Siglec15 shapes a non-inflamed tumor microenvironment and predicts the molecular subtype in bladder cancer

Senescence-specific molecular subtypes stratify the hallmarks of the tumor microenvironment and guide precision medicine in bladder cancer

BACKGROUND

Bladder cancer (BLCA) is notably associated with advanced age, characterized by its high incidence and mortality among the elderly. Despite promising advancements in models that amalgamate molecular subtypes with treatment and prognostic outcomes, the considerable heterogeneity in BLCA poses challenges to their universal applicability. Consequently, there is an urgent need to develop a new molecular subtyping system focusing on a critical clinical feature of BLCA: senescence.

METHODS

Utilizing unsupervised clustering on the Cancer Genome Atlas Program (TCGA)-BLCA cohort, we crafted a senescence-associated molecular classification and precision quantification system (Senescore). This method underwent systematic validation against established molecular subtypes, treatment strategies, clinical outcomes, the immune tumor microenvironment (TME), relevance to immune checkpoints, and identification of potential therapeutic targets.

RESULTS

External validations were conducted using the Xiangya cohort, IMvigor210 cohort, and meta-cohort, with multiplex immunofluorescence confirming the correlation between Senescore, immune infiltration, and cellular senescence. Notably, patients categorized within higher Senescore group were predisposed to the basal subtype, showcased augmented immune infiltration, harbored elevated driver gene mutations, and exhibited increased senescence-associated secretory phenotype (SASP) factors expression in the transcriptome. Despite poorer prognoses, these patients revealed greater responsiveness to immunotherapy and neoadjuvant chemotherapy.

CONCLUSIONS

Our molecular subtyping and Senescore, informed by age-related clinical features, accurately depict age-associated biological traits and its clinical application potential in BLCA. Moreover, this personalized assessment framework is poised to identify senolysis targets unique to BLCA, furthering the integration of aging research into therapeutic strategies.

Comprehensive single-cell pan-cancer atlas unveils IFI30+ macrophages as key modulators of intra-tumoral immune dynamics

Background

The convergence of macrophage-targeted strategies with immune checkpoint blockade therapies defines a pivotal avenue in contemporary tumor therapy. Identifying robust genetic regulators in this context is imperative.

Methods

This study elucidates IFI30's role in enhancing Major Histocompatibility Complex II (MHC-II) restriction antigen processing. Despite its recognition in cancer immunotherapy, IFI30 remains a nascent focus. Our approach involves a multi-omics analysis of IFI30 tumor immunological profile in the macrophage-mediated Tumor Microenvironment (TME), spanning various cancers and bolstered by rigorous co-culture laboratory work.

Results

IFI30 predominantly localizes in monocyte/macrophage populations, correlating strongly with immune cell infiltration. Substantiated by single-cell analysis, IFI30 exhibits significant functional enrichment in immune-related pathways. Co-expression with immune-related genes, including MHC elements and immune checkpoints, further validates its relevance.

Conclusion

Our study positions IFI30 as a promising immunotherapeutic target. Pan-cancer analyses and glioblastoma multiforme (GBM) investigations collectively underscore IFI30's potential as a TME modulator, particularly in its interaction with M2-macrophages. IFI30 emerges as a prospective intervention point in the immunotherapeutic landscape.

Immune-related glycosylation genes based classification predicts prognosis and therapy options of osteosarcoma

Osteosarcoma is the most common primary bone malignancy, with a very poor prognosis. Aberrant glycosylation is close involvement in osteosarcoma. Accordingly, this study aimed at investigating the role of glycosylation genes in the prognosis and therapy options of osteosarcoma. The microenvironment of osteosarcoma was assessed using estimate algorithm. A total of 20 immune-related glycosylation genes (IRGGs) was identified using Pearson correlation analysis. Accordingly, osteosarcoma patients were divided into C1 and C2 type using consensus clustering. Multiple algorithms (Xcell, MCP-counter, ssGSEA, epic, quantiseq), cancer immune cycle analysis, and GSVA were applied to estimate the immune, molecule and metabolism characteristics of osteosarcoma, indicating that C1 type was featured with high immune infiltration, high glycosylation, enriched MEK signaling, and good prognosis, while C2 type was characterized by more metastasis, enriched immunotherapy-positive gene signatures, high tumor mutation burden, and poor prognosis. Results from TIDE algorithm and immunotherapy datasets suggested the C2 type's preference of immune checkpoint inhibitors (ICIs), while data of GDSC, CMap analysis and cell experiments indicated that C1 type was sensitivity to MEK inhibitor PD0325901. In addition, univariate Cox and Lasso analysis was combined to establish an IRGGs' risk score containing 6 genes (B3GNT8, FUT7, GAL3ST4, GALNT14, HS3ST2, and MFNG). The data of DCA and ROC indicated its well prediction of prognosis in osteosarcoma. Finally, cellular location analysis showed that the 6 genes not only distributed in tumor cells but also in immune cells. In summary, the classification and risk score based on IRGGs effectively predicted the prognosis and therapy options of osteosarcoma. Further studies on IRGGs may contribute to the understanding of cancer immunity in osteosarcoma.

Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway

Background

Recent years have seen persistently poor prognoses for glioma patients. Therefore, exploring the molecular subtyping of gliomas, identifying novel prognostic biomarkers, and understanding the characteristics of their immune microenvironments are crucial for improving treatment strategies and patient outcomes.

Methods

We integrated glioma datasets from multiple sources, employing Non-negative Matrix Factorization (NMF) to cluster samples and filter for differentially expressed metabolic genes. Additionally, we utilized Weighted Gene Co-expression Network Analysis (WGCNA) to identify key genes. A predictive model was developed utilizing the optimal consistency index derived from a combination of 101 machine learning techniques, and its effectiveness was confirmed through multiple datasets employing different methodologies. In-depth analyses were conducted on immune cell infiltration and tumor microenvironmental aspects. Single-cell sequencing data were employed for clustering and differential expression analysis of genes associated with glioma. Finally, the immune relevance of the model gene ALPK1 in the context of pan-cancer was explored, including its relationship with immune checkpoints.

Results

The application of NMF, coupled with differential analysis of metabolic-related genes, led to the identification of two clusters exhibiting significant differences in survival, age, and metabolic gene expression among patients. Core genes were identified through WGCNA, and a total of 101 machine learning models were constructed, with LASSO+GBM selected as the optimal model, demonstrating robust validation performance. Comprehensive analyses revealed that high-risk groups exhibited greater expression of specific genes, with ALPK1 showing significant correlations with immune regulation.

Conclusion

This research employed a multi-dataset strategy and various methods to clarify the differences in metabolic traits and immune conditions in glioma patients, while creating an innovative prognostic risk evaluation framework. These results offer fresh perspectives on the intricate biological processes that define gliomas.

Integrative Analysis of the Role of MRPL21 in Human Pan-Cancer and Its Relationship with the Progression of Lung Adenocarcinoma

Mitochondrial ribosomal protein L21 (MRPL21) is essential for normal cell function and may play a significant role in cancer development. In this study, we performed a comprehensive pan-cancer analysis to explore MRPL21's function across different cancers, utilizing multiple online data platforms such as TCGA. Our analysis covered its clinical significance and biological functions, including expression levels, survival and diagnostic analysis, gene mutations, multidimensional immune-correlation analysis, tumor heterogeneity, and cancer-associated signaling pathways. Additionally, we constructed a prognostic nomogram for lung adenocarcinoma (LUAD) patients based on MRPL21 and validated its biological function through in vitro experiments. Our findings revealed that MRPL21 is commonly overexpressed in various cancers and is associated with poor prognosis. It significantly impacts cancer-related pathways, particularly those related to cell cycle activation. Moreover, MRPL21 is critical in the tumor microenvironment and is closely linked to immune infiltration across several cancer types. Its expression correlates with essential factors such as tumor mutational burden, microsatellite instability, immune checkpoint, and methylation patterns. In LUAD, MRPL21 was identified as an independent risk factor and demonstrated that MRPL21 promotes LUAD progression. Overall, MRPL21 holds potential as both a diagnostic and prognostic marker in cancer and could serve as a promising therapeutic target, particularly for LUAD.

TBX3 shapes an immunosuppressive microenvironment and induces immunotherapy resistance

Background: Identifying biomarkers that predict immunotherapy efficacy and discovering new targets for combination therapies are critical elements for improving the prognosis of bladder cancer (BLCA) patients. Methods: Firstly, we explored the expression patterns of TBX3 in normal and pan-cancer tissues and the correlation between TBX3 and the immune microenvironment using data from multiple public databases. Then, we combined various techniques, including bulk RNA sequencing, single-cell RNA sequencing, high-throughput cytokine arrays, functional experiments, ProcartaPlex multiplex immunoassays and TissueFAXS panoramic tissue quantification assays, to demonstrate that TBX3 shapes an immunosuppressive tumor microenvironment (TME) in BLCA. Results: We identified TBX3 as a key factor associated with the immunosuppressive microenvironment in BLCA through a systematic multi-omics analysis. We found that TBX3 is primarily expressed in malignant cells, where TBX3high tumor cells increase the secretion of TGFβ1, which promotes the infiltration of cancer-associated fibroblasts (CAFs), thereby forming an immunosuppressive microenvironment. We further demonstrated that TBX3 enhances TGFβ1 expression by binding to the TGFβ1 promoter, and blocking TGFβ1 counteracts the immunosuppressive effects of TBX3. Moreover, TBX3 reduced the cancer-killing efficiency of CD8+ T cells by decreasing the proportion of GZMB+ CD8+ T cells, and knocking down TBX3 combined with anti-PD-1 treatment increased CD8+ T cell infiltration and reduced CAFs in vivo. We also validated the inverse relationship between TBX3+ malignant cells and CD8+ T cells and the positive relationship with CAFs in tissue microarrays. Lastly, we found that TBX3 predicted immunotherapy efficacy in our real-world immunotherapy cohort and multiple public cohorts. Conclusion: In summary, TBX3 promotes BLCA progression and immunotherapy resistance by inducing an immunosuppressive microenvironment, and targeting TBX3 could enhance the efficacy of immunotherapy for BLCA.

