Pan-cancer analysis of NLRP3 inflammasome with potential implications in prognosis and immunotherapy in human cancer

Single-cell and bulk transcriptome analysis identifies B-cell subpopulations and associated cancer subtypes with distinct clinical and molecular characteristics

BACKGROUD

Previous studies have identified B cell subpopulations with pro- and anti-tumoral activities, while the clinical relevance of B cell subpopulations-specific markers in pan-cancer remains understudied.

METHODS

We integrated 14 scRNA-seq datasets (102,504 cells from 424 patients, 15 cancer types) to identify B cell subpopulations via unsupervised clustering. We characterized their functional dynamics and prognostic relevance through analyzing single-cell, bulk and spatial transcriptomic data. Moreover, using B cell subpopulations-specific gene signatures, we constructed models for predicting cancer prognosis and immunotherapy response.

RESULTS

We identified eight B cell subpopulations (b00-b07) which were classified into naive, plasma, memory, germinal center (GC), and cycling B cells. Trajectory analysis revealed b02-naive and b04-GC cells in early phases, evolving into b01- and b03-plasma/b05- and b06-memory/b07-cycling and b05-memory subpopulations. Anti-tumor responses were activated in early pseudotime, complement/immunoglobulin pathways peaked in mid-pseudotime, and energy metabolism increased in late-pseudotime. The enrichment of b07-cycling and b04-GC was negatively correlated with cancer prognosis, while b02-naive had a positive correlation. Spatial transcriptomic analysis showed clustered b00-b06 versus dispersed b07 cells, with b04-GC and b07-cycling cells distant from tertiary lymphoid structure cores. Based on the expression profiles of 1,047 B cell subpopulations-specific signatures, we identified three pan-cancer subtypes with distinct clinical and molecular characteristics. Using 13 B cell subpopulations-specific signatures, we constructed models to accurately predict cancer survival outcomes and immunotherapy response.

CONCLUSIONS

Our study delineates eight B cell subpopulations with distinct prognostic relevance. Signature-based stratification and models underscore their clinical relevance in cancer outcomes and therapy response, advancing understanding of B cell heterogeneity in cancer.

SOAT1 Activates NLRP3 Inflammasome to Promote Cancer-Related Lymphangiogenesis and Metastasis via IL-1β/IL-1R-1 Axis in Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is a prevalent type of cancer in the head and neck region, significantly impacting patient survival rates and quality of life. Lymph node (LN) metastasis is a lead contributor to the poor prognosis associated with OSCC. SOAT1 plays a critical role in cholesterol metabolism and has been implicated in various cancers, although its specific mechanisms in OSCC are poorly understood. Additionally, NLRP3 inflammasome has been identified as a factor that promotes cancer progression by influencing various processes involved in tumor development, with its activation linked to cancer metastasis. Lymphangiogenesis enhancing cancer metastasis has been identified in OSCC, while the molecule networks of regulating it remains unclear. In our study, we found that SOAT1 is overexpressed in OSCC and promotes proliferation, migration, and invasion of OSCC cells. Knockdown SOAT1 expression impaired OSCC progression both in vitro and in vivo, and reduced the rate of LN metastasis. RNA sequencing analysis revealed that NLRP3 is a downstream regulated by SOAT1, with NLRP3 inflammasome reactivation having recovered cancer malignancy inhibited by SOAT1 knockdown. Furthermore, we revealed that IL-1β, released by NLRP3 inflammasome activation, could directly bind to IL-1R-1 in lymphatic endothelial cells (LECs), and enhance tube formation capacity of LECs, indicating the potential role of NLRP3 inflammasome in promoting lymphangiogenesis and metastasis in OSCC. In conclusion, SOAT1 could promote OSCC malignancy and regulate the activation of NLRP3 inflammasome to increase the rate of lymphangiogenesis and cancer metastasis via IL-1β/IL-1R-1 axis in OSCC.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Deciphering the Immunomodulatory Function of GSN+ Inflammatory Cancer-Associated Fibroblasts in Renal Cell Carcinoma Immunotherapy: Insights From Pan-Cancer Single-Cell Landscape and Spatial Transcriptomics Analysis

The heterogeneity of cancer-associated fibroblasts (CAFs) could affect the response to immune checkpoint inhibitor (ICI) therapy. However, limited studies have investigated the role of inflammatory CAFs (iCAFs) in ICI therapy using pan-cancer single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) analysis. We performed pan-cancer scRNA-seq and ST-seq analyses to identify the subtype of GSN+ iCAFs, exploring its spatial distribution characteristics in the context of ICI therapy. The pan-cancer scRNA-seq and bulk RNA-seq data are incorporated to develop the Caf.Sig model, which predicts ICI response based on CAF gene signatures and machine learning approaches. Comprehensive scRNA-seq analysis, along with in vivo and in vitro experiments, investigates the mechanisms by which GSN+ iCAFs influence ICI efficacy. The Caf.Sig model demonstrates well performances in predicting ICI therapy response in pan-cancer patients. A higher proportion of GSN+ iCAFs is observed in ICI non-responders compared to responders in the pan-cancer landscape and clear cell renal cell carcinoma (ccRCC). Using real-world immunotherapy data, the Caf.Sig model accurately predicts ICI response in pan-cancer, potentially linked to interactions between GSN+ iCAFs and CD8+ Tex cells. ST-seq analysis confirms that interactions and cellular distances between GSN+ iCAFs and CD8+ exhausted T (Tex) cells impact ICI efficacy. In a co-culture system of primary CAFs, primary tumour cells and CD8+ T cells, downregulation of GSN on CAFs drives CD8+ T cells towards a dysfunctional state in ccRCC. In a subcutaneously tumour-grafted mouse model, combining GSN overexpression with ICI treatment achieves optimal efficacy in ccRCC. Our study provides the Caf.Sig model as an outperforming approach for patient selection of ICI therapy, and advances our understanding of CAF biology and suggests potential therapeutic strategies for upregulating GSN in CAFs in cancer immunotherapy.

Targeting the NLRP3 inflammasome for inflammatory disease therapy

The NOD-like receptor pyrin domain-containing 3 (NLRP3) inflammasome is a megadalton complex implicated in numerous inflammation-driven diseases including COVID-19, Alzheimer's disease, and gout. Although past efforts have focused on inhibiting IL-1β downstream of NLRP3 activation using drugs such as canakinumab, no FDA-approved NLRP3-targeted inhibitors are currently available. MCC950, a direct NLRP3 inhibitor, showed promise but exhibited off-target effects. Recent research has focused on optimizing the sulfonylurea-based MCC950 scaffold by leveraging recent structural and medicinal chemistry insights into the NLRP3 nucleotide-binding and oligomerization (NACHT) domain to improve solubility and clinical efficacy. In addition, oxidized DNA (oxDNA) has emerged as a key inflammasome trigger, and molecules targeting the pyrin domain have shown promise in inhibiting NLRP3 activation. This review discusses the role of NLRP3 in inflammation-related diseases, the status of ongoing clinical trials, and emerging small-molecule therapeutics targeting NLRP3.

The intrinsic expression of NLRP3 in Th17 cells promotes their protumor activity and conversion into Tregs

Th17 cells can perform either regulatory or inflammatory functions depending on the cytokine microenvironment. These plastic cells can transdifferentiate into Tregs during inflammation resolution, in allogenic heart transplantation models, or in cancer through mechanisms that remain poorly understood. Here, we demonstrated that NLRP3 expression in Th17 cells is essential for maintaining their immunosuppressive functions through an inflammasome-independent mechanism. In the absence of NLRP3, Th17 cells produce more inflammatory cytokines (IFNγ, Granzyme B, TNFα) and exhibit reduced immunosuppressive activity toward CD8+ cells. Moreover, the capacity of NLRP3-deficient Th17 cells to transdifferentiate into Treg-like cells is lost. Mechanistically, NLRP3 in Th17 cells interacts with the TGF-β receptor, enabling SMAD3 phosphorylation and thereby facilitating the acquisition of immunosuppressive functions. Consequently, the absence of NLRP3 expression in Th17 cells from tumor-bearing mice enhances CD8 + T-cell effectiveness, ultimately inhibiting tumor growth.

Mutational signature-based biomarker to predict the response of immune checkpoint inhibitors therapy in cancers

Patients have a limited response rate to immune checkpoint inhibitors (ICIs) therapy. Although several biomarkers have been proposed, their ability to accurately predict the response to ICIs therapy remains unsatisfactory. In addition, mutational signatures were validated to be associated with ICIs therapy. Therefore, we developed a mutational signature-based biomarker (MS-bio) to predict the response to ICIs therapy. Based on differentially mutated genes, we extracted six mutational signatures (single-base substitution (SBS)-A, SBS-B, SBS-C, SBS-D, double-base substitution (DBS)-A, and DBS-B) as MS-bio, and constructed a random forest (RF) model to predict the response. Internal and external validations consistently demonstrated the excellent predictive capability of MS-bio, with an accuracy reaching up to 0.82. Moreover, MS-bio exhibited superior performance compared to existing biomarkers. To further validate the accuracy of MS-bio, we explored its performance in The Cancer Genome Atlas (TCGA) cohort and found that the predicted responders were immunologically "hot". Finally, we found that SBS-C had the highest importance in prediction and was related to T cell differentiation. Overall, here we introduced MS-bio as a novel biomarker for accurately predicting the response to ICIs therapy, thereby contributing to the advancement of precision medicine.

