with molecular and clinical features in cancer shows an opposite pattern compared to the other RCD pathways. Furthermore, heightened RCD (except cuproptosis) activities were correlated with increased sensitivity to immune checkpoint inhibitors. Additionally, elevated activities of pyroptosis, autophagy, cuproptosis and necroptosis were associated with increased drug sensitivity in a broad spectrum of anti-tumor targeted therapies, while the elevated activity of ferroptosis was correlated with decreased sensitivity to numerous targeted therapies. CONCLUSION: RCD (except autophagy) activities correlate with unfavorable cancer prognosis, while the autophagy activity correlate with favorable clinical outcomes. RCD (except cuproptosis) activities are positive biomarkers for anti-tumor immunity and immunotherapy response.

Discovering explainable biomarkers for breast cancer anti-PD1 response via network Shapley value analysis.

BACKGROUND AND OBJECTIVE: Immunotherapy holds promise in enhancing pathological complete response rates in breast cancer, albeit confined to a select cohort of patients. Consequently, pinpointing factors predictive of treatment responsiveness is of paramount importance. Gene expression and regulation, inherently operating within intricate networks, constitute fundamental molecular machinery for cellular processes and often serve as robust biomarkers. Nevertheless, contemporary feature selection approaches grapple with two key challenges: opacity in modeling and scarcity in accounting for gene-gene interactions METHODS: To address these limitations, we devise a novel feature selection methodology grounded in cooperative game theory, harmoniously integrating with sophisticated machine learning models. This approach identifies interconnected gene regulatory network biomarker modules with priori genetic linkage architecture. Specifically, we leverage Shapley values on network to quantify feature importance, while strategically constraining their integration based on network expansion principles and nodal adjacency, thereby fostering enhanced interpretability in feature selection. We apply our methods to a publicly available single-cell RNA sequencing dataset of breast cancer immunotherapy responses, using the identified feature gene set as biomarkers. Functional enrichment analysis with independent validations further illustrates their effective predictive performance RESULTS: We demonstrate the sophistication and excellence of the proposed method in data with network structure. It unveiled a cohesive biomarker module encompassing 27 genes for immunotherapy response. Notably, this module proves adept at precisely predicting anti-PD1 therapeutic outcomes in breast cancer patients with classification accuracy of 0.905 and AUC value of 0.971, underscoring its unique capacity to illuminate gene functionalities CONCLUSION: The proposed method is effective for identifying network module biomarkers, and the detected anti-PD1 response biomarkers can enrich our understanding of the underlying physiological mechanisms of immunotherapy, which have a promising application for realizing precision medicine.

Pan-cancer analysis of Sp1 with a focus on immunomodulatory roles in gastric cancer.

BACKGROUND: Sp1, a transcription factor, regulates essential cellular processes and plays important tumorigenic roles across diverse cancers. However, comprehensive pan-cancer analyses of its expression and potential immunomodulatory roles remain unexplored. METHODS: Utilizing bioinformatics tools and public datasets, we examined the expression of Sp1 across normal tissues, tumors, and immune cells, and screened for pre- and post-transcriptional modifications, including genetic alterations, DNA methylation, and protein phosphorylation, affecting its expression or function. The association of Sp1 expression with immune cell infiltration, tumor mutational burden, and immune checkpoint signaling was also investigated. Single-cell transcriptome data was used to assess Sp1 expression in immune cells in gastric cancer (GC), and findings were corroborated using immunohistochemistry and multiplex immunofluorescence in an immunotherapy-treated patient cohort. The prognostic value of Sp1 in GC patients receiving immunotherapy was evaluated with Cox regression models. RESULTS: Elevated Sp1 levels were observed in various cancers compared to normal tissues, with notable prominence in GC. High Sp1 expression correlated with advanced stage, poor prognosis, elevated tumor mutational burden (TMB), and microsatellite instability (MSI) status, particularly in GC. Significant correlations between Sp1 levels and CD8+ T cell and the M1 phenotype of tumor-associated macrophages were further detected upon multiplex immunofluorescence in GC samples. Interestingly, we verified that GC patients with higher Sp1 levels exhibited improved response to immunotherapy. Moreover, Sp1 emerged as a prognostic and predictive biomarker for GC patients undergoing immunotherapy. CONCLUSIONS: Our pan-cancer analysis sheds light on the multifaceted role of Sp1 in tumorigenesis and underscores its potential as a prognostic and predictive biomarker for patients with GC undergoing immunotherapy.