Network Medicine-Based Strategy Identifies Maprotiline as a Repurposable Drug by Inhibiting PD-L1 Expression via Targeting SPOP in Cancer

Immune checkpoint inhibitors (ICIs) are drugs that inhibit immune checkpoint (ICP) molecules to restore the antitumor activity of immune cells and eliminate tumor cells. Due to the limitations and certain side effects of current ICIs, such as programmed death protein-1, programmed cell death-ligand 1, and cytotoxic T lymphocyte-associated antigen 4 (CTLA4) antibodies, there is an urgent need to find new drugs with ICP inhibitory effects. In this study, a network-based computational framework called multi-network algorithm-driven drug repositioning targeting ICP (Mnet-DRI) is developed to accurately repurpose novel ICIs from ≈3000 Food and Drug Administration-approved or investigational drugs. By applying Mnet-DRI to PD-L1, maprotiline (MAP), an antidepressant drug is repurposed, as a potential PD-L1 modifier for colorectal and lung cancers. Experimental validation revealed that MAP reduced PD-L1 expression by targeting E3 ubiquitin ligase speckle-type zinc finger structural protein (SPOP), and the combination of MAP and anti-CTLA4 in vivo significantly enhanced the antitumor effect, providing a new alternative for the clinical treatment of colorectal and lung cancer.

Harnessing machine learning and multi-omics to explore tumor evolutionary characteristics and the role of AMOTL1 in prostate cancer

Although recent advancements have shed light on the crucial role of coordinated evolution among cell subpopulations in influencing disease progression, the full potential of these insights has not yet been fully harnessed in the clinical application of personalized precision medicine for prostate cancer (PCa). In this study, we utilized single-cell sequencing to identify the evolutionary characteristics of tumoral cell states and employed comprehensive bulk RNA sequencing to evaluate their potential as prognostic indicators and therapeutic targets. Leveraging advancements in artificial intelligence, we integrated machine learning with multi-omics to develop and validate the tumor evolutionary characteristic predictive indicator (TECPI). TECPI not only demonstrated superior prognostic performance compared to traditional clinical predictors and 81 previously published models but also improved patient outcomes by accurately identifying individuals who would benefit from immunotherapy and targeted therapies. Furthermore, we experimentally validated the critical role of AMOTL1 in PCa pharmacodynamics through its interaction with AR, pivotal for modulating the sensitivity to AR antagonist. Additionally, we demonstrated the generalizability and applicability of TECPI across pan-cancers. In summary, this study emphasizes the importance of understanding cellular diversity and dynamics within the tumor microenvironment to predict PCa progression and to guide targeted therapy effectively.

A comprehensive review of immune checkpoint inhibitors for cancer treatment

Immunology-based therapies are emerging as an effective cancer treatment, using the body's immune system to target tumors. Immune checkpoints, which regulate immune responses to prevent tissue damage and autoimmunity, are often exploited by cancer cells to avoid destruction. The discovery of checkpoint proteins like PD-1/PD-L1 and CTLA-4 was pivotal in developing cancer immunotherapy. Immune checkpoint inhibitors (ICIs) have shown great success, with FDA-approved drugs like PD-1 inhibitors (Nivolumab, Pembrolizumab, Cemiplimab), PD-L1 inhibitors (Atezolizumab, Durvalumab, Avelumab), and CTLA-4 inhibitors (Ipilimumab, Tremelimumab), alongside LAG-3 inhibitor Relatlimab. Research continues on new checkpoints like TIM-3, VISTA, B7-H3, BTLA, and TIGIT. Biomarkers like PDL-1 expression, tumor mutation burden, interferon-γ presence, microbiome composition, and extracellular matrix characteristics play a crucial role in predicting responses to immunotherapy with checkpoint inhibitors. Despite their effectiveness, not all patients experience the same level of benefit, and organ-specific immune-related adverse events (irAEs) such as rash or itching, colitis, diarrhea, hyperthyroidism, and hypothyroidism may occur. Given the rapid advancements in this field and the variability in patient outcomes, there is an urgent need for a comprehensive review that consolidates the latest findings on immune checkpoint inhibitors, covering their clinical status, biomarkers, resistance mechanisms, strategies to overcome resistance, and associated adverse effects. This review aims to fill this gap by providing an analysis of the current clinical status of ICIs, emerging biomarkers, mechanisms of resistance, strategies to enhance therapeutic efficacy, and assessment of adverse effects. This review is crucial to furthering our understanding of ICIs and optimizing their application in cancer therapy.

m6A methylation profiling as a prognostic marker in nasopharyngeal carcinoma: insights from MeRIP-Seq and RNA-Seq

Background

Nasopharyngeal carcinoma (NPC) is a type of malignant tumors commonly found in Southeast Asia and China, with insidious onset and clinical symptoms. N6-methyladenosine (m6A) modification significantly contributes to tumorigenesis and progression by altering RNA secondary structure and influencing RNA-protein binding at the transcriptome level. However, the mechanism and role of abnormal m6A modification in nasopharyngeal carcinoma remain unclear.

Methods

Nasopharyngeal Carcinoma tissues from 3 patients and non-cancerous nasopharyngeal tissues from 3 individuals, all from Fujian Cancer Hospital, were sequenced for m6A methylation. These were combined with transcriptome sequencing data from 192 nasopharyngeal cancer tissues. Genes linked to prognosis were discovered using differential analysis and univariate Cox regression. Subsequently, a prognostic model associated with m6A was developed through the application of LASSO regression analysis. The model's accuracy was verified using both internal transcriptome databases and external databases. An extensive evaluation of the tumor's immune microenvironment and signaling pathways was performed, analyzing both transcriptomic and single-cell data.

Results

The m6A methylation sequencing analysis revealed 194 genes with varying expression levels, many of which are predominantly associated with immune pathways. By integrating transcriptome sequencing data, 19 m6A-modified genes were found to be upregulated in tumor tissues, leading to the development of a three-gene (EME1, WNT4, SHISA2) risk prognosis model. The group with lower risk exhibited notable enrichment in pathways related to immunity, displaying traits like enhanced survival rates, stronger immune profiles, and increased responsiveness to immunotherapy when compared to the higher-risk group. Single-cell analysis revealed that malignant cells exhibited the highest risk score levels compared to immune cells, with a high-risk score indicating worse biological behavior. The three hub genes demonstrated significant correlation with m6A modification regulators, and MeRIP-RT-PCR confirmed the occurrence of m6A methylation in these genes within nasopharyngeal carcinoma cells.

Conclusions

A prognostic model for nasopharyngeal carcinoma risk based on m6A modification genes was developed, and its prognostic value was confirmed through self-assessment data. The study highlighted the crucial impact of m6A modification on the immune landscape of nasopharyngeal cancer.

Migrasome regulator TSPAN4 shapes the suppressive tumor immune microenvironment in pan-cancer

Background

Migrasomes are newly identified organelles on the retracting fibers of migrating cells, involved in releasing signaling molecules, expelling damaged mitochondria, and facilitating intercellular communication through phagocytosis. TSPAN4, a key regulator of migrasome formation, is a valuable marker for visualizing these organelles. However, its role in cancer remains unclear.

Methods

We analyzed TSPAN4 expression and its prognostic significance across multiple cancers using TCGA Pan-Cancer (PANCAN), and TCGA TARGET GTEx datasets. The relationship between TSPAN4 and tumor heterogeneity, stemness, and the immunosuppressive tumor microenvironment was explored through RNA-seq and scRNA-seq data. In addition, we examined TSPAN4's role in glioma, focusing on migrasome formation, cell proliferation, and macrophage polarization.

Results

Our analysis reveals that TSPAN4 is aberrantly expressed in various tumors, likely linked to its methylation status. It correlates with tumor heterogeneity, stemness, and a suppressive immune microenvironment. In glioma, TSPAN4 enhances cell proliferation and promotes macrophage polarization toward the immunosuppressive M2 phenotype.

Conclusions

TSPAN4, as a migrasome regulator, plays a crucial role in shaping the immunosuppressive tumor microenvironment in pan-cancer.

Siglec15 in blood system diseases: from bench to bedside

Inhibiting the PD-1/PD-L1 pathway using immunomodulators has demonstrated promising outcomes in clinics. Immunomodulators can effectively target immune checkpoints with a strong preference for the tumor microenvironment (TME). Besides, immunomodulators specifically target the recently discovered inhibitory immune checkpoint, sialic acid-binding immunoglobulin-like lectin (Siglec-15). Distinctive in its molecular composition, Siglec-15 has a unique molecular composition and been shown to be highly prevalent in numerous solid tumor tissues and tumor-associated macrophages (TAMs) in human subjects. Notably, Siglec-15 is up-regulated across various cancer types. As a result, Siglec-15 has attracted significant attention due to its exclusive nature concerning PD-L1 expression, suggesting its role in immune evasion in patients lacking PD-L1. Siglec-15 predominantly appears in certain populations and can promote tumor development by repressing T lymphocyte activation and proliferation, thereby facilitating tumor cell immune escape. Furthermore, Siglec-15 is implicated in osteoclast differentiation and bone remodeling, indicating that it is a promising target for next-generation cancer immunotherapies. Additionally, Siglec-15 can modulate immune responses to microbial infections. The current treatment strategies for hematological conditions predominantly include conventional intensive chemotherapy and transplantation methods. However, emerging immunotherapeutic approaches are increasingly recognized for their overall effectiveness, indicating that specific molecular targets should be identified. The expression of Siglec-15 within tumor cells may indicate a novel pathway for treating hematological malignancies. In this study, the biological attributes, expression patterns, and pathogenic mechanisms of Siglec-15 across various diseases were reviewed. The role of Siglec-15 in the pathogenesis and laboratory diagnosis of hematological disorders was also evaluated.

CMTM7 shapes the chronic inflammatory and immunosuppressive tumor microenvironment in hepatocellular carcinoma as an M2 macrophage biomarker

Transmembrane domain-containing 7 (CMTM7) is a protein located at the plasma membrane. It plays a role in regulating the development and immune microenvironment of tumor cells. However, the impact of CMTM7 on hepatocellular carcinoma (HCC) is not well understood. To better understand the role of CMTM7 in HCC, the correlations of CMTM7 with clinical characteristics, patient prognosis, chronic inflammation, and immune cell infiltration were analyzed using tissue microarray slides, sequencing datasets and various analysis tools (Web). The bulk sequencing analysis indicated that elevated expression of CMTM7 appears to promote chronic inflammation, immunosuppression, M2 macrophage infiltration, a diminished response to cancer immunotherapy, and an unfavorable clinical prognosis in patients with hepatocellular carcinoma (HCC). Further investigation through single-cell RNA sequencing and multiple fluorescence staining demonstrated that CMTM7 serves as a molecular marker for M2 macrophages and is associated with T cell exhaustion as well as highly plastic stem-like characteristics. We propose that CMTM7 may represent a novel immune checkpoint for HCC patients experiencing suboptimal therapeutic outcomes. Utilizing the Connectivity Map and AutoDock Vina, we predicted two potential compounds targeting CMTM7-fasudil and arachidonyltrifluoromethane-as promising therapeutic candidates. Collectively, these findings suggest that CMTM7-positive macrophages play significant roles in establishing an immunosuppressive tumor microenvironment while promoting highly plastic and stem-like traits in HCC cells, ultimately contributing to poor prognostic outcomes.