Comprehensive pan-cancer single-cell analysis reveals glycolysis-related signatures as predictive biomarkers for immunotherapy response and their role in bladder cancer

Glycolysis is a vital metabolic biological process in tumor progression and immune modulation. This study comprehensively investigated the roles of glycolysis in pan-cancer, especially in bladder cancer. Exploration of 34 single-cell RNA sequencing (scRNA-seq) cohorts, eight ICI-treated bulk RNA-seq cohorts, and TCGA bulk pan-cancer RNA-seq cohorts uncovered a Glycolysis.Sig which strongly correlated with immunotherapy response and demonstrated excellent predictive performance in prognosis and immune response. Hub-Glycolysis.Sig exhibited varying interactions with the immune microenvironment based on cancer type. In bladder cancer, higher glycolysis risk scores correlated with poorer prognosis, with distinct immune infiltration characteristics between subtypes. scRNA-seq revealed high glycolysis levels in bladder epithelial cells. COPB2 was highly expressed in bladder cancer, promoting cell proliferation, migration, and glycolytic activity in vitro and in vivo. Our large-scale data analysis confirmed the negative correlation between glycolysis and immunotherapy outcomes, identifying Glycolysis.Sig as a novel predictive biomarker. Hub-Glycolysis.Sig provides clinical insights for bladder cancer therapy strategies, while COPB2 and other potential therapeutic targets facilitate personalized cancer treatment.

Unravelling Cancer Immunity: Coagulation.Sig and BIRC2 as Predictive Immunotherapeutic Architects

Immune checkpoint inhibitors (ICIs) represent a groundbreaking advancement in cancer therapy, substantially improving patient survival rates. Our comprehensive research reveals a significant positive correlation between coagulation scores and immune-related gene expression across 30 diverse cancer types. Notably, tumours exhibiting high coagulation scores demonstrated enhanced infiltration of cytotoxic immune cells, including CD8+ T cells, natural killer (NK) cells, and macrophages. Leveraging the TCGA pan-cancer database, we developed the Coagulation.Sig model, a sophisticated predictive framework utilising a coagulation-related genes (CRGs) to forecast immunotherapy outcomes. Through rigorous analysis of ten ICI-treated cohorts, we identified and validated seven critical CRGs: BIRC2, HMGB1, STAT2, IFNAR1, BID, SPATA2, IL33 and IFNG, which form the foundation of our predictive model. Functional analyses revealed that low-risk tumours characterised by higher immune cell populations, particularly CD8+ T cells, demonstrated superior ICI responses. These tumours also exhibited increased mutation rates, elevated neoantigen loads, and greater TCR/BCR diversity. Conversely, high-risk tumours displayed pronounced intratumor heterogeneity (ITH) and elevated NRF2 pathway activity, mechanisms strongly associated with immune evasion. Experimental validation highlighted BIRC2 as a promising therapeutic target. Targeted BIRC2 knockdown, when combined with anti-PD-1 therapy, significantly suppressed tumour growth, enhanced CD8+ T cell infiltration, and amplified IFN-γ and TNF-α secretion in tumour models. Our findings position the Coagulation.Sig model as a novel, comprehensive approach to personalised cancer treatment, with BIRC2 emerging as both a predictive biomarker and a potential therapeutic intervention point.

iMLGAM: Integrated Machine Learning and Genetic Algorithm-driven Multiomics analysis for pan-cancer immunotherapy response prediction

To address the substantial variability in immune checkpoint blockade (ICB) therapy effectiveness, we developed an innovative R package called integrated Machine Learning and Genetic Algorithm-driven Multiomics analysis (iMLGAM), which establishes a comprehensive scoring system for predicting treatment outcomes through advanced multi-omics data integration. Our research demonstrates that iMLGAM scores exhibit superior predictive performance across independent cohorts, with lower scores correlating significantly with enhanced therapeutic responses and outperforming existing clinical biomarkers. Detailed analysis revealed that tumors with low iMLGAM scores display distinctive immune microenvironment characteristics, including increased immune cell infiltration and amplified antitumor immune responses. Critically, through clustered regularly interspaced short palindromic repeats screening, we identified Centrosomal Protein 55 (CEP55) as a key molecule modulating tumor immune evasion, mechanistically confirming its role in regulating T cell-mediated antitumor immune responses. These findings not only validate iMLGAM as a powerful prognostic tool but also propose CEP55 as a promising therapeutic target, offering novel strategies to enhance ICB treatment efficacy. The iMLGAM package is freely available on GitHub (https://github.com/Yelab1994/iMLGAM), providing researchers with an innovative approach to personalized cancer immunotherapy prediction.

Advances in Polymer-Based Self-Adjuvanted Nanovaccines

Nanovaccines, as a new generation of vaccines, have garnered significant interest due to their exceptional potential in enhancing disease prevention and treatment. Their unique features, such as high stability, antigens protection, prolonged retention, and targeted delivery to lymph nodes, immune cells, and tumors, set them apart as promising candidates in the field of immunotherapy. Polymers, with their superior degradability, capacity to mimic pathogen characteristics, and surface functionality that facilitates modifications, serve as ideal carriers for vaccine components. Polymer-based self-adjuvanted nanovaccines have the remarkable ability to augment immune responses. The inherent adjuvant-like properties of polymers themselves offer a pathway toward more efficient exploitation of nanomaterials and the optimization of nanovaccines. This review article aims to summarize the categorization of polymers and elucidate their mechanisms of action as adjuvants. Additionally, it delves into the advantages and limitations of polymer-based self-adjuvanted nanovaccines in disease management and prevention, providing valuable insights for their design and application. This comprehensive analysis could contribute to the development of more effective and tailored nanovaccines for a wide range of diseases.

Curcumin modulates purinergic signaling and inflammatory response in cutaneous metastatic melanoma cells

Cutaneous melanoma (CM) poses a therapeutic challenge due to its aggressive nature and often limited response to conventional treatments. Exploring novel therapeutic targets is essential, and natural compounds have emerged as potential candidates. This study aimed to elucidate the impact of curcumin, a natural compound known for its anti-inflammatory, antioxidant, and anti-tumor properties, on metastatic melanoma cells, focusing on the purinergic system and immune responses. Human melanoma cell line SK-Mel-28 were exposed to different curcumin concentrations for either 6 or 24 h, after which we assessed components related to the purinergic system and the inflammatory cascade. Using RT-qPCR, we assessed the gene expression of CD39 and CD73 ectonucleotidases, as well as adenosine deaminase (ADA). Curcumin effectively downregulated CD39, CD73, and ADA gene expression. Flow cytometry analysis revealed that curcumin significantly reduced CD39 and CD73 protein expression at specific concentrations. Moreover, the A2A receptor's protein expression decreased across all concentrations. Enzymatic activity assays demonstrated that curcumin modulated CD39, CD73, and ADA activities, with effects dependent on concentration and duration of treatment. Extracellular ATP levels increased after 24 h of curcumin treatment, emphasizing its role in modulating hydrolytic activity. Curcumin also displayed anti-inflammatory properties by reducing NLRP3 gene expression and impacting the levels of key inflammatory cytokines. In conclusion, this study unveils the potential of curcumin as a promising adjuvant in CM treatment. Curcumin modulates the expression and activity of crucial components of the purinergic system and exhibits anti-inflammatory effects, indicating its potential therapeutic role in combating CM. These findings underscore curcumin's promise and warrant further investigation in preclinical and clinical settings for melanoma management.

NLRP3 inflammasome-based therapies by natural products: a new development in the context of cancer therapy

The leucine-rich repeat containing protein (NLR) canonical inflammasome family includes Nod-like receptor protein 3 (NLRP3). Via the mediation of apoptosis proteins and immunological reactions, it controls the pathogenesis of malignancy. Experimental studies showed a relationship among lymphogenesis, cancer metastasis, and NLRP3 expression. Natural products have also been used as lead-based substances in a number of investigations to speed up the creation of novel, specific NLRP3 inhibitors. Via the mediation of apoptotic proteins and immunological responses, it controls the pathogenesis of malignancy. Moreover, it was recently noted that among human cancers, chemotherapy activates NLRP3. Induction of NLRP3 could encourage the generation of IL-1β and IL-22 to facilitate the propagation of malignancy. Additionally, prior research has demonstrated that the usage of NLRP3 in cancer therapy may result in resistance to drugs. The depletion of NLRP3 could affect the survival of cells. Natural products have been used as lead materials in a number of studies to help generate novel, specific NLRP3 antagonists more quickly. In the present review, we examine the mechanism behind the beneficial effects of the natural substances on the inhibition of cancer growth and progression, with special focus on NLRP3 regulation.