Investigating PPT2's role in ovarian cancer prognosis and immunotherapy outcomes.

Ovarian cancer (OC) remains the primary cause of mortality among gynecological malignancies, and the identification of reliable molecular biomarkers to prognosticate OC outcomes is yet to be achieved. The gene palmitoyl protein thioesterase 2 (PPT2), which has been sparsely studied in OC, was closely associated with metabolism. This study aimed to determine the association between PPT2 expression, prognosis, immune infiltration, and potential molecular mechanisms in OC. We obtained the RNA-seq and clinical data from The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO) databases, then Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, nomogram, and calibration were conducted to assess and verify the role of PPT2. Gene set enrichment analysis (GSEA) was used to figure out the closely correlated pathways with PPT2. Overexpression experiment was performed to explore the function of PPT2. Our findings showed that PPT2 mRNA expression was apparent down-regulation in OC tissue compared to normal ovarian tissues in TCGA, GTEx datasets, and GEO datasets. This differential expression was also confirmed in our in-house datasets at both the mRNA and protein levels. Decreased PPT2 expression correlated with lower survival rates in TCGA, several GEO datasets, and our in-house datasets. Multivariate analysis revealed that PPT2 was an independent factor in predicting better outcomes for OC patients in TCGA and GEO. A negative correlation was revealed between immune infiltration and PPT2 expression through Single-sample GSEA (ssGSEA). Additionally, PPT2 was negatively correlated with an up-regulated immune score, stromal score, and estimate score, suggesting that patients with low PPT2 expression might benefit more from immunotherapy. Numerous chemical agents showed lower IC50 in patients with high PPT2 expression. In single-cell RNA sequencing (scRNA-seq) analysis of several OC datasets, we found PPT2 was mainly expressed in endothelial cells. Furthermore, we found that PPT2 inhibited OC cell proliferation in vitro. Our results demonstrated that PPT2 was considered a favorable prognostic biomarker for OC and may be vital in predicting response to immunotherapy and chemotherapy. Further research was needed to fully understand the relationship between PPT2 and immunotherapy efficacy in OC patients.

Revealing the mechanisms of RAC3 in tumor aggressiveness, the immunotherapy response, and drug resistance in bladder cancer.

Background: Bladder cancer (BLCA) is a prevalent urinary tract malignancy with a high propensity for recurrence and chemoresistance. The molecular mechanisms underlying its progression and response to therapy have not been fully elucidated. Methods: We conducted a multifaceted analysis, integrating immunohistochemical (IHC) staining, bioinformatics evaluation using TCGA and CCLE databases, and in vitro assays using the BLCA cell lines 5637 and T24. RAC3 expression was assessed relative to clinical and pathological features. Functional enrichment analyses and gene set enrichment analysis (GSEA) were performed to identify associated biological processes and pathways. The impacts of RAC3 on cell proliferation, migration, invasion, and the immune microenvironment were evaluated using siRNA knockdown, CCK-8, Transwell, wound healing and colony formation assays. Results: Elevated RAC3 expression was significantly correlated with an advanced tumor stage, lymph node metastasis, and poor prognosis for BLCA patients. The functional enrichment analysis implicated RAC3 in immune cell infiltration and immune checkpoint mechanisms. Notably, RAC3 knockdown significantly reduced the proliferative, migratory, and invasive capabilities of BLCA cells. These effects were reversed by the overexpression of RAC3. Additionally, RAC3 expression was linked to chemoresistance, with high RAC3 expression predicting resistance to certain therapeutic agents. The TIDE algorithm indicated that RAC3 expression could be a predictive biomarker for the immunotherapy response. Conclusion: RAC3 was identified as a potential therapeutic target and biomarker of BLCA, as its expression significantly influenced tumor progression, the immune response, and chemosensitivity. Targeting RAC3 may provide a novel strategy for the management of BLCA, particularly for patients resistant to conventional therapies. Further research is essential to elucidate the detailed mechanisms of RAC3 in BLCA and explore its clinical application in precision medicine.