Comprehensive Analysis Reveals That ISCA1 Is Correlated with Ferroptosis-Related Genes Across Cancers and Is a Biomarker in Thyroid Carcinoma

BACKGROUND

ISCA1 (Iron-Sulfur Cluster Assembly 1) is involved in the assembly of iron-sulfur (Fe-S) clusters, which are vital for electron transport and enzyme activity. Some studies suggest the potential involvement of ISCA1 in tumor progression through interactions with ferroptosis-related genes (FRGs) and the tumor immune microenvironment (TME). However, there has been no systematic analysis of its role in FRGs and the TME or its predictive value for prognosis and immunotherapy response across different cancer types.

METHODS

In this study, we analyzed the expression and prognosis of ISCA1 RNA, CNV, methylation, and protein in multiple tumor tissues via data from the TCGA and CPTAC databases and clinical information. We conducted a comprehensive analysis of the correlations between ISCA1 and FRGs, immune-related genes (including immune regulatory genes and immune checkpoint genes), immune cell infiltration, immune infiltration scores, tumor stemness, and genomic heterogeneity.

RESULTS

We performed drug prediction and validation through molecular docking and molecular dynamics analysis to identify candidate drugs that could promote or inhibit ISCA1 RNA expression. Our findings revealed that ISCA1 could serve as a biomarker in thyroid carcinoma, play a role with different FRGs in various cell types, and mediate different ligand-receptor pathways for cell-cell communication.

CONCLUSIONS

Overall, our study highlights the potential of ISCA1 as a novel biomarker for predicting prognosis and immunotherapeutic efficacy in thyroid carcinoma and suggests its potential for developing novel antitumor drugs or improving immunotherapy.

Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy

Background

Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.

Methods

Data on clinical and pathological characteristics, genetic alterations, DNA methylation patterns, and the expression of mRNA, lncRNA, and microRNA, along with somatic mutations in LUAD patients, were gathered from the TCGA and GEO datasets. A computational workflow was utilized to merge multi-omics data from LUAD patients through 10 clustering techniques, which were paired with 10 machine learning methods to pinpoint detailed molecular subgroups and refine a prognostic risk model. The disparities in somatic mutations, copy number alterations, and immune cell infiltration between high- and low-risk groups were assessed. The effectiveness of immunotherapy in patients was evaluated through the TIDE and SubMap algorithms, supplemented by data from various immunotherapy groups. Furthermore, the Cancer Therapeutics Response Portal (CTRP) and the PRISM Repurposing dataset (PRISM) were employed to investigate new drug treatment approaches for LUAD. In the end, the role of SLC2A1 in tumor dynamics was examined using RT-PCR, immunohistochemistry, CCK-8, wound healing, and transwell tests.

Results

By employing multi-omics clustering, we discovered two unique cancer subtypes (CSs) linked to prognosis, with CS2 demonstrating a better outcome. A strong model made up of 17 genes was created using a random survival forest (RSF) method, which turned out to be an independent predictor of overall survival and showed reliable and impressive performance. The low-risk group not only had a better prognosis but also was more likely to display the "cold tumor" phenotype. On the other hand, individuals in the high-risk group showed a worse outlook and were more likely to respond positively to immunotherapy and six particular chemotherapy medications. Laboratory cell tests demonstrated that SLC2A1 is abundantly present in LUAD tissues and cells, greatly enhancing the proliferation and movement of LUAD cells.

Conclusions

Thorough examination of multi-omics data offers vital understanding and improves the molecular categorization of LUAD. Utilizing a powerful machine learning system, we highlight the immense potential of the riskscore in providing individualized risk evaluations and customized treatment suggestions for LUAD patients.

Evasion of immunosurveillance by the upregulation of Siglec15 in bladder cancer

Immunotherapy resistance in bladder cancer (BLCA) is associated with elevated levels of sialic acid-binding immunoglobulin-like lectin (Siglec15). This protein plays a crucial role in fostering a noninflammatory tumor microenvironment (TME), which is conducive to cancer progression. Our study confirmed that the overexpression of Siglec15 led to a reduction in CD8+ T cell infiltration. This effect was mediated by the downregulation of pro-inflammatory cytokines and chemokines, which in turn exacerbated BLCA malignancy. Furthermore, Siglec15 inhibited the cytotoxicity of effector T cell, contributing to immune evasion. An in vivo study demonstrated that Siglec15 overexpression induced a non-inflammatory TME and promoted resistance to immunotherapy. These findings highlight Siglec15 as a potential therapeutic target for BLCA. By modulating inflammation in the TME and CD8+ T cell function, targeting Siglec15 may offer a novel strategy for overcoming immunotherapy resistance and improving patient outcomes.

An immune scoring system predicts prognosis and immune characteristics in lung adenocarcinoma brain metastases by RNA sequencing

BACKGROUND

Previous studies have reported that the tumor immune microenvironment (TIME) was associated with the prognosis of lung cancer patients and the efficacy of immunotherapy. However, given the significant challenges in obtaining specimens of brain metastases (BrMs), few studies explored the correlation between the TIME and the prognosis in patients with BrMs from lung adenocarcinoma (LUAD).

METHODS

Transcript profiling of archival formalin-fixed and paraffin-embedded specimens of BrMs from 70 LUAD patients with surgically resected BrMs was carried out using RNA sequencing. An immune scoring system, the green-yellow module score (GYMS), was developed to predict prognosis and immune characteristics in both BrMs and primary LUAD using Weighted Correlation Network analysis (WGCNA) and GSVA analysis. We comprehensively evaluated the immunological role of GYMS based on gene expression profile of LUAD BrMs by systematically correlating GYMS with immunological characteristics and immunotherapy responsiveness in the BrMs. Immunohistochemistry was applied for validation.

RESULTS

We found that the high-GYMS group had better clinical prognosis and inflamed immune landscape including high infiltrations of various immune cells, increased immunomodulatory expression, and enriched immune-related pathways by using RNA-seq and immunohistochemical analysis. Low-GYMS group presented a lacked immune infiltration characteristic. Besides, the high-GYMS group had lower TIDE score and higher T-cell inflamed score than low-GYMS group. The GYMS has been validated in independent BrMs cohorts and primary NSCLC cohort treated with anti-PD-1/PD-L1, showing strong reproducibility and stability in both primary LUAD and BrMs. In addition, we construct a GYMS-related risk signature for patients with LUAD BrMs to predict prognosis.

CONCLUSIONS

We identified two immune-related subtypes which used to estimate prognosis and immune characteristics and developed a reliable GYMS-related risk signature in LUAD BrMs. These results will enhance the understanding of the immune microenvironment in LUAD BrMs and lay the theoretical foundation for the development of personalized therapies for LUAD patients with BrMs.

Comprehensive analysis of the relationship between RNA modification writers and immune microenvironment in head and neck squamous cell carcinoma

OBJECTIVES

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. Four types of RNA modification writers (m6A, m1A, A-I editing, and APA) are widely involved in tumorigenesis and the TME. We aimed to comprehensively explore the role of the four RNA modification writers in the progression and immune microenvironment of HNSCC.

MATERIALS AND METHODS

We first obtained transcription profile data and transcriptional variation of the four types of RNA modification writers from The Cancer Genome Atlas (TCGA) database. HNSCC patients in TCGA dataset were divided into different clusters based on the four types of RNA modification writers. Univariate Cox and Least absolute shrinkage and selection operator (LASSO) analyses were performed to conduct a Writer-score scoring system, which was successfully verified in the GSE65858 dataset and our clinical sample dataset. Finally, we evaluated the relationship between different RNA modification clusters (Writer-score) and immunological characteristics of HNSCC.

RESULTS

Two different RNA modification clusters (A and B) were obtained. These RNA modification clusters (Writer-score) were strongly associated with immunological characteristics (immunomodulators, cancer immunity cycles, infiltrating immune cells (TIICs), inhibitory immune checkpoints, and T cell inflamed score (TIS)) of HNSCC.

CONCLUSIONS

This study identified two different RNA modification clusters and explored the potential relationship between RNA modification clusters (Writer-score) and immunological characteristics, offering a new theoretical basis for precision immunotherapy in patients with HNSCC.

MFAP3L predicts tumor microenvironment status, molecular subtypes and clinical benefit in patients with bladder cancer

Bladder cancer (BLCA), ranking as the tenth most prevalent malignancy globally, imposes a substantial public health and socio-economic challenge. Despite ongoing efforts by urologists to identify novel molecular subtypes and treatment paradigms, the intrinsic heterogeneity of BLCA continues to obstruct the efficacy of current diagnostic and therapeutic evaluations, leaving a gap in the comprehensive management of BLCA. This necessitates an in-depth investigation into the BLCA tumor microenvironment (TME) to identify pivotal molecules like MFAP3L. Our research concentrated on MFAP3L, commencing with a pan-cancer analysis of its immune profile. We discovered that MFAP3L exhibits a significant negative correlation with numerous immune components and markers in BLCA, a trend not observed in other cancer types. Within the TCGA-BLCA cohort, patients were classified into High-MFAP3L and Low-MFAP3L groups according to their MFAP3L transcript levels. Our exploration into the BLCA TME delved into immune infiltration, molecular subtype patterns, and treatment preferences within these MFAP3L groups. High MFAP3L expression was linked to favorable prognoses, luminal subtypes, and low immune infiltration, inversely associated with various immune molecules and characteristics. Additionally, high MFAP3L expressors exhibited diminished immune checkpoint levels, suggesting enhanced immunotherapy tolerance and sensitivity to oncogenic pathway targeting. Conversely, low MFAP3L expression correlated with poor outcomes, basal subtypes, increased immune infiltration, and heightened gene mutation rates, alongside sensitivity to radiotherapy, EGFR-targeted treatments, and immunotherapy. Hence, MFAP3L emerges as a critical yet underexplored gene in BLCA, offering insights into immune status within the TME and aiding in molecular subtyping and therapeutic decision-making.

Siglec-15 expression in diffuse gliomas and its correlation with MRI morphologic features and apparent diffusion coefficient

BACKGROUND

Sialic acid-binding immunoglobulin-like lectin 15 (Siglec-15) enhances tumor immune escape and leads to tumor growth.

PURPOSE

To investigate the expression of Siglec-15 in diffuse gliomas and its correlation with tumor magnetic resonance imaging (MRI) features.

MATERIAL AND METHODS

This study included 57 patients with gliomas. Morphological MRI features, including the largest tumor diameter, enhancement category, location, calcification, cysts, and hemorrhage, were visually rated. Apparent diffusion coefficient (ADC) values were calculated in tumor region. MRI morphologic features and ADC were compared between patients with positive and negative Siglec-15 expression. Receiver operating characteristic (ROC) curves were further constructed to assess the diagnostic performance.