The Role of the Tumor Microenvironment (TME) in Advancing Cancer Therapies: Immune System Interactions, Tumor-Infiltrating Lymphocytes (TILs), and the Role of Exosomes and Inflammasomes

Understanding how different contributors within the tumor microenvironment (TME) function and communicate is essential for effective cancer detection and treatment. The TME encompasses all the surroundings of a tumor such as blood vessels, fibroblasts, immune cells, signaling molecules, exosomes, and the extracellular matrix (ECM). Subsequently, effective cancer therapy relies on addressing TME alterations, known drivers of tumor progression, immune evasion, and metastasis. Immune cells and other cell types act differently under cancerous conditions, either driving or hindering cancer progression. For instance, tumor-infiltrating lymphocytes (TILs) include lymphocytes of B and T cell types that can invade malignancies, bringing in and enhancing the ability of immune system to recognize and destroy cancer cells. Therefore, TILs display a promising approach to tackling the TME alterations and have the capability to significantly hinder cancer progression. Similarly, exosomes and inflammasomes exhibit a dual effect, resulting in either tumor progression or inhibition depending on the origin of exosomes, type of inflammasome and tumor. This review will explore how cells function in the presence of a tumor, the communication between cancer cells and immune cells, and the role of TILs, exosomes and inflammasomes within the TME. The efforts in this review are aimed at garnering interest in safer and durable therapies for cancer, in addition to providing a promising avenue for advancing cancer therapy and consequently improving survival rates.

The intervention of NLRP3 inflammasome inhibitor: oridonin against azoxymethane and dextran sulfate sodium-induced colitis-associated colorectal cancer in male BALB/c mice

Colorectal cancer (CRC) ranks third globally in cancer diagnoses. The dysregulation of the NLRP3 inflammasome is prominently linked to several types of cancers. Oridonin, a principal component of Rabdosia rubescens, exhibits inhibitory activity against NLRP3 and is well-recognized for its diverse pharmacological benefits. However, its role in an animal model of colitis-associated colorectal cancer (CACC) remains unexplored. In the present study, the effectiveness of oridonin was investigated against CACC, developed using azoxymethane (AOM), a tumour initiator, and dextran sulphate sodium (DSS), a tumour promoter, in male BALB/c mice. The two-stage murine model of inflammation-associated cancer was established by administering AOM (10 mg/kg b.w.; i.p., once) followed by DSS (2% w/v) in drinking water (3 cycles, 7 days/cycle). Over a span of 10 weeks, the dose-dependent (2.5, 5, and 10 mg/kg, b.w.; i.p.) effects of oridonin were investigated in BALB/c mice. Oridonin significantly alleviated CACC severity, as evidenced by reduced DAI scores and restored body weight. Moreover, it attenuated surrogate markers of inflammation, including myeloperoxidase, nitrite, plasma LPS, TNF-α, IL-1β, and DNA damage. Histopathological examination revealed diminished tumorigenesis and apoptotic cells, corroborated by reduced Ki-67 and TNF-α, along with increased p53 expression in the colon. Following oridonin treatment, IHC/immunofluorescence analyses demonstrated a significantly reduced expression of the components of NLRP3 inflammasome including NLRP3, ASC-1, and caspase-1. Notably, the high dose of oridonin (10 mg/kg) consistently exhibited significant protective effects against CACC by modulating various molecular targets. Present findings confirmed the potential of oridonin in the protection of colitis-associated colorectal cancer, providing valuable insights into its mechanism of action and clinical significance.

NLRP3-inflammasome Related Genes as Emerging Biomarkers and Therapeutic Targets in Psoriasis

The NLRP3 inflammasome is closely associated with inflammatory diseases, including psoriasis. Objective diagnostic biomarkers and alternative therapies for psoriasis remain limited. We aimed to identify reliable biomarkers for the diagnosis of psoriasis and investigate potential therapy strategies. Machine learning methods were performed in over 1000 skin samples from public transcriptome database to identify NLRP3 inflammasome-related biomarkers. Multivariate Cox regression analysis was used to establish the biomarker-based diagnostic model. TNF-induced HaCaT cell model was used to evaluate biomarker-related inflammatory changes. Biomarker-targeting drugs was predicted with NetworkAnalyst database and validated in imiquimod (IMQ)-induced mouse model. Elevated level of four NLRP3 inflammasome-related biomarkers, including NLRP3, ASC, TXNIP and CASP-1, were identified from the public psoriasis transcriptome samples and validated in our local psoriasis skin biopsies. The biomarker-based diagnostic model was developed from training dataset and validation dataset, which both showed significant diagnostic value for psoriasis. Knocking down one of these genes in vitro showed reduced inflammatory factors, reduced cell apoptosis and improved cell viability. Furthermore, Predictive biomarker-targeting therapeutics, including resveratrol and JQ-1, demonstrated effective alleviation of psoriasis severity and reduced inflammation in IMQ-induced psoriasis mice. Combinational evaluation of NLRP3, ASC, TXNIP and CASP-1 may constitute a novel diagnostic approach for psoriasis. Targeting these proteins provide more options for psoriasis therapy.

Inflammation in cancer: therapeutic opportunities from new insights

As one part of the innate immune response to external stimuli, chronic inflammation increases the risk of various cancers, and tumor-promoting inflammation is considered one of the enabling characteristics of cancer development. Recently, there has been growing evidence on the role of anti-inflammation therapy in cancer prevention and treatment. And researchers have already achieved several noteworthy outcomes. In the review, we explored the underlying mechanisms by which inflammation affects the occurrence and development of cancer. The pro- or anti-tumor effects of these inflammatory factors such as interleukin, interferon, chemokine, inflammasome, and extracellular matrix are discussed. Since FDA-approved anti-inflammation drugs like aspirin show obvious anti-tumor effects, these drugs have unique advantages due to their relatively fewer side effects with long-term use compared to chemotherapy drugs. The characteristics make them promising candidates for cancer chemoprevention. Overall, this review discusses the role of these inflammatory molecules in carcinogenesis of cancer and new inflammation molecules-directed therapeutic opportunities, ranging from cytokine inhibitors/agonists, inflammasome inhibitors, some inhibitors that have already been or are expected to be applied in clinical practice, as well as recent discoveries of the anti-tumor effect of non-steroidal anti-inflammatory drugs and steroidal anti-inflammatory drugs. The advantages and disadvantages of their application in cancer chemoprevention are also discussed.

Transcriptome and single-cell profiling of the mechanism of diabetic kidney disease

BACKGROUND

The NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasome may play an important role in diabetic kidney disease (DKD). However, the exact link remains unclear.

AIM

To investigate the role of the NLRP3 inflammasome in DKD.

METHODS

Using datasets from the Gene Expression Omnibus database, 30 NLRP3 inflammasome-related genes were identified. Differentially expressed genes were selected using differential expression analysis, whereas intersecting genes were selected based on overlapping differentially expressed genes and NLRP3 inflammasome-related genes. Subsequently, three machine learning algorithms were used to screen genes, and biomarkers were identified by overlapping the genes from the three algorithms. Potential biomarkers were validated by western blotting in a db/db mouse model of diabetes.

RESULTS

Two biomarkers, sirtuin 2 (SIRT2) and caspase 1 (CASP1), involved in the Leishmania infection pathway were identified. Both biomarkers were expressed in endothelial cells. Pseudo-temporal analysis based on endothelial cells showed that DKD mostly occurs during the mid-differentiation stage. Western blotting results showed that CASP1 expression was higher in the DKD group than in the control group (P < 0.05), and SIRT2 content decreased (P < 0.05).

CONCLUSION

SIRT2 and CASP1 provide a potential theoretical basis for DKD treatment.

The critical role of NLRP3 in drug resistance of cancers: Focus on the molecular mechanisms and possible therapeutics

Nod-like receptor protein 3 (NLRP3) is a member of the leucine-rich repeat-containing protein (NLR) canonical inflammasome family. It regulates the pathophysiology of cancer by facilitating immune responses and apoptotic proteins. Furthermore, it has been observed that chemotherapy activates NLRP3 in human malignancies. The secretion of IL-1β and IL-22 to promote cancer spread may be triggered by NLRP3 activation. Furthermore, earlier studies have exhibited that NLRP3 may cause medication resistance when used in cancer treatments given that cell viability may be regulated by NLRP3 depletion. Additionally, clinical studies have demonstrated correlation between NLRP3 expression, lymphogenesis, and cancer metastasis. Various NLRP3 agonists may cause the EMT process, stimulate IL-1β and Wnt/β-catenin signaling, and alter miRNA function in drug-resistant cells. This review seeks to clarify the possibility involvement of NLRP3-related pathways in the control of cancer cells' resistance to widely used treatment approaches, such as chemotherapy. In the end, an improved perception of the corresponding mechanisms behind NLRP3's tumor-supporting activities will help NLRP3-based treatments advance in the future.