Integration of ubiquitination-related genes in predictive signatures for prognosis and immunotherapy response in sarcoma.

Background: Ubiquitination is one of the most prevalent and complex post-translational modifications of proteins in eukaryotes, playing a critical role in regulating various physiological and pathological processes. Targeting ubiquitination pathways, either through inhibition or activation, holds promise as a novel therapeutic approach for cancer treatment. However, the expression patterns, prognostic significance, and underlying mechanisms of ubiquitination-related genes (URGs) in sarcoma (SARC) remain unclear. Methods: We analyzed URG expression patterns and prognostic implications in TCGA-SARC using public databases, identifying DEGs related to ubiquitination among SARC molecular subtypes. Functional enrichment analysis elucidated their biological significance. Prognostic signatures were developed using LASSO-Cox regression, and a predictive nomogram was constructed. External validation was performed using GEO datasets and clinical tissue samples. The association between URG risk scores and various clinical parameters, immune response, drug sensitivity, and RNA modification regulators was investigated. Integration of data from multiple sources and RT-qPCR confirmed upregulated expression of prognostic URGs in SARC. Single-cell RNA sequencing data analyzed URG distribution across immune cell types. Prediction analysis identified potential target genes of microRNAs and long non-coding RNAs. Results: We identified five valuable genes (CALR, CASP3, BCL10, PSMD7, PSMD10) and constructed a prognostic model, simultaneously identifying two URG-related subtypes in SARC. The UEGs between subtypes in SARC are mainly enriched in pathways such as Cell cycle, focal adhesion, and ECM-receptor interaction. Analysis of URG risk scores reveals that patients with a low-risk score have better prognoses compared to those with high-risk scores. There is a significant correlation between DRG riskscore and clinical features, immune therapy response, drug sensitivity, and genes related to pan-RNA epigenetic modifications. High-risk SARC patients were identified as potential beneficiaries of immune checkpoint inhibitor therapy. We established regulatory axes in SARC, including CALR/hsa-miR-29c-3p/LINC00943, CASP3/hsa-miR-143-3p/LINC00944, and MIR503HG. RT-qPCR data further confirmed the upregulation of prognostic URGs in SARC. Finally, we validated the prognostic model's excellent predictive performance in predicting outcomes for SARC patients. Conclusion: We discovered a significant correlation between aberrant expression of URGs and prognosis in SARC patients, identifying a prognostic model related to ubiquitination. This model provides a basis for individualized treatment and immunotherapy decisions for SARC patients.

The baseline hemoglobin level is a positive biomarker for immunotherapy response and can improve the predictability of tumor mutation burden for immunotherapy response in cancer.

Purpose: Because only a subset of cancer patients can benefit from immunotherapy, identifying predictive biomarkers of ICI therapy response is of utmost importance. Methods: We analyzed the association between hemoglobin (HGB) levels and clinical outcomes in 1,479 ICIs-treated patients across 16 cancer types. We explored the dose-dependent associations between HGB levels and survival and immunotherapy response using the spline-based cox regression analysis. Furthermore, we investigated the associations across subgroups of patients with different clinicopathological characteristics, treatment programs and cancer types using the bootstrap resampling method. Results: HGB levels correlated positively with clinical outcomes in cancer patients receiving immunotherapy but not in those without immunotherapy. Moreover, this association was independent of other clinicopathological characteristics (such as sex, age, tumor stage and tumor mutation burden (TMB)), treatment program and cancer type. Also, this association was independent of the established biomarkers of immunotherapy response, including TMB, PD-L1 expression and microsatellite instability. The combination of TMB and HGB level are more powerful in predicting immunotherapy response than TMB alone. Multi-omics analysis showed that HGB levels correlated positively with antitumor immune signatures and negatively with tumor properties directing antitumor immunosuppression, such as homologous recombination defect, stemness and intratumor heterogeneity. Conclusion: The HGB measure has the potential clinical value as a novel biomarker of immunotherapy response that is easily accessible from clinically routine examination. The combination of TMB and HGB measures have better predictive performance for immunotherapy response than TMB.

Clinical features and immunotherapy outcomes in antibody-negative autoimmune encephalitis: a retrospective case-control study.