RESULTS

Siglec-15 was expressed in immunocytes, such as macrophages in the peritumoral area. Siglec-15 expression was positive in 20/57 (35.09%) patients, with higher expression in patients with IDH-mutant gliomas and lower grade gliomas. The tumor diameter was significantly smaller in patients with positive Siglec-15 expression than in those with negative expression for all patients (P = 0.017) and for patients with IDH-mutant gliomas (P = 0.020). Moreover, ADC values of the tumor were significantly higher in patients with positive Siglec-15 expression than in those with negative expression for all patients (P = 0.027). The areas under the ROC curve (AUCs) of the diameter and ADC were 0.702 and 0.686, respectively. A combination of these two parameters generated an improved AUC of 0.762.

CONCLUSION

Siglec-15 was expressed in immunocytes such as macrophages in the peritumoral area, with a positive rate of 35.09%. Positive Siglec-15 expression in diffuse gliomas was correlated with smaller tumor size and higher ADC values.

Leveraging mitochondrial-programmed cell death dynamics to enhance prognostic accuracy and immunotherapy efficacy in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a highly heterogeneous disease, posing significant challenges to accurate prognosis prediction. Mitochondria play a central role in the energy metabolism of eukaryotic cells and can influence programmed cell death (PCD) mechanisms, which are critical in tumorigenesis and cancer progression. However, the prognostic significance of the interplay between mitochondrial function and PCD in LUAD requires further investigation.

METHODS

We analyzed data from 1231 LUAD patients across seven global cohorts to develop a mitochondrial-related PCD signature (MPCDS) using machine learning. Validation was done using six immunotherapy cohorts (LUAD, melanoma, clear cell renal cell carcinoma; n=935) and a pan-cancer cohort of 21 tumor types. An in-house LUAD tissue cohort (n=100) confirmed the prognostic significance of nucleoside diphosphate kinase 4 (NME4). In vivo and in vitro experiments explored NME4's role in immune exclusion.

RESULTS

The MPCDS demonstrated strong predictive performance for prognosis in LUAD patients, surpassing 114 previously published LUAD signatures. Additionally, MPCDS effectively predicted outcomes in immunotherapy patients (including those with LUAD, melanoma, and clear cell renal cell carcinoma). Biologically, MPCDS was significantly associated with immune features, with the high MPCDS group exhibiting reduced immune activity and a tendency towards cold tumors. NME4, a key gene within the MPCDS (correlation=0.55, p<0.05), was associated with poorer prognosis in LUAD patients with high expression, particularly in CD8 desert phenotypes, as validated by our in-house cohort. Multiplex immunofluorescence confirmed the spatial colocalization and exclusion relationship between NME4 and immune cells such as CD3+ T cells and CD20+ B cells. Further experiments revealed that NME4 regulated the proliferation and invasion of LUAD cells both in vitro and in vivo. Importantly, inhibiting NME4 increased the abundance and activity of CD8+ T cells and enhanced the antitumor immunity of anti-programmed cell death protein-1 therapy in vivo.

CONCLUSION

The MPCDS provides personalized risk assessment and immunotherapy interventions for individual LUAD patients. NME4, a key gene within the MPCDS, has been identified as a novel oncogene associated with immune exclusion and may serve as a new target for LUAD intervention and immunotherapy.

CALB2 drives pancreatic cancer metastasis through inflammatory reprogramming of the tumor microenvironment

BACKGROUND

Early dissemination to distant organs accounts for the dismal prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). Chronic, dysregulated, persistent and unresolved inflammation provides a preferred tumor microenvironment (TME) for tumorigenesis, development, and metastasis. A better understanding of the key regulators that maintain inflammatory TME and the development of predictive biomarkers to identify patients who are most likely to benefit from specific inflammatory-targeted therapies is crucial for advancing personalized cancer treatment.

METHODS

This study identified cell-specific expression of CALB2 in human PDAC through single-cell RNA sequencing analysis and assessed its clinicopathological correlations in tissue microarray using multi-color immunofluorescence. Co-culture systems containing cancer-associated fibroblasts (CAFs) and patient-derived organoids (PDOs) in vitro and in vivo were employed to elucidate the effects of CALB2-activated CAFs on PDAC malignancy. Furthermore, CUT&RUN assays, luciferase reporter assays, RNA sequencing, and gain- or loss-of-function assays were used to unravel the molecular mechanisms of CALB2-mediated inflammatory reprogramming and metastasis. Additionally, immunocompetent KPC organoid allograft models were constructed to evaluate CALB2-induced immunosuppression and PDAC metastasis, as well as the efficacy of inflammation-targeted therapy.

RESULTS

CALB2 was highly expressed both in CAFs and cancer cells and correlated with an unfavorable prognosis and immunosuppressive TME in PDAC patients. CALB2 collaborated with hypoxia to activate an inflammatory fibroblast phenotype, which promoted PDAC cell migration and PDO growth in vitro and in vivo. In turn, CALB2-activated CAFs upregulated CALB2 expression in cancer cells through IL6-STAT3 signaling-mediated direct transcription. In cancer cells, CALB2 further activated Ca2+-CXCL14 inflammatory axis to facilitate PDAC metastatic outgrowth and immunosuppression. Genetic or pharmaceutical inhibition of CXCL14 significantly suppressed CALB2-mediated metastatic colonization of PDAC cells in vivo and extended mouse survival.

CONCLUSIONS

These findings identify CALB2 as a key regulator of inflammatory reprogramming to promote PDAC metastatic progression. Combination therapy with αCXCL14 monoclonal antibody and gemcitabine emerges as a promising strategy to suppress distant metastasis and improve survival outcomes in PDAC with CALB2 overexpression.

Construction and verification of an innovative immune-related and hallmark gene sets prognostic model for bladder cancer

Background

Bladder cancer (BC) is a life-threatening malignancy with high mortality rates. Current prognostic models are insufficient in accurately predicting clinical outcomes, impeding personalized treatment strategies. This study aimed to identify BC subtypes and prognostic gene sets by analyzing changes in immune and hallmark gene sets activity in tumor and adjacent non-tumor tissues to enhance patient outcomes.

Methods

Utilizing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), gene set variation analysis (GSVA) was applied to C7 immune-related and hallmark gene sets from the Molecular Signatures Database (MSigDB). The CancerSubtype R package was utilized for clustering these gene sets into three categories, from which 109 candidate sets were identified using Venn diagrams. A refined subset of seven gene sets was selected through least absolute shrinkage and selection operator (LASSO) regression for the construction of a risk model. Model validity was confirmed with receiver operating characteristic (ROC) and calibration curves, and a nomogram was constructed to integrate risk scores with clinical parameters. Finally, genes from the gene sets of the model were acquired and analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interactions (PPI) via plugin Molecular Complex Detection (MCODE) and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) in Cytoscape in both tumor and non-tumor tissues.

Results

Three BC subtypes were characterized by immunologic and hallmark gene sets, with subtype 1 patients showing worse survival. The prognostic model, based on seven gene sets, effectively stratified risk, with high-risk patients having significantly shorter survival. GO, KEGG, and PPI analyses indicated distinct influences of non-tumor and tumor tissues on the prognosis of BC patients.

Conclusions

We constructed and validated a novel prognostic model for risk stratification in BC based on immunologic and hallmark genes sets, which presents a novel perspective on rational treatment approaches and accurate prognostic evaluations for BC by considering both tumor and adjacent non-tumor tissues. This highlights the importance of focusing on alterations in both tumor and adjacent non-tumor tissues, rather than solely on the tumor itself.

Elevated α-1,2-mannosidase MAN1C1 in glioma stem cells and its implications for immunological changes and prognosis in glioma patients

Glioblastoma multiforme (GBM) is the most aggressive type of primary brain tumor, and the presence of glioma stem cells (GSCs) has been linked to its resistance to treatments and recurrence. Additionally, aberrant glycosylation has been implicated in the aggressiveness of cancers. However, the influence and underlying mechanism of N-glycosylation on the GSC phenotype and GBM malignancy remain elusive. Here, we performed an in-silico analysis approach on publicly available datasets to examine the function of N-glycosylation-related genes in GSCs and gliomas, accompanied by a qRT-PCR validation experiment. We found that high α-1,2-mannosidase MAN1C1 is associated with immunological functions and worse survival of glioma patients. Differential gene expression analysis and qRT-PCR validation revealed that MAN1C1 is highly expressed in GSCs. Furthermore, higher MAN1C1 expression predicts worse outcomes in glioma patients. Also, MAN1C1 expression is increased in the perinecrotic region of GBM and is associated with immunological and inflammatory functions, a hallmark of the GBM mesenchymal subtype. Further analysis confirmed that MAN1C1 expression is closely associated with infiltrating immune cells and disrupted immune response in the GBM microenvironment. These suggest that MAN1C1 is a potential biomarker for gliomas and may be important as an immunotherapeutic target for GBM.

Interplay of RNA m6A Modification-Related Geneset in Pan-Cancer

Background: N6-methyladenosine (m6A), is the most common modification found in mRNA and lncRNA in higher organisms and plays an important role in physiology and pathology. However, its role in pan-cancer has not been explored. Results: A total of 31 m6A modification regulators, including 12 writers, 2 erasers, and 17 readers are identified in the current study. The functional analysis of the regulators results in the enrichment of processes, primarily related to RNA modification and metabolism, and the PPI network reveals multiple interactions among the regulators. The mRNA expression analysis reveals a high expression for most of the regulators in pan-cancer. Most of the m6A regulators are found to be mutated across the cancers, with ZC3H13, VIRMA, and PRRC2A having a higher frequency rate. Significant correlations of the regulators with clinicopathological parameters, such as age, gender, tumor stage, and grade are identified in pan-cancer. The m6A regulators' expression is found to have significant positive correlations with the miRNAs in pan-cancer. The expression pattern of the m6A regulators is able to classify the tumors into different subclusters as well as into high- and low-risk groups. These tumor groups show differential patterns in terms of their immune cell infiltration, tumor stemness score, genomic heterogeneity score, expression of immune regulatory/checkpoint genes, and correlations between the regulators and the drugs. Conclusions: Our study provide a comprehensive overview of the functional roles, genetic and epigenetic alterations, and prognostic value of the RNA m6A regulators in pan-cancer.

Multicenter cohort analysis of anoikis and EMT: implications for prognosis and therapy in lung adenocarcinoma

BACKGROUND

Anoikis and epithelial-mesenchymal transition (EMT) are pivotal in the distant metastasis of lung adenocarcinoma (LUAD). A detailed understanding of their interplay and the identification of key genes is vital for effective therapeutic strategies against LUAD metastasis.