A Distinguished Roadmap of Fibroblast Senescence in Predicting Immunotherapy Response and Prognosis Across Human Cancers

The resistance of tumors to immune checkpoint inhibitors (ICI) may be intricately linked to cellular senescence, although definitive clinical validation remains elusive. In this study, comprehensive pan-cancer scRNA-seq analyses identify fibroblasts as exhibiting the most pronounced levels of cellular senescence among tumor-associated cell populations. To elucidate this phenomenon, a fibroblast senescence-associated transcriptomic signature (FSS), which correlated strongly with protumorigenic signaling pathways and immune dysregulation that fosters tumor progression, is developed. Leveraging the FSS, the machine learning (ML) framework demonstrates exceptional accuracy in predicting ICI response and survival outcomes, achieving superior area under curve (AUC) values across validation, testing, and in-house cohorts. Strikingly, FSS consistently outperforms established signatures in predictive robustness across diverse cancer subtypes. From an integrative analysis of 17 CRISPR/Cas9 libraries, CDC6 emerges as a pivotal biomarker for pan-cancer ICI response and prognostic stratification. Mechanistically, experimental evidence reveals that CDC6 in tumor cells orchestrates fibroblast senescence via TGF-β1 secretion and oxidative stress, subsequently reprogramming the tumor microenvironment and modulating ICI response. These findings underscore the translational potential of targeting fibroblast senescence as a novel therapeutic strategy to mitigate immune resistance and enhance antitumor efficacy.

Inflammasome activation in melanoma progression: the latest update concerning pathological role and therapeutic value

The progression of melanoma is a complex process influenced by both internal and external cues which encourage the transition of tumour cells, uncontrolled growth, migration, and metastasis. Additionally, inflammation allows tumours to evade the immune system, contributing to cancer development. The inflammasome, a complex of many proteins, is crucial in enhancing immune responses to external and internal triggers. As a critical inflammatory mechanism, it contributes to the development of melanoma. These mechanisms may be triggered via various internal and external stimuli, causing the induction of specific enzymes such as caspase-1, caspase-11, or caspase-8. This, in turn, leads to the release of interleukin (IL)-1β and IL-18 and cell death by apoptosis and pyroptosis. Proper inflammasome stimulation is crucial for the host to deal with invading pathogens or tissue injury. However, inappropriate inflammasome stimulation can result in unregulated tissue reactions, thus easing many diseases, including melanoma. Hence, keeping a delicate equilibrium between the stimulation and prohibition of inflammasomes is crucial, necessitating meticulous control of the assembly and functional aspects of inflammasomes. This review examines the latest advancements in inflammasome studies, specifically focusing on the molecular processes that control inflammasome formation, signalling, and modulation in melanoma.

Characterization of NOD-like receptor-based molecular heterogeneity in glioma and its association with immune micro-environment and metabolism reprogramming

Background and purpose

The characteristics and role of NOD-like receptor (NLR) signaling pathway in high-grade gliomas were still unclear. This study aimed to reveal the association of NLR with clinical heterogeneity of glioblastoma (GBM) patients, and to explore the role of NLR pathway hub genes in the occurrence and development of GBM.

Methods

Transcriptomic data from 496 GBM patients with complete prognostic information were obtained from the TCGA, GEO, and CGGA databases. Using the NMF clustering algorithm and the expression profiles of NLR genes, these 496 GBM patients were classified into different clinical subtypes. The pathway activity of NLR and the immune micro-environment characteristics were then compared between these subtypes. A novel and accurate NLR expression profile-based prognostic marker for GBM was developed using LASSO and COX regression analysis.

Results

Based on the NLR gene expression profile, GBM patients were accurately divided into two clinical subtypes (C1 and C2) with different clinical outcomes. The two groups of patients showed different immune microenvironment characteristics and metabolic characteristics, which might be the potential reason for the difference in prognosis. Differential expression and enrichment analyzes revealed intrinsic gene signature differences between C1 and C2 subtypes. Based on the differential expression profiles of C1 and C2, prognostic molecular markers related to NLR were developed. The AUC value of the 3-year ROC curve ranged from 0.601 to 0.846, suggesting its potential clinical significance. Single-cell sequencing analysis showed that the NLR gene was mainly active in myeloid cells within GBM. The random forest algorithm identified the crucial role of TRIP6 gene in NLR pathway. Molecular biology experiments confirmed that TRIP6 was abnormally overexpressed in GBM. Knockdown of TRIP6 gene can significantly inhibit the proliferation and migration ability of GBM cells.

Conclusion

The NLR signaling pathway plays a critical role in regulating immune microenvironment and metabolism reprogramming of GBM. TRIP6 is a potential hub gene within the NLR pathway and affects the malignant biological behavior of GBM cells.

Intestinal Subtype as a Biomarker of Response to Neoadjuvant Immunochemotherapy in Locally Advanced Gastric Adenocarcinoma: Insights from a Prospective Phase II Trial

PURPOSE

Neoadjuvant immunochemotherapy (NAIC) markedly induces pathologic regression in locally advanced gastric adenocarcinoma. However, specific biomarkers are still lacking to effectively identify the beneficiary patients for NAIC.

PATIENTS AND METHODS

A prospective, single-arm, phase II study was conducted to treat locally advanced gastric adenocarcinoma with NAIC (NCT05515796). Correlation between clinicopathologic characteristics and neoadjuvant efficacy was investigated. Bulk RNA sequencing data from 104 samples (from 75 patients in two independent cohorts) and single-cell RNA sequencing data from 105 treatment-naïve gastric adenocarcinomas were comprehensively analyzed to decipher the association of epithelial and microenvironmental characteristics and clinical responses.

RESULTS

The prespecified primary endpoints were achieved: pathologic complete regression rate was 30%, major pathologic regression rate was 43%, and the regimen was well tolerated. Analysis of baseline clinical-pathologic parameters revealed the intestinal subtype of Lauren's classification as a key feature stratifying patients with increased sensitivity to NAIC. Mechanistically, an increased pool of DNA damage repair-active cancer cells and enrichment of CLEC9A+ dendritic cells in the tumor microenvironment were associated with enhanced responsiveness of the intestinal subtype gastric adenocarcinoma to NAIC. More importantly, an intestinal subtype-specific signature model was constructed by the machine learning algorithm NaiveBayes via integrating the transcriptomic features of both DNA damage repair-active cancer cells and CLEC9A+ dendritic cells, which accurately predicted the efficacy of NAIC in multiple independent gastric adenocarcinoma cohorts.

CONCLUSIONS

Intestinal subtype is a histologic biomarker of enhanced sensitivity of gastric adenocarcinoma to NAIC. The intestinal subtype-specific signature model is applicable to guide NAIC for patients with locally advanced gastric adenocarcinoma.

Screening of Herbs with Potential Modulation of NLRP3 Inflammasomes for Acute Liver Failure: A Study Based on the Herb-Compound-Target Network and the ssGSEA Algorithm

OBJECTIVE

NLRP3 inflammasomes are considered to be key factors in the pathogenesis of Acute Liver Failure (ALF). Some Traditional Chinese Medicines (TCMs) have shown protective and therapeutic effects against ALF by inhibiting NLRP3 inflammasomes. However, the inhibitory effects of most TCMs on ALF remain to be further elucidated. This study aimed to screen potential herbs that can treat ALF based on the inhibition of NLRP3 inflammasomes.

METHODS

Initially, we constructed the target set for 502 herbs. Subsequently, based on the target set and the gene set related to the NLRP3 inflammasome, using the ssGSEA algorithm, we evaluated herb scores and NLRP3 scores in the ALF expression matrix and performed a preliminary herb screening based on score correlations. Through bioinformatics approaches, we identified the key targets for candidate herbs and determined core herbs based on the herb-compound-target network. Furthermore, molecular docking and molecular biology methods validated the screening results of the herbs.

RESULTS

A total of 18 crucial targets associated with the inhibition of the NLRP3 inflammasome were identified, which included ALDH2, HMOX1, and VEGFA. Subsequently, based on these key targets, a set of 10 primary herbs was chosen, notably Qinghao, Duzhong, and Gouteng. Moreover, the results were verified through molecular docking and molecular dynamic simulation.

CONCLUSION

Ten key herbs have been identified as potential inhibitors of the NLRP3 inflammasome, offering insights into ALF therapy for drug development.

Comprehensive Pan-cancer Analysis of CMPK2 as Biomarker and Prognostic Indicator for Immunotherapy

BACKGROUND

UMP-CMP kinase 2 (CMPK2) is involved in mitochondrial DNA synthesis, which can be oxidized and released into the cytoplasm in innate immunity. It initiates the assembly of NLRP3 inflammasomes and mediates various pathological processes such as human immunodeficiency virus infection and systemic lupus erythematosus. However, the role of CMPK2 in tumor progression and tumor immunity remains unclear.

METHODS

We identified CMPK2 expression patterns in the Genotype Tissue-Expression (GTEx), The Cancer Genome Atlas (TCGA), and the Cancer Cell Line Encyclopedia (CCLE) databases. Validation was performed using immunohistochemical staining data from the Human Protein Atlas (HPA) database and qPCR experiments. Receiver operating characteristic curve analysis and Kaplan-Meier survival analysis were conducted to assess the clinical relevance of CMPK2 expression. The Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) algorithm and the Tumor IMmune Estimation Resource (TIMER) database were used to evaluate the correlation between CMPK2 and immune infiltration in tumors. The Tumor Immune Syngeneic Mouse (TISMO) database and other public datasets were utilized to assess the impact of CMPK2 on immune therapy response. MEXPRESS and MethSurv databases were employed to investigate the effects of methylation on CMPK2 expression.