Objective: This study aimed to compare clinical features, laboratory findings, and immunotherapy responses between antibody-positive and antibody-negative Autoimmune encephalitis (AE) patients. Methods: A retrospective analysis of clinical data from 60 AE patients (33 antibody-positive, 27 antibody-negative) diagnosed at Zhongshan Hospital of Xiamen University between January 1, 2016, and March 1, 2024 was conducted. Disease severity and treatment response were assessed using the modified Rankin Scale (mRS) and the Clinical Assessment Scale for Autoimmune Encephalitis (CASE). Results: Antibody-positive AE patients more frequently presented with multiple symptoms (≥4 symptoms: 39.4% vs. 14.8%, p = 0.036). They demonstrated significantly elevated serum IgG concentrations (p = 0.010) and cerebrospinal fluid (CSF) leukocyte counts (p = 0.014). Conversely, antibody-negative AE patients presented with higher CSF total protein levels (p = 0.025) and albumin quotients (p = 0.018), indicative of more severe blood-brain barrier disruption. Antibody-positive AE patients more frequently received combination first-line immunotherapy (75.8% vs. 48.1%, p = 0.027) and exhibited superior treatment outcomes (90.9% vs. 70%, p = 0.022). Among critically ill patients (peak mRS score: 4-5), improvement in CASE scores was markedly greater in the antibody-positive cohort (median: 4.50 vs. 1.00, p = 0.024). Conclusion: Antibody-positive AE patients manifested a more diverse symptom spectrum, elevated serum IgG concentrations and CSF leukocyte counts, and superior responses to immunotherapy. In contrast, antibody-negative AE patients demonstrated more severe blood-brain barrier dysfunction, as evidenced by higher CSF total protein concentrations and albumin quotients.

Leveraging programmed cell death patterns to predict prognosis and therapeutic sensitivity in OSCC.

OBJECTIVES: Intricate associations between programmed cell death (PCD) and cancer development and treatment outcomes have been increasingly appreciated. Here, we integrated 12 PCD patterns to construct a novel biomarker, cell death index (CDI), for oral squamous cell carcinoma (OSCC) prognostication and therapeutic prediction. MATERIALS AND METHODS: Univariate Cox regression, Kaplan-Meier survival, and LASSO analyses were performed to construct the CDI. A nomogram combining CDI and selected clinicopathological parameters was established by multivariate Cox regression. The associations between CDI and immune landscape and therapeutic sensitivity were estimated. Single-cell RNA-seq data of OSCC was used to infer CDI genes in selected cell types and determine their expression along cell differentiation trajectory. RESULTS: Ten selected PCD genes derived a novel prognostic signature for OSCC. The predictive prognostic performance of CDI and nomogram was robust and superior across multiple independent patient cohorts. CDI was negatively associated with tumor-infiltrating immune cell abundance and immunotherapeutic outcomes. Moreover, scRNA-seq data reanalysis revealed that GSDMB, IL-1A, PRKAA2, and SFRP1 from this signature were primarily expressed in cancer cells and involved in cell differentiation. CONCLUSIONS: Our findings established CDI as a novel powerful predictor for prognosis and therapeutic response for OSCC and suggested its potential involvement in cancer cell differentiation.

Construction of M2 macrophage-related gene signature for predicting prognosis and revealing different immunotherapy response in bladder cancer patients.

BACKGROUND: Bladder cancer development is closely associated with the dynamic interaction and communication between M2 macrophages and tumor cells. However, specific biomarkers for targeting M2 macrophages in immunotherapy remain limited and require further investigation. METHODS: In this study, we identified key co-expressed genes in M2 macrophages and developed gene signatures to predict prognosis and immunotherapy response in patients. Public database provided the bioinformatics data used in the analysis. We created and verified an M2 macrophage-related gene signature in these datasets using Lasso-Cox analysis. RESULTS: The predictive value and immunological functions of our risk model were examined in bladder cancer patients, and 158 genes were found to be significantly positively correlated with M2 macrophages. Moreover, we identified two molecular subgroups of bladder cancer with markedly different immunological profiles and clinical prognoses. The five key risk genes identified in this model were validated, including CALU, ECM1, LRP1, CYTL1, and CCDC102B, demonstrating the model can accurately predict prognosis and identify unique responses to immunotherapy in patients with bladder cancer. CONCLUSIONS: In summary, we constructed and validated a five-gene signature related to M2 macrophages, which shows strong potential for forecasting bladder cancer prognosis and immunotherapy response.