METHODS

Key prognostic genes related to anoikis and EMT were identified through univariate Cox regression analysis. We utilized ten machine learning algorithms to develop the Anoikis and EMT-Related Optimal Model (AEOM). The TCGA-LUAD dataset served as the training cohort, while six additional international multicenter LUAD datasets were employed as validation cohorts. The average concordance index (c-index) was used to evaluate model performance and identify the most effective model. Subsequent multi-omics analyses were conducted to explore differences in pathway enrichment, immune infiltration, and mutation landscapes between high and low AEOM groups. Experimental validation demonstrated that RHPN2, a key biomarker within the model, acts as an oncogene facilitating LUAD progression.

RESULTS

The AEOM displayed superior prognostic predictive performance for LUAD patients, outperforming numerous previously published LUAD signatures. Biologically, the AEOM was notably associated with immune features; the high AEOM group exhibited decreased immune activity and a tendency towards immune-cold tumors, as well as a higher tumor mutational burden (TMB). Subgroup analysis revealed that the low AEOM + high TMB group had the most favorable prognosis. The high AEOM group was primarily enriched in cell cycle-related pathways, promoting cancer cell proliferation. RHPN2, a crucial gene within the AEOM (correlation = 0.85, P < 0.05), was linked to poorer prognosis in LUAD patients with elevated RHPN2 expression. Further in vitro experiments showed that RHPN2 modulates LUAD cell proliferation and invasion.

CONCLUSION

The AEOM provides a robust prognostic model for LUAD, uncovering critical immune and biological pathways, with RHPN2 identified as a key oncogenic driver. These findings offer valuable insights for targeted therapies and enhanced patient outcomes.

Machine learning based androgen receptor regulatory gene-related random forest survival model for precise treatment decision in prostate cancer

Background

It has been demonstrated that aberrant androgen receptor (AR) signaling contributes to the pathogenesis of prostate cancer (PCa). To date, the most efficacious strategy for the treatment of PCa remains to target the AR signaling axis. However, numerous PCa patients still face the issue of overtreatment or undertreatment. The establishment of a precise risk prediction model is urgently needed to distinguish patients with high-risk and select appropriate treatment modalities.

Methods

In this study, a consensus AR regulatory gene-related signature (ARS) was developed by integrating a total of 101 algorithm combinations of 10 machine learning algorithms. We evaluated the value of ARS in predicting patient prognosis and the therapeutic effects of the various treatments. Additionally, we conducted a screening of therapeutic targets and agents for high-risk patients, followed by the verification in vitro and in vivo.

Results

ARS was an independent risk factor for biochemical recurrence and distant metastasis in PCa patients. The enhanced and consistent prognostic predictive capability of ARS across various platforms was confirmed when compared with 44 previously published signatures. More importantly, PCa patients in the ARShigh group benefit more from PARP inhibitors and immunotherapy, while chemotherapy, radiotherapy, and AR-targeted therapy are more effective for ARSlow patients. The results of in silico screening suggest that AURKB could potentially serve as a promising therapeutic target for ARShigh patients.

Conclusions

Collectively, this prediction model based on AR regulatory genes holds great clinical translational potential to solve the dilemma of treatment choice and identify potential novel therapeutic targets in PCa.

The Potential Impact of HNRNPA2B1 on Human Cancers Prognosis and Immune Microenvironment

HNRNPA2B1 is a member of the HNRNP family, which is associated with telomere function, mRNA translation, and splicing, and plays an important role in tumor development. To date, there have been no pan-cancer studies of HNRNPA2B1, particularly within the TME. Therefore, we conducted a pan-cancer analysis of HNRNPA2B1 using TCGA data. Based on datasets from TCGA, TARGET, Genotype-Tissue Expression, and Human Protein Atlas, we employed a range of bioinformatics approaches to explore the potential oncogenic role of HNRNPA2B1. This included analyzing the association of HNRNPA2B1 expression with prognosis, tumor mutation burden (TMB), microsatellite instability (MSI), immune response, and immune cell infiltration of individual tumors. We further validated the bioinformatic findings using immunohistochemistry techniques. HNRNPA2B1 was found to be differentially expressed across most tumor types in TCGA's pan-cancer database and was predictive of poorer clinical staging and survival status. HNRNPA2B1 expression was also closely linked to TMB, MSI, tumor stemness, and chemotherapy response. HNRNPA2B1 plays a significant role in the TME and is involved in the regulation of novel immunotherapies. Its expression is significantly associated with the infiltration of macrophages, dendritic cells, NK cells, and T cells. Furthermore, HNRNPA2B1 is closely associated with immune checkpoints, immune-stimulatory genes, immune-inhibitory genes, MHC genes, chemokines, and chemokine receptors. We performed a comprehensive evaluation of HNRNPA2B1, revealing its potential role as a prognostic indicator for patients and its immunomodulatory functions.

Sushi Domain Containing 2 Dysfunction Contributes to Cancer Progression in Patients with Bladder Cancer

Bladder cancer is the most prevalent type of cancer in Taiwan, and therefore, enhancing the diagnostic sensitivity of biomarkers for early-stage tumors and identifying therapeutic targets to improve patient survival rates are essential. Although Sushi Domain Containing 2 (SUSD2) dysfunction has been identified in several types of human cancer, its biological role in bladder cancer remains unclear. Analysis of The Cancer Genome Atlas revealed significantly higher expression of SUSD2 mRNA in bladder cancer tissues than in adjacent normal tissues. This elevated expression of SUSD2 significantly correlated with pathological stage (p = 0.029), pN stage (p < 0.001), and pM stage (p = 0.047). Univariate analysis revealed that high SUSD2 expression was associated with decreased overall survival (crude hazard ratio = 1.70, 95% confidence interval = 1.13-2.56, p = 0.01). Multivariate analysis revealed a significant correlation between high SUSD2 expression and poor survival outcomes (adjusted hazard ratio = 1.53, 95% confidence interval = 1.01-2.31, p = 0.043). IHC analysis revealed a significant correlation between elevated SUSD2 protein levels and unfavorable pathological stages (p < 0.001). SUSD2 suppression significantly reduced the proliferation, colony formation, and invasion of bladder cancer cells. In addition, cell cycle analysis revealed that SUSD2 knockdown induced G2/M phase arrestin bladder cancer cells. Tumor Immune Estimation Resource analysis indicated that expression of SUSD2 was significantly associated with macrophage infiltration and M2 macrophage polarization in bladder cancer. In addition, miR-383-5p directly targeted the 3'UTR of SUSD2, with its ectopic expression inhibiting the growth and motility of bladder cancer cells. Our study revealed that miR-383-5p/SUSD2 axis dysfunction may contribute to a poor prognosis for bladder cancer by affecting cell growth, metastasis, and the tumor microenvironment.

S100A9, as a potential predictor of prognosis and immunotherapy response for GBM, promotes the malignant progression of GBM cells and migration of M2 macrophages

In the past decades, the therapeutic effect of glioblastoma (GBM) has not been significantly improved. Generous evidence indicates that S100A9 has a wide range of functions in tumors, but its exploration in GBM is less. The purpose of this study is to conduct a comprehensive bioinformatics analysis and cytological experiment on S100A9 in GBM. The expression data and clinical data of GBM samples were downloaded from the public database, and comprehensive bioinformatics analysis was performed on S100A9 in GBM using R software. Wound healing assay and transwell assay were used to detect the migration activity of cells, and colony formation assay, EdU staining, and CCK-8 assay were used to detect the proliferation activity of cells. The effect of S100A9 on the migration activity of M2 macrophages was verified by the cell co-culture method. The protein expression was detected by western blotting and immunohistochemical staining. S100A9 is an independent prognostic factor in GBM patients and is related to poor prognosis. It can be used as an effective tool to predict the response of GBM patients to immune checkpoint inhibitors (ICIs). In addition, S100A9 can promote the malignant progression of GBM and the migration of M2 macrophages. On the whole, our study highlights the potential value of S100A9 in predicting prognosis and immunotherapeutic response in GBM patients. More importantly, S100A9 may promote the malignant progress of GBM by involving in some carcinogenic pathways and remodeling the tumor microenvironment (TME).

Pan-cancer analysis and single-cell analysis reveals FAM110B as a potential target for survival and immunotherapy

Background: FAM110B belongs to the family that has a 110 sequence similarity (FAM110) and is located in the centrosome and mitotic spindle. FAM110B has been linked to tumor cell growth in earlier research. Uncertainty exists regarding FAM110B's function within the tumor microenvironment is unclear as well as pan-cancer. Methods: In order to assess the variation in FAM110B expression within normal and pan-cancer tissues, we combined the TCGA and GTEx databases. The cBioPortal database and the GSCALite platform were used to examine the variation in genome and methylation alteration of FAM110B. Cox regression, Kaplan-Meier, and SangerBox were employed to examine the clinical features and prognosis of FAM110B and pan-cancer. The purpose of the correlational research was to investigate the associations within immunerelated genes, tumor mutation burden, microsatellite instability, immune-related genes, and immunological checkpoints and FAM110B expression. ESTIMATE, EPIC, QUANTISEQ, and MCPCOUNTER methods were used to calculate the interaction among FAM110B expression as well as the tumor immune microenvironment. The immunoinfiltration and function of FAM110B were analyzed by single-cell databases (TISCH and CancerSEA). Finally, we evaluated the sensitivity of FAM110B to small-molecule medications through GDSC and CTRP databases. Results: The transcription and protein expression of FAM110B varies significantly throughout cancer types, and this has predictive value for the prognosis of some tumors; including brain lower grade glioma (LGG), stomach adenocarcinoma (STAD), pancreatic adenocarcinoma (PAAD), etc. In the tumor microenvironment, the expression level of FAM110B was associated with immune cell infiltration, immune checkpoint immune regulatory genes, tumor mutational burden, and microsatellite fragility to a certain extent. Conclusion: This work investigates the possibility of utility of FAM110B as a marker to forecast pan-cancer immunotherapy response, providing a theoretical basis for cancer therapy.

Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma

Neutrophil extracellular traps (NETs) are novel inflammatory cell death in neutrophils. Emerging studies demonstrated NETs contributed to cancer progression and metastases in multiple ways. This study intends to provide a prognostic NETs signature and therapeutic target for lung adenocarcinoma (LUAD) patients. Consensus cluster analysis performed by 38 reported NET-related genes in TCGA-LUAD cohorts. Then, WGCNA network was conducted to investigate characteristics genes in clusters. Seven machine learning algorithms were assessed for training of the model, the optimal model was picked by C-index and 1-, 3-, 5-year ROC value. Then, we constructed a NETs signature to predict the overall survival of LUAD patients. Moreover, multi-omics validation was performed based on NETs signature. Finally, we constructed stable knockdown critical gene LUAD cell lines to verify biological functions of Phospholipid Scramblase 1 (PLSCR1) in vitro and in vivo. Two NETs-related clusters were identified in LUAD patients. Among them, C2 cluster was provided as "hot" tumor phenotype and exhibited a better prognosis. Then, WGCNA network identified 643 characteristic genes in C2 cluster. Then, Coxboost algorithm proved its optimal performance and provided a prognostic NETs signature. Multi-omics revealed that NETs signature was involved in an immunosuppressive microenvironment and predicted immunotherapy efficacy. In vitro and in vivo experiments demonstrated that knockdown of PLSCR1 inhibited tumor growth and EMT ability. Besides, cocultural assay indicated that the knockdown of PLSCR1 impaired the ability of neutrophils to generate NETs. Finally, tissue microarray (TMA) for LUAD patients verified the prognostic value of PLSCR1 expression. In this study, we focus on emerging hot topic NETs in LUAD. We provide a prognostic NETs signature and identify PLSCR1 with multiple roles in LUAD. This work can contribute to risk stratification and screen novel therapeutic targets for LUAD patients.

Identification and validation of basement membrane-related genes predicting prognosis and immune infiltration associated with bladder cancer

Bladder cancer (BC) is fatal during muscle invasion and treatment progress is limited. In this study, we aimed to construct and validate basement membrane (BM)-associated gene prognosis to predict BC progression and tumor immune infiltration correlation. We choreographed BM-related genes in the Cancer Genome Atlas (TCGA) database using COX regression and least absolute shrinkage and selection operator (LASSO) analysis, and the predictive value of BM-related genes was further validated by the GSE32548, GSE129845, and immunohistochemistry staining. All analyses were performed with R-version 4.2.2, and its appropriate packages. Three genes were identified to construct a gene signature to predictive of BC prognosis. We divided the TCGA database into 2 groups, and patients in the high-risk group had worse overall survival (OS) than those in the low-risk group. In GSE32548, we confirmed that patients in the high-risk group had a poorer prognosis compared to those in the low-risk group in terms of OS. Immunohistochemical staining of EPEMP1, GPC2, and ITGA3 showed significantly higher expression at the protein level in BC tissues than in normal tissues. The Spearman analysis showed risk score was positively correlated with B cell naïve, Macrophages M2, and Mast cells resting. stromal score, immune score, and ESTIMATE scores were significantly higher in the high-risk group. drugs sensitivity analysis showed IC50 of Cisplatin, Gemcitabine, and Methotrexate in the high-risk group was significantly higher than that in the low-risk group. We identified 3 prognostic genes from a novel perspective of BM genes as effective risk stratification tools for BC patients.

Identification of HOXC Gene Family as Prognostic and Immune-Related Biomarkers in Breast Cancer Through mRNA Transcriptional Profile and Experimental Validation

Breast cancer (BC) is the most common malignancy in women worldwide, and more effective biomarkers are urgently needed for the prevention and treatment of BC. Our study aimed to investigate the role of the HOXC gene family (HOXCs) and its relationship with the immune response in BC. The differential expression of HOXCs and its clinical prognostic significance in BC were explored using bioinformatics analysis, and the cBioPortal database was used to evaluate the genetic mutation profile of the HOXCs in BC. The results indicated that the expression levels of HOXC4, 10, 11, 12, and 13 were significantly increased in BC tissues compared with the normal tissues, and expressions of these genes were closely associated with BC stage, among them, high expression levels of HOXC10 and HOXC13 predicted poor outcome in BC patients. In addition, to elucidate the essential role of HOXCs in the tumor microenvironment and immunotherapeutic response of BC, the impact of HOXCs on the regulation of immune infiltration in BC was comprehensively assessed. The result showed that HOXC10 and HOXC13 expressions were significantly positively linked with the infiltration levels of CD8+T cell and M1 macrophage, while they were negatively related to Mast and Natural killer cells, suggesting the important influence of HOXCs on regulating tumor immunity in BC patients. Lastly, the RT-qPCR assay was employed to validate HOXCs expression in samples of BC patients. In conclusion, HOXCs may be a promising prognostic indicator and could regulate the immune infiltration in BC patients, thus being a promising targeted immunotherapy for BC.

Bibliometric insights into drug resistance in bladder cancer: Two decades of progress (1999-2022)

Aims

To provide a comprehensive bibliometric overview of drug resistance in bladder cancer (BC) from 1999 to 2022, aiming to illuminate its historical progression and guide future investigative avenues.

Methods

Literature on BC drug resistance between 1999 and 2022 was sourced from the Web of Science. Visual analyses were executed using Vosviewer and Citespace software, focusing on contributions by countries, institutions, journals, authors, references, and keywords.

Results

From 2727 publications, a marked growth in BC drug resistance studies was discerned over the two decades. Prominent among all institutions is the University of Texas System. The majority of top-ranked journals were American. In authorship significance, McConkey DJ led in publications, while Bellmunt J dominated in citations. Research topics predominantly spanned cancer demographics, drug efficacy evaluations, molecular features, oncology subtypes, and individualized treatment strategies, with a notable contemporary emphasis on molecular mechanisms behind drug resistance and nuances of ICIs.

Conclusions

Our bibliometric analysis charts the landscape of BC drug resistance research from 1999 to 2022. While the study of resistance mechanisms has been robust, there's an evident need for deeper exploration into the molecular intricacies and the potential of ICIs and targeted therapeutic strategies.

Ferroptosis-related lncRNAs: Distinguishing heterogeneity of the tumour microenvironment and predicting immunotherapy response in bladder cancer

Ferroptosis, a cell death pathway dependent on iron, has been shown in research to play a role in the development, advancement, and outlook of tumours through ferroptosis-related lncRNAs (FRLRs). However, the value of the FRLRs in bladder cancer (BLCA) has not been thoroughly investigated. This research project involved developing a predictive model using ten specific FRLRs (AC099850.4, AL731567.1, AL133415.1, AC021321.1, SPAG5-AS1, HMGA2-AS1, RBMS3-AS3, AC006160.1, AL583785.1, and AL662844.4) through univariate COX and LASSO regression techniques. The validation of this signature as a standalone predictor was confirmed in a group of 65 patients from the urology bladder tumour database at the First Affiliated Hospital of Wenzhou Medical University in Wenzhou, China. Patients were categorized based on their median risk score into either a low-risk group or a high-risk group. Enrichment analysis identified possible molecular mechanisms that could explain the variations in clinical outcomes observed in high-risk and low-risk groups. Moreover, we explored the correlation between FLPS and immunotherapy-related indicators. The ability of FLPS to forecast the effectiveness of immunotherapy was validated by the elevated levels of immune checkpoint genes (PD-L1, CTLA4, and PD-1) in the group at high risk. We also screened the crucial FRLR (HMGA2-AS1) through congruent expression and prognostic conditions and established a ceRNA network, indicating that HMGA2-AS1 may affect epithelial-mesenchymal transition by modulating the Wnt signalling pathway through the ceRNA mechanism. We identified the top five mRNAs (NFIB, NEGR1, JAZF1, JCAD, and ESM1) based on random forest algorithm and analysed the relationship between HMGA2-AS1, the top five mRNAs, and immunotherapy, and their interactions with drug sensitivities. Our results suggest that patients with BLCA have a greater sensitivity to four drugs (dasatinib, pazopanib, erismodegib and olaparib). Our study provides new insights into the TME, key signalling pathways, genome, and potential therapeutic targets of BLCA, with future guidance for immunotherapy and targeted precision drugs.

Identification of a risk model for prognostic and therapeutic prediction in renal cell carcinoma based on infiltrating M0 cells

The tumor microenvironment (TME) comprises immune-infiltrating cells that are closely linked to tumor development. By screening and analyzing genes associated with tumor-infiltrating M0 cells, we developed a risk model to provide therapeutic and prognostic guidance in clear cell renal cell carcinoma (ccRCC). First, the infiltration abundance of each immune cell type and its correlation with patient prognosis were analyzed. After assessing the potential link between the depth of immune cell infiltration and prognosis, we screened the infiltrating M0 cells to establish a risk model centered on three key genes (TMEN174, LRRC19, and SAA1). The correlation analysis indicated a positive correlation between the risk score and various stages of the tumor immune cycle, including B-cell recruitment. Furthermore, the risk score was positively correlated with CD8 expression and several popular immune checkpoints (ICs) (TIGIT, CTLA4, CD274, LAG3, and PDCD1). Additionally, the high-risk group (HRG) had higher scores for tumor immune dysfunction and exclusion (TIDE) and exclusion than the low-risk group (LRG). Importantly, the risk score was negatively correlated with the immunotherapy-related pathway enrichment scores, and the LRG showed a greater therapeutic benefit than the HRG. Differences in sensitivity to targeted drugs between the HRG and LRG were analyzed. For commonly used targeted drugs in RCC, including axitinib, pazopanib, temsirolimus, and sunitinib, LRG had lower IC50 values, indicating increased sensitivity. Finally, immunohistochemistry results of 66 paraffin-embedded specimens indicated that SAA1 was strongly expressed in the tumor samples and was associated with tumor metastasis, stage, and grade. SAA1 was found to have a significant pro-tumorigenic effect by experimental validation. In summary, these data confirmed that tumor-infiltrating M0 cells play a key role in the prognosis and treatment of patients with ccRCC. This discovery offers new insights and directions for the prognostic prediction and treatment of ccRCC.

Metabolic landscape of head and neck squamous cell carcinoma informs a novel kynurenine/Siglec-15 axis in immune escape

BACKGROUND

Metabolic reprograming and immune escape are two hallmarks of cancer. However, how metabolic disorders drive immune escape in head and neck squamous cell carcinoma (HNSCC) remains unclear. Therefore, the aim of the present study was to investigate the metabolic landscape of HNSCC and its mechanism of driving immune escape.

METHODS

Analysis of paired tumor tissues and adjacent normal tissues from 69 HNSCC patients was performed using liquid/gas chromatography-mass spectrometry and RNA-sequencing. The tumor-promoting function of kynurenine (Kyn) was explored in vitro and in vivo. The downstream target of Kyn was investigated in CD8+ T cells. The regulation of CD8+ T cells was investigated after Siglec-15 overexpression in vivo. An engineering nanoparticle was established to deliver Siglec-15 small interfering RNA (siS15), and its association with immunotherapy response were investigated. The association between Siglec-15 and CD8+ programmed cell death 1 (PD-1)+ T cells was analyzed in a HNSCC patient cohort.