RESULTS

CMPK2 expression was elevated in 23 cancers and decreased in two cancers. Furthermore, CMPK2 expression had a high diagnostic value for 16 cancers. Elevated CMPK2 expression was associated with lower overall survival (OS), disease-specific survival (DSS), and progression- free interval (PFI) in four cancers. Immune microenvironment-related analysis revealed strong associations between CMPK2 expression and immune cell infiltration, as well as immune checkpoint expression across various tumors. Notably, in four mouse immunotherapy cohorts, CMPK2 expression in treated mouse tumors was higher post-treatment. In five clinical immunotherapy cohorts, patients with high CMPK2 expression show better responses to immunotherapy. Moreover, the methylation level of CMPK2 gene was closely correlated to its expression and tumor prognosis. Among these cancers, the clinical and immunological indications of skin cutaneous melanoma (SKCM) are particularly closely related to CMPK2 expression.

CONCLUSION

Our analysis preliminarily describes the complex function of CMPK2 in cancer progression and immune microenvironment, highlighting its potential as a diagnostic and therapeutic target for immunotherapy.

Identification of immunogenic cell death gene-related subtypes and risk model predicts prognosis and response to immunotherapy in ovarian cancer

Background

Immunogenic cell death (ICD) has been associated with enhanced anti-tumor immunotherapy by stimulating adaptive immune responses and remodeling the immune microenvironment in tumors. Nevertheless, the role of ICD-related genes in ovarian cancer (OC) and tumor microenvironment remains unexplored.

Methods

In this study, high-throughput transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases as training and validation sets separately were obtained and proceeded to explore ICD-related clusters, and an ICD-related risk signature was conducted based on the least absolute shrinkage and selection operator (LASSO) Cox regression model by iteration. Multiple tools including CIBERSORT, ESTIMATE, GSEA, TIDE, and immunohistochemistry were further applied to illustrate the biological roles of ICD-related genes as well as the prognostic capacity of ICD risk signature in OC.

Results

Two ICD-related subtypes were identified, with the ICD-high subtype showing more intense immune cell infiltration and higher activities of immune response signaling, along with a favorable prognosis. Additionally, four candidate ICD genes (IFNG, NLRP3, FOXP3, and IL1B) were determined to potentially impact OC prognosis, with an upregulated expression of NLRP3 in OC and metastatic omental tissues. A prognostic model based on these genes was established, which could predict overall survival (OS) and response to immunotherapy for OC patients, with lower-risk patients benefiting more from immunotherapy.

Conclusion

Our research conducted a prognostic and prediction of immunotherapy response model based on ICD genes, which could be instrumental in assessing prognosis and assigning immunotherapeutic strategies for OC patients. NLRP3 is a promising target for prognosis in OC.

Integrating necroptosis into pan-cancer immunotherapy: a new era of personalized treatment

Introduction

Necroptosis has emerged as a promising biomarker for predicting immunotherapy responses across various cancer types. Its role in modulating immune activation and therapeutic outcomes offers potential for precision oncology.

Methods

A comprehensive pan-cancer analysis was performed using bulk RNA sequencing data to develop a necroptosis-related gene signature, termed Necroptosis.Sig. Multi-omics approaches were employed to identify critical pathways and key regulators of necroptosis, including HMGB1. Functional validation experiments were conducted in A549 lung cancer cells to evaluate the effects of HMGB1 knockdown on tumor proliferation and malignancy.

Results

The Necroptosis.Sig gene signature effectively predicted responses to immune checkpoint inhibitors (ICIs). Multi-omics analyses highlighted HMGB1 as a key modulator of necroptosis, with potential to enhance immune activation and therapeutic efficacy. Functional experiments demonstrated that HMGB1 knockdown significantly suppressed tumor proliferation and malignancy, reinforcing the therapeutic potential of targeting necroptosis.

Discussion

These findings underscore the utility of necroptosis as a biomarker to guide personalized immunotherapy strategies. By advancing precision oncology, necroptosis provides a novel avenue for improving cancer treatment outcomes.

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.

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

The widespread use of high-throughput sequencing technologies has revolutionized the understanding of biology and cancer heterogeneity. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients' outcome and clinical response. However, an open-source R package covering state-of-the-art machine-learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct a machine learning-based integration model with elegant performance (Mime). Mime streamlines the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting the outcomes of patients compared with other published models. Furthermore, the PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from the PIEZO1-associated signatures demonstrated high potential as a glioma target. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields. The Mime package is available on GitHub (https://github.com/l-magnificence/Mime).

Bioinformatic and experimental data pertaining to the role of the NLRP3 inflammasome in ovarian cancer

The Nod-Like Receptor (NLR) family pyrin domain containing 3 (NLRP3) inflammasome plays a role in regulating inflammatory signaling and is a well-established contributor to pyroptotic cell death. It has been investigated extensively in cancer but there remains limited evidence of its role within ovarian cancer (OC). Bioinformatic investigation of gene expression data has highlighted that higher expression of NLRP3 and genes associated with the NLRP3 complex appear to be positively correlated with OC and may also have prognostic significance. However, heterogeneity exists within the results and experimental data is limited and contradictory. If the NLRP3 inflammasome is to be exploited as a therapeutic target, further laboratory-based investigation is required to determine its role in cancer. Furthermore, its relationship with clinically important characteristics such as histopathological subtype may be of key significance in developing targeted therapies towards specific cohorts of patients.

Pan-cancer analysis and experimental validation of FPR3 as a prognostic and immune infiltration-related biomarker for glioma

Formyl peptide receptor 3 (FPR3) is known to have implications in the progression of various cancer types. Despite this, its biological significance within pan-cancer datasets has yet to be investigated. In this investigation, we scrutinized FPR3's expression profiles, genetic alterations, prognostic significance, immune-related characteristics, methylation status, tumor mutation burden (TMB), and microsatellite instability (MSI) across different types of cancer. We utilized TISCH's single-cell data to identify immune cells closely associated with FPR3. The predictive significance of FPR3 was evaluated independently in gliomas using data from TCGA and CGGA datasets, leading to the development of a prognostic nomogram. Immunohistochemistry and Western blot analysis confirmed FPR3 expression in gliomas. Lastly, the CCK-8 and wound-healing assays were employed to assess the impact of FPR3 on the proliferation and metastasis of GBM cell lines. In numerous cancer types, heightened FPR3 expression correlated with adverse outcomes, immune cell infiltration, immune checkpoints, TMB, and MSI. In glioma, FPR3 emerged as a notable risk factor, with the prognostic model effectively forecasting patient results. The potential biological relevance of FPR3 was confirmed in glioma, and it was shown to have significant involvement in the processes of glioma growth, immune infiltration, and metastasis. Our results imply a potential association of FPR3 with tumor immunity, indicating its viability as a prognostic indicator in glioma.

Characterization of prognostic signature related with twelve types of programmed cell death in lung squamous cell carcinoma

OBJECTIVE

This study aimed to develop a prognostic cell death index (CDI) based on the expression of genes related with various types of programmed cell death (PCD), and to assess its clinical relevance in lung squamous cell carcinoma (LUSC).

METHODS

PCD-related genes were gathered and analyzed in silico using the transcriptomic data from the LUSC cohorts of The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Differentially expressed PCD genes were analyzed, and a prognostic model was subsequently constructed. CDI scores were calculated for each patient, and their correlations with clinical features, survival outcomes, tumor mutation burden, gene clusters, and tumor microenvironment were investigated. Unsupervised consensus clustering was performed based on CDI model genes. Furthermore, the correlation of CDI for sensitivity of targeted drugs, chemotherapy efficacy, and immunotherapy responses was assessed.

RESULTS

Based on 351 differentially expressed PCD genes in LUSC, a CDI signature comprising FGA, GAB2, JUN, and CDKN2A was identified. High CDI scores were significantly associated with poor survival outcomes (p < 0.05). Unsupervised clustering revealed three distinct patient subsets with varying survival rates. CDKN2A exhibited significantly different mutation patterns between patients with high and low CDI scores (p < 0.01). High CDI scores were also linked to increased immune cell infiltration of specific subsets and altered expression of immune-related genes. Patients with high-CDI showed reduced sensitivity to several chemotherapeutic drugs and a higher Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating potential resistance to immunotherapy.

CONCLUSION

The CDI signature based on PCD genes offers valuable prognostic insights into LUSC, reflecting molecular heterogeneity, immune microenvironment associations, and potential therapeutic challenges. The CDI holds potential clinical utility in predicting treatment responses and guiding the selection of appropriate therapies for patients with LUSC. Future studies are warranted to further validate the prognostic value of CDI in combination with clinical factors and to explore its application across diverse patient cohorts.

Immune landscape in the glomerular transcriptome of nephrotic syndrome and anca-associated vasculitis

BACKGROUND

ANCA-associated vasculitis (AAV), and nephrotic syndrome encompassing diseases including minimal change disease (MCD), focal and segmental glomerulosclerosis (FSG), membranous nephropathy (MN), remain a challenge due to their varied immunological characteristics. Recent therapeutic advancements have highlighted the importance of understanding these diseases' immunological landscapes.