Prognostic and immunotherapeutic significances of M2 macrophage-related genes signature in lung cancer.

Objective: We aimed to investigate the immunological significance of M2 macrophage-related genes in lung cancer (LC) patients, specifically focusing on constructing a risk score to predict patient prognosis and response to immunotherapy. Methods: We developed a novel risk score by identifying and incorporating 12 M2 macrophage-related genes. The risk score was calculated by multiplying the expression levels of risk genes by their respective coefficients. Through comprehensive enrichment analysis, we explored the potential functions distinguishing high- and low-risk groups. Moreover, we examined the relationship between patients in different risk groups and immune infiltration as well as their response to immunotherapy. The single-cell RNA sequencing data were acquired to ascertain the spatial pattern of RNF130 expression. The expression of RNF130 was examined using TCGA datasets and verified by HPA. The qRT-PCR was employed to examine RNF130 expression in LC cells. Finally, in vitro experiments were carried out to validate the expression and function of RNF130. Results: Our results indicated that the risk score constructed from 12 M2 macrophage-related genes was an independent prognostic factor. Patients in the high-risk group had a significantly worse prognosis compared to those in the low-risk group. Functional enrichment analysis showed a significant relationship between the risk score and immunity. Furthermore, we explored immune infiltration in different risk groups using seven immune algorithms. The results demonstrated a negative correlation between high-risk group patients and immune infiltration of B cells, CD4+ cells, and CD8+ cells. We further validated these findings using an immunotherapy response database, which revealed that high-risk patients were more likely to exhibit immune evasion and might have poorer immunotherapy outcomes. Additionally, drug sensitivity analysis indicated that patients in the high-risk group were more sensitive to certain chemotherapeutic and targeted drugs than those in the low-risk group. Single-cell analysis indicated that macrophages were the primary site of RNF130 distribution. The results from the TCGA and HPA database demonstrated a trend toward a low expression of RNF130 in LC. Finally, in vitro experiments further validated the expression and function of RNF130 in LC cells. Conclusions: The high-risk group constructed with M2 macrophage-related genes in LC was closely associated with poor prognosis, low immune cell infiltration, and poorer response to immunotherapy. This risk score can help differentiate and predict the prognosis and immune status of LC patients, thereby aiding in the development of precise and personalized immunotherapy strategies.

CD19+CD73+ B cells infiltration indicates poor prognosis and unfavorable responses to immunotherapy in gastric cancer.

OBJECTIVES: Cluster of Differentiation 73 (CD73) is expressed on immune cells and plays a significant role in tumor inhibition by suppressing antitumor immunity. The objectives of this study were to explore the expression and functional mechanisms of CD73 on B cells in patients with gastric cancer (GC). METHODS: The prognostic significance of CD19+CD73+ B cells was evaluated in 390 GC patients through dual immunohistochemistry staining. Flow cytometry was employed to analyze the phenotype of the CD19 subpopulation using fresh tumor and non-tumor tissue samples from 8 GC patients. A bioinformatics analysis of CD19+CD73+ B cells was also performed within the scRNA-seq cohort, and the CD19+ B cell subtype was assessed using multiple immunofluorescence staining. RESULTS: The infiltration of CD19+CD73+ B cells was observed to be elevated in gastric cancer (GC) tissue compared to normal tissues. A strong correlation was observed between high CD19+CD73+ B cell infiltration, poor overall survival, and diminished responsiveness to neoadjuvant immunotherapy in GC. These cells emerged as a novel subset of regulatory B cells (Bregs) linked to adenosine metabolism and the exhaustion of CD8+ T cells. The CD19+CD73+ B cells also correlated with the production of immunosuppressive cytokines IL-10 and TGFB1. Further analysis indicated an association between CD19+CD73+ B cells and advanced-stage GC. CONCLUSIONS: The presence of CD19+CD73+ B cells in GC may serve as a prognostic indicator for clinical outcomes and a predictive marker for poor responsiveness to neoadjuvant immunotherapy. The correlation between the presence of CD19+CD73+ B cells and CD8+ T cell exhaustion, along with immunosuppression, highlights the tumor-promoting function of these cells.