RESULTS

A total of 178 metabolites showed significant dysregulation in HNSCC, including carbohydrates, lipids and lipid-like molecules, and amino acids. Among these, amino acid metabolism was the most significantly altered, especially Kyn, which promoted tumor proliferation and metastasis. In addition, most immune checkpoint molecules were upregulated in Kyn-high patients based on RNA-sequencing. Furthermore, tumor-derived Kyn was transferred into CD8+ T cells and induced T cell functional exhaustion, and blocking Kyn transporters restored its killing activity. Accroding to the results, mechanistically, Kyn transcriptionally regulated the expression of Siglec-15 via aryl hydrocarbon receptor (AhR), and overexpression of Siglec-15 promoted immune escape by suppressing T cell infiltration and activation. Targeting AhR in vivo reduced Kyn-mediated Siglec-15 expression and promoted intratumoral CD8+ T cell infiltration and killing capacity. Finally, a NH2-modified mesoporous silica nanoparticle was designed to deliver siS15, which restored CD8+ T cell function status and enhanced anti-PD-1 efficacy in tumor-bearing immunocompetent mice. Clinically, Siglec-15 was positively correlated with AhR expression and CD8+PD-1+ T cell infiltration in HNSCC tissues.

CONCLUSIONS

The findings describe the metabolic landscape of HNSCC comprehensively and reveal that the Kyn/Siglec-15 axis may be a novel potential immunometabolism mechanism, providing a promising therapeutic strategy for cancers.

Identification of a cisplatin resistant-based prognostic immune related gene signature in MIBC

Cisplatin resistance plays a significant role in the dismal prognosis and progression of muscle-invasive bladder cancer (MIBC). However, the strategies to predict prognosis and cisplatin resistance are inefficient, and it remains unclear whether cisplatin resistance is associated with tumor immunity. In this study, we integrated the transcriptional data from cisplatin-resistant cell lines and a TCGA-MIBC cohort to establish cisplatin-resistance-related cluster classification and a cisplatin-resistance-related gene risk score (CRRGRS). Kaplan-Meier survival curves showed that compared with those in low CRRGRS group, MIBC patients belonging to high CRRGRS group had worse prognosis in TCGA-MIBC cohort and external GEO cohorts. Meanwhile, CRRGRS was able to help forecast chemotherapy and immunotherapy response of MIBC patients in the TGCA cohort and IMvigor210 cohort. Moreover, compared with the low CRRGRS group, the high CRRGS group possessed a relatively immunosuppressive "cold tumor" phenotype with a higher tumor immune dysfunction and exclusion (TIDE) score, ESTIMATE score, stromal score and immune score and a lower immunophenoscore (IPS) score. The upregulated expression levels of immune checkpoint genes, including PD-1, PD-L1 and CTLA4, in the high CRRGRS group also further indicated that a relative immunosuppressive tumor microenvironment may exist in MIBC patients belonging to high CRRGRS group. In addition, we integrated CRRGRS and clinical characteristics with prognostic value to develop a nomogram, which could help forecast overall survival of MIBC patients. Furthermore, DIAPH3 was identified as a regulator of proliferation and cisplatin resistance in MIBC. The expression of DIAPH3 was increased in cisplatin-resistant cell lines and chemotherapy-unsensitive people. Further mechanism exploration revealed that DIAPH3 facilitated tumor proliferation and cisplatin resistance by regulating the NF-kB and epithelial-mesenchymal transition (EMT) pathways. In conclusion, the comprehensive investigations of CRRGRS increased the understanding of cisplatin resistance and provided promising insights to restrain tumor growth and overcome chemoresistance by targeting DIAPH3.

Analysis of the sodium pump subunit ATP1A3 in glioma patients: Potential value in prognostic prediction and immunotherapy

The ATP1A3 gene is associated with the development and progression of neurological diseases. However, the pathological function and therapeutic value of ATP1A3 in glioblastoma (GBM) remains unknown. In this study, we tried to explore the correlation between the ATP1A3 gene expression and immune features in GBM samples. We found that ATP1A3 gene expression levels showed significant negative correlation with immune checkpoints such as PD-L1, CTLA-4 and IDO1. Next, ATP1A3 gene expression levels showed significant negative correlation with the anti-cancer immune cell process, the immune score and stromal score. By grouping ATP1A3 expression levels, we found that that immunomodulator-related genes and tumor-associated immune cell effector gene expression levels were associated with lower ATP1A3 expression. In addition, immunotherapy prediction pathway activity and a majority of the anti-cancer immune cell process activity levels were also showed to be correlated with lower ATP1A3 gene expression. Further, nine prognostic factors were identified by prognostic analysis, and a GBM prognostic model (risk score) was established. We applied the model to the TCGA GBM training set sample and the GSE4412 validation set sample and found that patients in the high risk score subgroup had significantly shorter survival time, demonstrating the prognostic value and prognostic efficacy of the risk score. Furthermore, ATP1A3 overexpression has also been found to sensitize cancer cells to anti-PD-1 therapy. In conclusion, we showed that ATP1A3 is a highly promising treatment target in GBM and the risk score is an independent prognostic factor for cancer and can be used to help guide the prediction of survival time in patients with GBM.

IL4I1: a novel molecular biomarker represents an inflamed tumor microenvironment and precisely predicts the molecular subtype and immunotherapy response of bladder cancer

Introduction: IL4I1, also known as Interleukin-4-induced gene 1, is an enzyme that can modulate the immune system by acting as a L-amino acid oxidase. Nevertheless, a precise understanding of the correlation of IL4I1 with immunological features and immunotherapy efficacy in bladder cancer (BLCA) remains incomplete. Methods: We analyzed RNA sequencing data from the Cancer Genome Atlas (TCGA) to investigate the immune function and prognostic importance of IL4I1 across different cancer types. We further examined the TCGA-BLCA cohort for correlations between IL4I1 and various immunological characteristics of tumor microenvironment (TME), such as cancer immune cycle, immune cell infiltration, immune checkpoint expression and T cell inflamed score. Validation was conducted using two independent cohort, GSE48075 and E-MTAB-4321. Finally, RNA sequencing data from the IMvigor210 cohort and immunohistochemistry assays were employed to validate the predictive value of IL4I1 for the TME and immunotherapy efficacy. Results: In our findings, a positive correlation was observed between IL4I1 expression and immunomodulators expression, immune cell infiltration, the cancer immune cycle, and T cell inflamed score in BLCA, suggesting a significant link to the inflamed TME. In addition, studies have shown that IL4I1 elevated levels of individuals tend to be more performance for basal subtype and exhibit enhanced response rates to diverse treatment modalities, specifically immunotherapy. Clinical data from the IMvigor 210 cohort confirmed a higher rate of response to immunotherapy and better survival benefits in patients with high IL4I1 expression. Discussion: To summarize, our research showed that elevated IL4I1 levels are indicative of an inflamed TME, the basal subtype, and a more favorable response to various treatment methods, especially immune checkpoint blockade therapy in BLCA.

DDIT3 is associated with breast cancer prognosis and immune microenvironment: an integrative bioinformatic and immunohistochemical analysis

DNA damage-inducible transcript 3 (DDIT3) is a transcription factor central to apoptosis, differentiation, and stress response. DDIT3 has been extensively studied in cancer biology. However, its precise implications in breast cancer progression and its interaction with the immune microenvironment are unclear. In this study, we utilized a novel multi-omics integration strategy, combining bulk RNA sequencing, single-cell sequencing, spatial transcriptomics and immunohistochemistry, to explore the role of DDIT3 in breast cancer and establish the correlation between DDIT3 and poor prognosis in breast cancer patients. We identified a robust prognostic signature, including six genes (unc-93 homolog B1, TLR signaling regulator, anti-Mullerian hormone, DCTP pyrophosphatase 1, mitochondrial ribosomal protein L36, nuclear factor erythroid 2, and Rho GTPase activating protein 39), associated with DDIT3. This signature stratified the high-risk patient groups, characterized by increased infiltration of the regulatory T cells and M2-like macrophages and fibroblast growth factor (FGF)/FGF receptor signaling activation. Notably, the high-risk patient group demonstrated enhanced sensitivity to immunotherapy, presenting novel therapeutic opportunities. Integrating multi-omics data helped determine the spatial expression pattern of DDIT3 in the tumor microenvironment and its correlation with immune cell infiltration. This multi-dimensional analysis provided a comprehensive understanding of the intricate interplay between DDIT3 and the immune microenvironment in breast cancer. Overall, our study not only facilitates understanding the role of DDIT3 in breast cancer but also offers innovative insights for developing prognostic models and therapeutic strategies. Identifying the DDIT3-related prognostic signature and its association with the immune microenvironment provided a promising avenue for personalized breast cancer treatment.

SLC7A5 correlated with malignancies and immunotherapy response in bladder cancer

BACKGROUND

Metabolic reprogramming contributes to bladder cancer development. This study aimed to understand the role of SLC7A5 in bladder cancer.

METHODS

We systematically analyzed the correlation between SLC7A5 and bladder cancer through various approaches, including bioinformatics, western blotting, cell cycle analysis, cell proliferation assays, and invasion experiments. We also investigated the immunological features within the tumor microenvironment (TME), encompassing cancer immune cycles, immune modulators, immune checkpoints, tumor-infiltrating immune cells (TIIC), T cell inflammation scores, and treatment responses. Additionally, for a comprehensive assessment of the expression patterns and immunological roles of SLC7A5, pan-cancer analysis was performed using cancer genomics datasets.

RESULTS

SLC7A5 was associated with adverse prognosis in bladder cancer patients, activating the Wnt pathway and promoting bladder cancer cell cycle progression, proliferation, migration, and invasion. Based on the evidence that SLC7A5 positively correlated with immunomodulators, TIIC, the cancer immune cycle, immune checkpoint and T cell inflammation scores, we also found that SLC7A5 was associated with the inflammatory tumor immune microenvironment. EGFR-targeted therapy, cancer immunotherapy, and radiation therapy were effective for patients with high SLC7A5 expression in bladder cancer. Low SLC7A5 patients were, however, sensitive to targeted therapies and anti-angiogenic therapy, such as blocking β-catenin network, PPAR-γ and FGFR3 signaling. Anti-SLC7A5 combined with cancer immunotherapy may have greater effectiveness than either therapy alone. Furthermore, we observed specific overexpression of SLC7A5 in TME of various cancers.

CONCLUSION

SLC7A5 can predict therapeutic response to immunotherapy, radiotherapy and chemotherapy in bladder cancer patients. Targeting SLC7A5 in combination with immunotherapy may be a potentially appropriate treatment option.