METHODS

This study analyzed transcriptomics data from renal glomerular tissues of patients with AAV, FSG, MCD, MN, and normal controls. Utilizing an immune-related gene set of 883 genes, methods including Gene Set Variation Analysis (GSVA), LASSO regression, and Weighted Correlation Network Analysis (WGCNA) were used. Predictions of immune cell compositions were made through CIBERSORT, TIMER, MCPcounter, and quanTIseq algorithms.

RESULTS

The study revealed distinct immunogenetic pathways enriched in each disease: hematopoietic cell lineage in ANCA, linoleic acid metabolism in FSG, PPAR signaling in MCD, and drug metabolism in MN. Classifiers based on immune gene expression showed high accuracy (AUC: ANCA 0.812, FSG 0.99, MCD 1, MN 0.888). Co-expression modules and PPI networks highlighted unique pathways for each disease. Predictions of immune cell composition showed elevated macrophages in FSG and MN, with Treg levels elevated across all four diseases compared to normal controls and highest in FSG. Correlation analyses demonstrated significant associations between classifier scores and immune cell types.

CONCLUSION

This study offers accurate classifiers for AAV, FSG, MCD, and MN, and reveals distinct immunological pathways. These findings advance personalized treatments and highlight potential therapeutic targets in AAV and nephrotic syndrome. Further research should validate these results for clinical applications.

CCL5 promotes the epithelial-mesenchymal transition of circulating tumor cells in renal cancer

BACKGROUND

Circulating tumor cells (CTCs) are pivotal in tumor metastasis across cancers, yet their specific role in renal cancer remains unclear.

METHODS

This study investigated C-C motif chemokine ligand 5 (CCL5)'s tumorigenic impact on renal cancer cells and CTCs using bioinformatics, in vivo, and in vitro experiments. It also assessed renal cancer patients' CTCs prognostic value through Lasso regression and Kaplan-Meier survival curves.

RESULTS

Bioinformatics analysis revealed differential genes focusing on cellular adhesion and migration between CTCs and tumor cells. CCL5 exhibited high expression in various CTCs, correlating with poor prognosis in renal cancer. In 786-O-CTCs, CCL5 enhanced malignancy, while in renal cell carcinoma cell line CAKI-2 and 786-O, it promoted epithelial-mesenchymal transition (EMT) via smad2/3, influencing cellular characteristics. The nude mouse model suggested CCL5 increased CTCs and intensified EMT, enhancing lung metastasis. Clinical results shown varying prognostic values for different EMT-typed CTCs, with mesenchymal CTCs having the highest value.

CONCLUSIONS

In summary, CCL5 promoted EMT in renal cancer cells and CTCs through smad2/3, enhancing the malignant phenotype and facilitating lung metastasis. Mesenchymal-type CTC-related factors can construct a risk model for renal cancer patients, allowing personalized treatment based on metastatic risk prediction.

Targeting NLRP3 inhibits AML progression by inducing PERK/eIF2-mediated apoptosis

BACKGROUND

Acute myeloid leukemia (AML) is characterized by the abnormal proliferation of myeloid precursor cells and presents significant challenges in treatment due to its heterogeneity. Recently, the NLRP3 inflammasome has emerged as a potential contributor to AML pathogenesis, although its precise mechanisms remain poorly understood.

METHODS

Public genome datasets were utilized to evaluate the expression of NLRP3 inflammasome-related genes (IL-1β, IL-18, ASC, and NLRP3) in AML patients compared to healthy individuals. CRISPR/Cas9 technology was employed to generate NLRP3-deficient MOLM-13 AML cells, followed by comprehensive characterization using real-time PCR, western blotting, FACS analysis, and transmission electron and immunofluorescence microscopy. Proteomic analyses were conducted to identify NLRP3-dependent alterations in protein levels, with a focus on the eIF2 kinase PERK-mediated signaling pathways. Additionally, in vivo studies were performed using a leukemic mouse model to elucidate the pathogenic role of NLRP3 in AML.

RESULTS

Elevated expression of NLRP3 was significantly associated with diminished overall survival in AML patients. Genetic deletion, pharmacological inhibition and silencing by RNA interference of NLRP3 led to decreased AML cell survival through the induction of apoptosis. Proteomic analyses uncovered NLRP3-dependent alterations in protein translation, characterized by enhanced eIF2α phosphorylation in NLRP3-deficient AML cells. Moreover, inhibition of PERK-mediated eIF2α phosphorylation reduced apoptosis by downregulating pro-apoptotic Bcl-2 family members. In vivo studies demonstrated reduced leukemic burden in mice engrafted with NLRP3 knockout AML cells, as evidenced by alleviated leukemic symptoms.

CONCLUSION

Our findings elucidate the involvement of the NLRP3/PERK/eIF2 axis as a novel driver of AML cell survival. Targeting NLRP3-induced signaling pathways, particularly through the PERK/eIF2 axis, presents a promising therapeutic strategy for AML intervention. These insights into the role of the NLRP3 inflammasome offer potential avenues for improving the prognosis and treatment outcomes of AML patients.

Crosstalk between inflammasome sensors and DNA damage response pathways

Eukaryotic cells encounter diverse threats jeopardizing their integrity, prompting the development of defense mechanisms against these stressors. Among these mechanisms, inflammasomes are well-known for their roles in coordinating the inflammatory response against infections. Extensive research has unveiled their multifaceted involvement in cellular processes beyond inflammation. Recent studies emphasize the intricate relationship between the inflammasome and the DNA damage response (DDR). They highlight how the DDR participates in inflammasome activation and the reciprocal impact of inflammasome on DDR and genome integrity preservation. Moreover, novel functions of inflammasome sensors in DDR pathways have emerged, broadening our understanding of their roles. Finally, this review delves into identifying common signals that drive the activation of inflammasome sensors alongside activation cues for the DNA damage response, offering potential insights into shared regulatory pathways between these critical cellular processes.

A pan-cancer cuproptosis signature predicting immunotherapy response and prognosis

Background

Cuproptosis may represent a potential biomarker for predicting prognosis and immunotherapy response, but the available evidence is insufficient.

Methods

The multiple single-cell RNA sequencing (scRNA-seq) datasets were analyzed to investigate the specific occurrence of cuproptosis in distinct cell populations. Utilizing 28 scRNA-seq datasets, TCGA pan-cancer cohort, and 10 immunotherapy cohorts, we developed a cuproptosis signature (Cup.Sig). This signature was used to construct prediction models for immunotherapy response and identify potential prognostic biomarkers for pan-cancer using 11 different machine learning algorithms.

Results

Malignant cells demonstrate the higher cuproptosis scores in comparison to other cell types across diverse cancer types. The Cup.Sig exhibits significant associations with cancer hallmarks and immune cell response in multiple cancer types. Leveraging the Cup.Sig, the robust pan-cancer immunotherapy prediction model and prognostic biomarker have been established and validated using diverse datasets from various platforms.

Conclusions

We developed a pan-cancer cuproptosis signature for predicting survival and immunotherapy response.

The prognostic and immune significance of SLAMF9 in pan-cancer and validation of its role in colorectal cancer

SLAMF9, a member of the conserved lymphocyte activation molecules family (SLAMF), has been less investigated compared to other SLAMs, especially concerning its implications across various cancer types. In our systematic pan-cancer investigation, we observed elevated SLAMF9 expression in various tumor tissues, which was correlated with reduced patient survival across most malignancies. Correlation analyses further revealed significant associations between SLAMF9 expression and immune cell infiltrates, immune checkpoint inhibitors, tumor mutation load, microsatellite instability, and epithelial-mesenchymal transition (EMT) scores. Cell-based assays demonstrated that SLAMF9 knockdown attenuated the proliferative, motile, and invasive capacities of colorectal cancer (CRC) cells. In a nude mouse xenograft model, suppression of SLAMF9 expression substantially inhibited tumor growth. These findings highlight the potential of SLAMF9 as a prognostic and therapeutic biomarker across tumors, with notable implications for CRC cell proliferation and migration.

Integrative analysis of genomic and epigenomic regulation reveals miRNA mediated tumor heterogeneity and immune evasion in lower grade glioma

The expression dysregulation of microRNAs (miRNA) has been widely reported during cancer development, however, the underling mechanism remains largely unanswered. In the present work, we performed a systematic integrative study for genome-wide DNA methylation, copy number variation and miRNA expression data to identify mechanisms underlying miRNA dysregulation in lower grade glioma. We identify 719 miRNAs whose expression was associated with alterations of copy number variation or promoter methylation. Integrative multi-omics analysis revealed four subtypes with differing prognoses. These glioma subtypes exhibited distinct immune-related characteristics as well as clinical and genetic features. By construction of a miRNA regulatory network, we identified candidate miRNAs associated with immune evasion and response to immunotherapy. Finally, eight prognosis related miRNAs were validated to promote cell migration, invasion and proliferation through in vitro experiments. Our study reveals the crosstalk among DNA methylation, copy number variation and miRNA expression for immune regulation in glioma, and could have important implications for patient stratification and development of biomarkers for immunotherapy approaches.

Tumor cell-intrinsic MELK enhanced CCL2-dependent immunosuppression to exacerbate hepatocarcinogenesis and confer resistance of HCC to radiotherapy

BACKGROUND

The outcome of hepatocellular carcinoma (HCC) is limited by its complex molecular characteristics and changeable tumor microenvironment (TME). Here we focused on elucidating the functional consequences of Maternal embryonic leucine zipper kinase (MELK) in the tumorigenesis, progression and metastasis of HCC, and exploring the effect of MELK on immune cell regulation in the TME, meanwhile clarifying the corresponding signaling networks.