Comprehensive analysis of Epha10 as a predictor of clinical prognosis and immune checkpoint therapy efficacy in non-small cell lung cancer.

The EphA family belongs to a large group of membrane receptor tyrosine kinases. Emerging evidence indicates that the EphA family participates in tumour occurrence and progression. Nonetheless, the expression patterns and prognostic values of the nine EphAs in non-small cell lung cancer (NSCLC) have rarely been studied before. In the current study, we comprehensively analysed the expression and prognostic role of EphA family members by different means. The Cancer Genome Atlas and Gene Expression Profiling Interactive Analysis databases were used to investigate the expression of EphAs in NSCLC. The cBioPortal database was applied to analyse the prognostic values and genetic mutations of EphAs.We discovered that the expression of EphA10 was significantly higher in NSCLC tissues than in adjacent noncancerous tissues, and survival analyses showed that a higher level of EphA10 predicted poor prognosis. Further exploration into the role of EphA10 by ESTIMATE, CIBERSORT, and ssGSEA analysis found that it was also related to immune infiltration and higher expression of targets of ICI targets. In conclusion, this study revealed that among the EphA family members, EphA10 played an oncogenic role and was a promising biomarker for poor prognosis and better immunotherapy response in NSCLC.

A novel M2-like tumor associated macrophages-related gene signature for predicting the prognosis and immunotherapy efficacy in gastric cancer.

BACKGROUND: M2-like tumor-associated macrophages (M2-like TAMs) play key roles in tumor progression and the immune response. However, the clinical significance and prognostic value of M2-like TAMs-associated regulatory genes in gastric cancer (GC) have not been clarified. METHODS: Herein, we identified M2-like TAM-related genes by weighted gene coexpression network analysis of TCGA-STAD and GSE84437 cohort. Lasso-Cox regression analyses were then performed to screen for signature genes, and a novel signature was constructed to quantify the risk score for each patient. Tumor mutation burden (TMB), survival outcomes, immune cells, and immune function were analyzed in the risk groups to further reveal the immune status of GC patients. A gene-drug correlation analysis and sensitivity analysis of anticancer drugs were used to identify potential therapeutic agents. Finally, we verified the mRNA expression of signature genes in patient tissues by qRT-PCR, and analyzed the expression distribution of these genes by IHC. RESULTS: A 4-gene (SERPINE1, MATN3, CD36, and CNTN1) signature was developed and validated, and the risk score was shown to be an independent prognostic factor for GC patients. Further analyses revealed that GC patients in the high-risk group had a worse prognosis than those in the low-risk group, with significant differences in TMB, clinical features, enriched pathways, TIDE score, and tumor microenvironment features. Finally, we used qRT-PCR and IHC analysis to verify mRNA and protein level expression of signature genes. CONCLUSION: These findings highlight the importance of M2-like TAMs, provide a new perspective on individualized immunotherapy for GC patients.

IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network.

INTRODUCTION: Immune checkpoint inhibitors (ICIs) are potent and precise therapies for various cancer types, significantly improving survival rates in patients who respond positively to them. However, only a minority of patients benefit from ICI treatments. OBJECTIVES: Identifying ICI responders before treatment could greatly conserve medical resources, minimize potential drug side effects, and expedite the search for alternative therapies. Our goal is to introduce a novel deep-learning method to predict ICI treatment responses in cancer patients. METHODS: The proposed deep-learning framework leverages graph neural network and biological pathway knowledge. We trained and tested our method using ICI-treated patients' data from several clinical trials covering melanoma, gastric cancer, and bladder cancer. RESULTS: Our results demonstrate that this predictive model outperforms current state-of-the-art methods and tumor microenvironment-based predictors. Additionally, the model quantifies the importance of pathways, pathway interactions, and genes in its predictions. A web server for IRnet has been developed and deployed, providing broad accessibility to users at https://irnet.missouri.edu. CONCLUSION: IRnet is a competitive tool for predicting patient responses to immunotherapy, specifically ICIs. Its interpretability also offers valuable insights into the mechanisms underlying ICI treatments.