Deciphering the potential role of PGRN in regulating CD8+ T cell antitumor immunity

A key factor contributing to resistance in immune checkpoint blockade (ICB) therapies is CD8+ T-cell tolerance in the tumor microenvironment (TME), partly resulting from upregulating coinhibitory receptors. Here, we describe the role of PGRN as a coinhibitory molecule that modulates the antitumor response of CD8+ T cells, thus presenting a novel immunosuppressive target for lung cancer. The in vivo subcutaneous transplanted lung cancer model showed that PGRN expression was elevated on CD8+ T cells that infiltrated transplanted lung cancers. Furthermore, PGRN deficiency was found to specifically encourage the infiltration of CD8+ T cells, enhance their proliferation, migration, and activation, and resist apoptosis, ultimately inhibiting tumor growth. This was achieved by PGRN knockout, increasing the production of T cell chemokine CCL3, which boosts the antitumor immune response induced by CD8+ T cells. Critically, the PD-L1 inhibitor exhibited a synergistic effect in enhancing the antitumor response in PGRN-/- mice. In summary, our findings highlight the significance of PGRN as a novel target for boosting CD8+ T cells antitumor immunity and its potential to overcome the resistance in ICB therapy.

Glutamine Metabolism Heterogeneity in Glioblastoma Unveils an Innovative Combination Therapy Strategy

Treatment of glioblastoma multiforme (GBM) remains challenging. Unraveling the orchestration of glutamine metabolism may provide a novel viewpoint on GBM therapy. The study presented a full and comprehensive comprehending of the glutamine metabolism atlas and heterogeneity in GBM for facilitating the development of a more effective therapeutic choice. Transcriptome data from large GBM cohorts were integrated in this study. A glutamine metabolism-based classification was established through consensus clustering approach, and a classifier by LASSO analysis was defined for differentiating the classification. Prognosis, signaling pathway activity, tumor microenvironment, and responses to immune checkpoint blockade (ICB) and small molecular drugs were characterized in each cluster. A combinational therapy of glutaminase inhibitor CB839 with dihydroartemisinin (DHA) was proposed, and the influence on glutamine metabolism, apoptosis, reactive oxygen species (ROS), and migration was measured in U251 and U373 cells. We discovered that GBM presented heterogeneous glutamine metabolism-based clusters, with unique survival outcomes, activity of signaling pathways, tumor microenvironment, and responses to ICB and small molecular compounds. In addition, the classifier could accurately differentiate the two clusters. Strikingly, the combinational therapy of CB839 with DHA synergistically attenuated glutamine metabolism, triggered apoptosis and ROS accumulation, and impaired migrative capacity in GBM cells, demonstrating the excellent preclinical efficacy. Altogether, our findings unveil the glutamine metabolism heterogeneity in GBM and propose an innovative combination therapy of CB839 with DHA for this malignant disease.

Identification of neutrophil extracellular trap-driven gastric cancer heterogeneity and C5AR1 as a therapeutic target

Neutrophil extracellular traps (NETs) are implicated in gastric cancer (GC) growth, metastatic dissemination, cancer-associated thrombosis, etc. This work is conducted to elucidate the heterogeneity of NETs in GC. The transcriptome heterogeneity of NETs is investigated in TCGA-STAD via a consensus clustering algorithm, with subsequent external verification in the GSE88433 and GSE88437 cohorts. Clinical and molecular traits, the immune microenvironment, and drug response are characterized in the identified NET-based clusters. Based upon the feature genes of NETs, a classifier is built for estimating NET-based clusters via machine learning. Multiple experiments are utilized to verify the expressions and implications of the feature genes in GC. A novel NET-based classification system is proposed for reflecting the heterogeneity of NETs in GC. Two NET-based clusters have unique and heterogeneous clinical and molecular features, immune microenvironments, and responses to targeted therapy and immunotherapy. A logistic regression model reliably differentiates the NET-based clusters. The feature genes C5AR1, CSF1R, CSF2RB, CYBB, HCK, ITGB2, LILRB2, MNDA, MPEG1, PLEK, SRGN, and STAB1 are proven to be aberrantly expressed in GC cells. Specific knockdown of C5AR1 effectively hinders GC cell growth and elicits intracellular ROS accumulation. In addition, its suppression suppresses the aggressiveness and EMT phenotype of GC cells. In all, NETs are the main contributors to intratumoral heterogeneity and differential drug sensitivity in GC, and C5AR1 has been shown to trigger GC growth and metastatic spread. These findings collectively provide a theoretical basis for the use of anti-NETs in GC treatment.

Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies

Objective

Significant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy.

Methods

We retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the "one-class logistic regression (OCLR)" algorithm to obtain the mRNAsi of patients with HCC. We performed "unsupervised consensus clustering" to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient's prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC.

Results

We screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient's prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines.

Conclusion

The novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.

The causal impact of complement C3d receptor 2 on head and neck cancer microenvironment and its implications for immunotherapy response prediction

This research dives into the intricate immune landscape of head and neck cancer (HNC), with a keen focus on the roles of specific immune cell subpopulations and their linked genes. We used tumour RNA-seq (in-house cohort: n = 192, TCGA-HNSC: n = 546) and Mendelian randomization to pinpoint key SNPs in immune cells that have a causal connection to HNC. Our discoveries unveil a spectrum of tumour immune phenotypes that either offer protection against or increase the risk of HNC. We underscore the therapeutic promise of Complement C3d Receptor 2 (CR2), a gene closely tied to immune cells, with its increased expression in tumour tissues linked to a more favourable prognosis. This is correlated with heightened immune pathway activity, stronger resistance to radiochemotherapy, and improved immunotherapy responses. Our research emphasises the pivotal role of CR2 in immune regulation and the significance of immune cells in tumour progression, highlighting the potential of CR2-targeted therapeutic interventions.

Type III interferon inhibits bladder cancer progression by reprogramming macrophage-mediated phagocytosis and orchestrating effective immune responses

BACKGROUND

Interferons (IFNs) are essential for activating an effective immune response and play a central role in immunotherapy-mediated immune cell reactivation for tumor regression. Type III IFN (λ), related to type I IFN (α), plays a crucial role in infections, autoimmunity, and cancer. However, the direct effects of IFN-λ on the tumor immune microenvironment have not been thoroughly investigated.

METHODS

We used mouse MB49 bladder tumor models, constructed a retroviral vector expressing mouse IFN-λ3, and transduced tumor cells to evaluate the antitumor action of IFN-λ3 in immune-proficient tumors and T cell-deficient tumors. Furthermore, human bladder cancer samples (cohort 1, n=15) were used for immunohistochemistry and multiplex immunoflurescence analysis to assess the expression pattern of IFN-λ3 in human bladder cancer and correlate it with immune cells' infiltration. Immunohistochemistry analysis was performed in neoadjuvant immunotherapy cohort (cohort 2, n=20) to assess the correlation between IFN-λ3 expression and the pathological complete response rate.

RESULTS

In immune-proficient tumors, ectopic Ifnl3 expression in tumor cells significantly increased the infiltration of cytotoxic CD8+ T cells, Th1 cells, natural killer cells, proinflammatory macrophages, and dendritic cells, but reduced neutrophil infiltration. Transcriptomic analyses revealed significant upregulation of many genes associated with effective immune response, including lymphocyte recruitment, activation, and phagocytosis, consistent with increased antitumor immune infiltrates and tumor inhibition. Furthermore, IFN-λ3 activity sensitized immune-proficient tumors to anti-PD-1/PD-L1 blockade. In T cell-deficient tumors, increased Ly6G-Ly6C+I-A/I-E+ macrophages still enhanced tumor cell phagocytosis in Ifnl3 overexpressing tumors. IFN-λ3 is expressed by tumor and stromal cells in human bladder cancer, and high IFN-λ3 expression was positively associated with effector immune infiltrates and the efficacy of immune checkpoint blockade therapy.

CONCLUSIONS

Our study indicated that IFN-λ3 enables macrophage-mediated phagocytosis and antitumor immune responses and suggests a rationale for using Type III IFN as a predictive biomarker and potential immunotherapeutic candidate for bladder cancer.

The cell death-related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the prevailing subtype of hepatocellular malignancy. While previous investigations have evidenced a robust link with programmed cell death (PCD) and tumorigenesis, a comprehensive inquiry targeting the relationship between multiple PCDs and HCC remains scant. Our aim was to develop a predictive model for different PCD patterns in order to investigate their impact on survival rates, prognosis and drug response rates in HCC patients. We performed functional annotation and pathway analysis on identified PCD-related genes (PCDRGs) using multiple bioinformatics tools. The prognostic value of these PCDRGs was verified through a dataset obtained from GEO. Consensus clustering analysis was utilized to elucidate the correlation between diverse PCD clusters and pertinent clinical characteristics. To comprehensively uncover the distinct PCD regulatory patterns, our analysis integrated gene expression profiling, immune cell infiltration and enrichment analysis. To predict survival differences in HCC patients, we established a PCD model. To enhance the clinical applicability for the model, we developed a highly accurate nomogram. To address the treatment of HCC, we identified several promising chemotherapeutic agents and novel targeted drugs. These drugs may be effective in treating HCC and could improve patient outcomes. To develop a cell death feature for HCC patients, we conducted an analysis of 12 different PCD mechanisms using eligible data obtained from public databases. Through this analysis, we were able to identify 1254 PCDRGs likely to contribute to cell death on HCC. Further analysis of 1254 PCDRGs identified 37 genes with prognostic value in HCC patients. These genes were then categorized into two PCD clusters A and B. The categorization was based on the expression patterns of the genes in the different clusters. Patients in PCD cluster B had better survival probabilities. This suggests that PCD mechanisms, as represented by the genes in cluster B, may have a protective effect against HCC progression. Furthermore, the expression of PCDRGs was significantly higher in PCD cluster A, indicating that this cluster may be more closely associated with PCD mechanisms. Furthermore, our observations indicate that patients exhibiting elevated tumour mutation burden (TMB) are at an augmented risk of mortality, in comparison to those displaying low TMB and low-risk statuses, who are more likely to experience prolonged survival. In addition, we have investigated the potential distinctions in the susceptibility of diverse risk cohorts towards emerging targeted therapies, designed for the treatment of HCC. Moreover, our investigation has shown that AZD2014, SB505124, LJI308 and OSI-207 show a greater efficacy in patients in the low-risk category. Conversely, for the high-risk group patients, PD173074, ZM447439 and CZC24832 exhibit a stronger response. Our findings suggest that the identification of risk groups and personalized treatment selection could lead to better clinical outcomes for patients with HCC. Furthermore, significant heterogeneity in clinical response to ICI therapy was observed among HCC patients with varying PCD expression patterns. This novel discovery underscores the prospective usefulness of these expression patterns as prognostic indicators for HCC patients and may aid in tailoring targeted treatment for those of distinct risk strata. Our investigation introduces a novel prognostic model for HCC that integrates diverse PCD expression patterns. This innovative model provides a novel approach for forecasting prognosis and assessing drug sensitivity in HCC patients, driving a more personalized and efficacious treatment paradigm, elevating clinical outcomes. Nonetheless, additional research endeavours are required to confirm the model's precision and assess its potential to inform clinical decision-making for HCC patients.