METHODS

Bioinformatic analysis was used to validate the prognostic value of MELK for HCC. Murine xenograft assays and HCC lung metastasis mouse model confirmed the role of MELK in tumorigenesis and metastasis in HCC. Luciferase assays, RNA sequencing, immunopurification-mass spectrometry (IP-MS) and coimmunoprecipitation (CoIP) were applied to explore the upstream regulators, downstream essential molecules and corresponding mechanisms of MELK in HCC.

RESULTS

We confirmed MELK to be a reliable prognostic factor of HCC and identified MELK as an effective candidate in facilitating the tumorigenesis, progression, and metastasis of HCC; the effects of MELK depended on the targeted regulation of the upstream factor miR-505-3p and interaction with STAT3, which induced STAT3 phosphorylation and increased the expression of its target gene CCL2 in HCC. In addition, we confirmed that tumor cell-intrinsic MELK inhibition is beneficial in stimulating M1 macrophage polarization, hindering M2 macrophage polarization and inducing CD8 + T-cell recruitment, which are dependent on the alteration of CCL2 expression. Importantly, MELK inhibition amplified RT-related immune effects, thereby synergizing with RT to exert substantial antitumor effects. OTS167, an inhibitor of MELK, was also proven to effectively impair the growth and progression of HCC and exert a superior antitumor effect in combination with radiotherapy (RT).

CONCLUSIONS

Altogether, our findings highlight the functional role of MELK as a promising target in molecular therapy and in the combination of RT therapy to improve antitumor effect for HCC.

Measuring Interactions Between Proteins and Small Molecules or Nucleic Acids

Interactions between proteins and small molecules or nucleic acids play a pivotal role in numerous biological processes critical for human health and are fundamental for advancing our understanding of biological systems. Proteins are the workhorses of the cell, executing various functions ranging from catalyzing biochemical reactions to transmitting signals within the body. Small molecules, including drugs and metabolites, can modulate protein activity, thereby impacting cellular processes and disease pathways. Similarly, nucleic acids, such as DNA and RNA, regulate protein synthesis and function through intricate interactions. Understanding these interactions is crucial for drug discovery and development and can shed light on gene regulation, transcriptional control, and RNA processing, providing insights into genetic diseases and developmental disorders. Moreover, studying protein-small molecule and protein-nucleic acid interactions enhances our comprehension of fundamental biological mechanisms. A wide array of methods to study these interactions range in cost, sensitivity, materials usage, throughput, and complexity. Notably in the last decade, new techniques have been developed that enhance our understanding of these interactions. In this review, we aim to summarize the new state-of-the-art methods for detecting interactions between proteins and small molecules or nucleic acids, as well as discuss older methods that still hold value today. © 2024 Wiley Periodicals LLC.

Screening Nonlinear miRNA Features of Breast Cancer by Using Ensemble Regularized Polynomial Logistic Regression

Differentiating breast cancer subtypes based on miRNA data helps doctors provide more personalized treatment plans for patients. This paper explored the interaction between miRNA pairs and developed a novel ensemble regularized polynomial logistic regression method for screening nonlinear features of breast cancer. Three different types of second-order polynomial logistic regression with elastic network penalty (SOPLR-EN) in which each type contains 10 identical models were integrated to determine the most suitable sample set for feature screening by using bootstrap sampling strategy. A single feature and 39 nonlinear features were obtained by screening features that appeared at least 15 times in 30 integrations and were involved in the classification of at least 4 subtypes. The second-order polynomial logistic regression with ridge penalty (SOPLR-R) built on screened feature set achieved 82.30% classification accuracy for distinguishing breast cancer subtypes, surpassing the performance of other six methods. Further, 11 nonlinear miRNA biomarkers were identified, and their significant relevance to breast cancer was illustrated through six types of biological analysis.

Inflammasomes Are Influenced by Epigenetic and Autophagy Mechanisms in Colorectal Cancer Signaling

Inflammasomes contribute to colorectal cancer signaling by primarily inducing inflammation in the surrounding tumor microenvironment. Its role in inflammation is receiving increasing attention, as inflammation has a protumor effect in addition to inducing tissue damage. The inflammasome's function is complex and controlled by several layers of regulation. Epigenetic processes impact the functioning or manifestation of genes that are involved in the control of inflammasomes or the subsequent signaling cascades. Researchers have intensively studied the significance of epigenetic mechanisms in regulation, as they encompass several potential therapeutic targets. The regulatory interactions between the inflammasome and autophagy are intricate, exhibiting both advantageous and harmful consequences. The regulatory aspects between the two entities also encompass several therapeutic targets. The relationship between the activation of the inflammasome, autophagy, and epigenetic alterations in CRC is complex and involves several interrelated pathways. This article provides a brief summary of the newest studies on how epigenetics and autophagy control the inflammasome, with a special focus on their role in colorectal cancer. Based on the latest findings, we also provide an overview of the latest therapeutic ideas for this complex network.

Comprehensive pan-cancer analysis of mitochondrial outer membrane permeabilisation activity reveals positive immunomodulation and assists in identifying potential therapeutic targets for immunotherapy resistance

BACKGROUND

Mitochondrial outer membrane permeabilisation (MOMP) plays a pivotal role in cellular death and immune activation. A deeper understanding of the impact of tumour MOMP on immunity will aid in guiding more effective immunotherapeutic strategies.

METHODS

A comprehensive pan-cancer dataset comprising 30 cancer-type transcriptomic cohorts, 20 immunotherapy transcriptomic cohorts and three immunotherapy scRNA-seq datasets was collected and analysed to determine the influence of tumour MOMP activity on clinical prognosis, immune infiltration and immunotherapy effectiveness. Leveraging 65 scRNA-Seq datasets, the MOMP signature (MOMP.Sig) was developed to accurately reflect tumour MOMP activity. The clinical predictive value of MOMP.Sig was explored through machine learning models. Integration of the MOMP.Sig model and a pan-cancer immunotherapy CRISPR screen further investigated potential targets to overcome immunotherapy resistance, which subsequently underwent clinical validation.

RESULTS

Our research revealed that elevated MOMP activity reduces mortality risk in cancer patients, drives the formation of an anti-tumour immune environment and enhances the response to immunotherapy. This finding emphasises the potential clinical application value of MOMP activity in immunotherapy. MOMP.Sig, offering a more precise indicator of tumour cell MOMP activity, demonstrated outstanding predictive efficacy in machine-learning models. Moreover, with the assistance of the MOMP.Sig model, FOXO1 was identified as a core modulator that promotes immune resistance. Finally, these findings were successfully validated in clinical immunotherapy cohorts of skin cutaneous melanoma and triple-negative breast cancer patients.

CONCLUSIONS

This study enhances our understanding of MOMP activity in immune modulation, providing valuable insights for more effective immunotherapeutic strategies across diverse tumours.

Inhibition of HDAC2 sensitises antitumour therapy by promoting NLRP3/GSDMD-mediated pyroptosis in colorectal cancer

BACKGROUND

Although numerous studies have indicated that activated pyroptosis can enhance the efficacy of antitumour therapy in several tumours, the precise mechanism of pyroptosis in colorectal cancer (CRC) remains unclear.

METHODS

Pyroptosis in CRC cells treated with antitumour agents was assessed using various techniques, including Western blotting, lactate dehydrogenase release assay and microscopy analysis. To uncover the epigenetic mechanisms that regulate NLRP3, chromatin changes and NLRP3 promoter histone modifications were assessed using Assay for Transposase-Accessible Chromatin using sequencing and RNA sequencing. Chromatin immunoprecipitation‒quantitative polymerase chain reaction was used to investigate the NLRP3 transcriptional regulatory mechanism. Additionally, xenograft and patient-derived xenograft models were constructed to validate the effects of the drug combinations.

RESULTS

As the core molecule of the inflammasome, NLRP3 expression was silenced in CRC, thereby limiting gasdermin D (GSDMD)-mediated pyroptosis. Supplementation with NLRP3 can rescue pyroptosis induced by antitumour therapy. Overexpression of HDAC2 in CRC silences NLRP3 via epigenetic regulation. Mechanistically, HDAC2 suppressed chromatin accessibility by eliminating H3K27 acetylation. HDAC2 knockout promotes H3K27ac-mediated recruitment of the BRD4-p-P65 complex to enhance NLRP3 transcription. Inhibiting HDAC2 by Santacruzamate A in combination with classic antitumour agents (5-fluorouracil or regorafenib) in CRC xenograft-bearing animals markedly activated pyroptosis and achieved a significant therapeutic effect. Clinically, HDAC2 is inversely correlated with H3K27ac/p-P65/NLRP3 and is a prognostic factor for CRC patients.

CONCLUSION

Collectively, our data revealed a crucial role for HDAC2 in inhibiting NLRP3/GSDMD-mediated pyroptosis in CRC cells and highlighted HDAC2 as a potential therapeutic target for antitumour therapy.