In Vivo Mouse Models for Adult Brain Tumors: Exploring Tumorigenesis and Advancing Immunotherapy Development.

Brain tumors, particularly glioblastoma (GBM), are devastating and challenging to treat, with a low 5-year survival rate of only 6.6%. Mouse models are established to understand tumorigenesis and develop new therapeutic strategies. Large-scale genomic studies have facilitated the identification of genetic alterations driving human brain tumor development and progression. Genetically engineered mouse models (GEMMs) with clinically relevant genetic alterations are widely used to investigate tumor origin. Additionally, syngeneic implantation models, utilizing cell lines derived from GEMMs or other sources, are popular for their consistent and relatively short latency period, addressing various brain cancer research questions. In recent years, the success of immunotherapy in specific cancer types has led to a surge in cancer immunology-related research which specifically necessitates the utilization of immunocompetent mouse models. In this review, we provide a comprehensive summary of GEMMs and syngeneic mouse models for adult brain tumors, emphasizing key features such as model origin, genetic alteration background, oncogenic mechanisms, and immune-related characteristics. Our review serves as a valuable resource for the brain tumor research community, aiding in the selection of appropriate models to study cancer immunology.

RNA modification gene WDR4 facilitates tumor progression and immunotherapy resistance in breast cancer.

INTRODUCTION: Growing interest toward RNA modification in cancer has inspired the exploration of gene sets related to multiple RNA modifications. However, a comprehensive elucidation of the clinical value of various RNA modifications in breast cancer is still lacking. OBJECTIVES: This study aimed to provide a strategy based on RNA modification-related genes for predicting therapy response and survival outcomes in breast cancer patients. METHODS: Genes related to thirteen RNA modification patterns were integrated for establishing a nine-gene-containing signature-RMscore. Alterations of tumor immune microenvironment and therapy response featured by different RMscore levels were assessed by bulk transcriptome, single-cell transcriptome and genomics analyses. The biological function of key RMscore-related molecules was investigated by cellular experiments in vitro and in vivo, using flow cytometry, immunohistochemistry and immunofluorescence staining. RESULTS: This study has raised an effective therapy strategy for breast cancer patients after a well-rounded investigation of RNA modification-related genes. With a great performance of predicting patient prognosis, high levels of the RMscore proposed in this study represented suppressive immune microenvironment and therapy resistance, including adjuvant chemotherapy and PD-L1 blockade treatment. As the key contributor of the RMscore, inhibition of WDR4 impaired breast cancer progression significantly in vitro and in vivo, as well as participated in regulating cell cycle and mTORC1 signaling pathway via m7G modification. CONCLUSION: Briefly, this study has developed promising and effective tactics to achieve the prediction of survival probabilities and treatment response in breast cancer patients.

Pan-cell death-related signature reveals tumor immune microenvironment and optimizes personalized therapy alternations in lung adenocarcinoma.

This study constructed a comprehensive analysis of cell death modules in eliminating aberrant cells and remodeling tumor microenvironment (TME). Consensus analysis was performed in 490 lung adenocarcinoma (LUAD) patients based on 4 types of cell death prognostic genes. Intersection method divided these LUAD samples into 5 cell death risk (CDR) clusters, and COX regression analysis were used to construct the CDR signature (CDRSig) with risk scores. Significant differences of TME phenotypes, clinical factors, genome variations, radiosensitivity and immunotherapy sensitivity were observed in different CDR clusters. Patients with higher risk scores in the CDRSig tended to be immune-excluded or immune-desert, and those with lower risk scores were more sensitive to radiotherapy and immunotherapy. The results from mouse model showed that intense expression of the high-risk gene PFKP was associated with low CD8+ T cell infiltration upon radiotherapy and anti-PD-L1 treatment. Deficient assays in vitro confirmed that PFKP downregulation enhanced cGAS/STING pathway activation and radiosensitivity in LUAD cells. In conclusion, our studies originally performed a comprehensive cell death analysis, suggesting the importance of CDR patterns in reprogramming TME and providing novel clues for LUAD personalized therapies.

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts.

Introduction: While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Methods: Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders. Results: Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort. Discussion: Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.