HIGHLIGHTS

Silencing of NLRP3 limits the GSDMD-dependent pyroptosis in colorectal cancer. HDAC2-mediated histone deacetylation leads to epigenetic silencing of NLRP3. HDAC2 suppresses the NLRP3 transcription by inhibiting the formation of H3K27ac/BRD4/p-P65 complex. Targeting HDAC2 activates pyroptosis and enhances therapeutic effect.

Pan-Cancer Single-Cell and Spatial-Resolved Profiling Reveals the Immunosuppressive Role of APOE+ Macrophages in Immune Checkpoint Inhibitor Therapy

The heterogeneity of macrophages influences the response to immune checkpoint inhibitor (ICI) therapy. However, few studies explore the impact of APOE+ macrophages on ICI therapy using single-cell RNA sequencing (scRNA-seq) and machine learning methods. The scRNA-seq and bulk RNA-seq data are Integrated to construct an M.Sig model for predicting ICI response based on the distinct molecular signatures of macrophage and machine learning algorithms. Comprehensive single-cell analysis as well as in vivo and in vitro experiments are applied to explore the potential mechanisms of the APOE+ macrophage in affecting ICI response. The M.Sig model shows clear advantages in predicting the efficacy and prognosis of ICI therapy in pan-cancer patients. The proportion of APOE+ macrophages is higher in ICI non-responders of triple-negative breast cancer compared with responders, and the interaction and longer distance between APOE+ macrophages and CD8+ exhausted T (Tex) cells affecting ICI response is confirmed by multiplex immunohistochemistry. In a 4T1 tumor-bearing mice model, the APOE inhibitor combined with ICI treatment shows the best efficacy. The M.Sig model using real-world immunotherapy data accurately predicts the ICI response of pan-cancer, which may be associated with the interaction between APOE+ macrophages and CD8+ Tex cells.

Pan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and survival prognostication

BACKGROUND

Immunotherapy has emerged as a potent clinical approach for cancer treatment, but only subsets of cancer patients can benefit from it. Targeting lactate metabolism (LM) in tumor cells as a method to potentiate anti-tumor immune responses represents a promising therapeutic strategy.

METHODS

Public single-cell RNA-Seq (scRNA-seq) cohorts collected from patients who received immunotherapy were systematically gathered and scrutinized to delineate the association between LM and the immunotherapy response. A novel LM-related signature (LM.SIG) was formulated through an extensive examination of 40 pan-cancer scRNA-seq cohorts. Then, multiple machine learning (ML) algorithms were employed to validate the capacity of LM.SIG for immunotherapy response prediction and survival prognostication based on 8 immunotherapy transcriptomic cohorts and 30 The Cancer Genome Atlas (TCGA) pan-cancer datasets. Moreover, potential targets for immunotherapy were identified based on 17 CRISPR datasets and validated via in vivo and in vitro experiments.

RESULTS

The assessment of LM was confirmed to possess a substantial relationship with immunotherapy resistance in 2 immunotherapy scRNA-seq cohorts. Based on large-scale pan-cancer data, there exists a notably adverse correlation between LM.SIG and anti-tumor immunity as well as imbalance infiltration of immune cells, whereas a positive association was observed between LM.SIG and pro-tumorigenic signaling. Utilizing this signature, the ML model predicted immunotherapy response and prognosis with an AUC of 0.73/0.80 in validation sets and 0.70/0.87 in testing sets respectively. Notably, LM.SIG exhibited superior predictive performance across various cancers compared to published signatures. Subsequently, CRISPR screening identified LDHA as a pan-cancer biomarker for estimating immunotherapy response and survival probability which was further validated using immunohistochemistry (IHC) and spatial transcriptomics (ST) datasets. Furthermore, experiments demonstrated that LDHA deficiency in pancreatic cancer elevated the CD8+ T cell antitumor immunity and improved macrophage antitumoral polarization, which in turn enhanced the efficacy of immunotherapy.

CONCLUSIONS

We unveiled the tight correlation between LM and resistance to immunotherapy and further established the pan-cancer LM.SIG, holds the potential to emerge as a competitive instrument for the selection of patients suitable for immunotherapy.

Pan-cancer dissection of vasculogenic mimicry characteristic to provide potential therapeutic targets

Introduction

Vasculogenic mimicry (VM) represents a novel form of tumor angiogenesis that is associated with tumor invasiveness and drug resistance. However, the VM landscape across cancer types remains poorly understood. In this study, we elucidate the characterizations of VM across cancers based on multi-omics data and provide potential targeted therapeutic strategies.

Methods

Multi-omics data from The Cancer Genome Atlas was used to conduct comprehensive analyses of the characteristics of VM related genes (VRGs) across cancer types. Pan-cancer vasculogenic mimicry score was established to provide a depiction of the VM landscape across cancer types. The correlation between VM and cancer phenotypes was conducted to explore potential regulatory mechanisms of VM. We further systematically examined the relationship between VM and both tumor immunity and tumor microenvironment (TME). In addition, cell communication analysis based on single-cell transcriptome data was used to investigate the interactions between VM cells and TME. Finally, transcriptional and drug response data from the Genomics of Drug Sensitivity in Cancer database were utilized to identify potential therapeutic targets and drugs. The impact of VM on immunotherapy was also further clarified.

Results

Our study revealed that VRGs were dysregulated in tumor and regulated by multiple mechanisms. Then, VM level was found to be heterogeneous among different tumors and correlated with tumor invasiveness, metastatic potential, malignancy, and prognosis. VM was found to be strongly associated with epithelial-mesenchymal transition (EMT). Further analyses revealed cancer-associated fibroblasts can promote EMT and VM formation. Furthermore, the immune-suppressive state is associated with a microenvironment characterized by high levels of VM. VM score can be used as an indicator to predict the effect of immunotherapy. Finally, seven potential drugs targeting VM were identified.

Conclusion

In conclusion, we elucidate the characteristics and key regulatory mechanisms of VM across various cancer types, underscoring the pivotal role of CAFs in VM. VM was further found to be associated with the immunosuppressive TME. We also provide clues for the research of drugs targeting VM. Our study provides an initial overview and reference point for future research on VM, opening up new avenues for therapeutic intervention.

Exercise potentially prevents colorectal cancer liver metastases by suppressing tumor epithelial cell stemness via RPS4X downregulation

BACKGROUND

Colorectal cancer (CRC) is the third most prevalent tumor globally. The liver is the most common site for CRC metastasis, and the involvement of the liver is a common cause of death in patients with late-stage CRC. Consequently, mitigating CRC liver metastasis (CRLM) is key to improving CRC prognosis and increasing survival. Exercise has been shown to be an effective method of improving the prognosis of many tumor types. However, the ability of exercise to inhibit CRLM is yet to be thoroughly investigated.

METHODS

The GSE157600 and GSE97084 datasets were used for analysis. A pan-cancer dataset which was uniformly normalized was downloaded and analyzed from the UCSC database: TCGA, TARGET, GTEx (PANCAN, n = 19,131, G = 60,499). Several advanced bioinformatics analyses were conducted, including single-cell sequencing analysis, correlation algorithm, and prognostic screen. CRC tumor microarray (TMA) as well as cell/animal experiments are used to further validate the results of the analysis.

RESULTS

The greatest variability was found in epithelial cells from the tumor group. RPS4X was generally upregulated in all types of CRC, while exercise downregulated RPS4X expression. A lowered expression of RPS4X may prolong tumor survival and reduce CRC metastasis. RPS4X and tumor stemness marker-CD44 were highly positively correlated and knockdown of RPS4X expression reduced tumor stemness both in vitro and in vivo.

CONCLUSION

RPS4X upregulation may enhance CRC stemness and increase the odds of metastasis. Exercise may reduce CRC metastasis through the regulation of RPS4X.

PI3K/AKT signaling pathway associates with pyroptosis and inflammation in patients with endometriosis

Endometriosis (EMS) is known to be closely associated with inflammation. We evaluate the possible mechanism linking the PI3K/AKT signaling pathway with pyroptosis and inflammation in EMS. We collected 30 patients undergoing laparoscopic for endometriosis as the EMS group and those undergoing surgery for uterine fibroids as the control group, from whom we collected serum, normal endometrium, eutopic endometrium and ectopic endometrium. Transmission electron microscopy (TEM) was used to observe the internal structure of endometrial cells. Western Blot was used to detect the protein expression of PI3K, P-PI3K, AKT, P-AKT, NLRP3, Caspase-1, GSDMD, and GSDMD-N. Immunohistochemistry (IHC) staining was used to detect the expression of PI3K, AKT, NLRP3, Caspase-1, GSDMD, and GSDMD-N proteins. Immunofluorescence (IF) staining was used to observe the expression of GSDMD-N. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to analyze the mRNA levels of PI3K, AKT, NLRP3, Caspase-1, GSDMD, and GSDMD-N. ELISA was used to detect serum levels of IL-1β, IL-18, TLR4, and NF-κB. We found that activation of PI3K/AKT signaling pathway in endometriosis significantly increased the level of cellular pyroptosis and inflammatory factors. Our results suggest that there is a positive correlation between the PI3K/AKT signaling pathway and pyroptosisas well as inflammation in EMS patients.