Integrated multi-omics identified the novel intratumor microbiome-derived subtypes and signature to predict the outcome, tumor microenvironment heterogeneity, and immunotherapy response for pancreatic cancer patients

Mast Cells and Microbiome in Health and Disease

Inter-kingdom communication between human microbiota and mast cells (MCs), as sentinels of innate immunity, is crucial in determining health and disease. This complex signaling hub involves micro-organisms and, more importantly, their metabolic products. Gut microbiota is the host's largest symbiotic ecosystem and, under physiological conditions, it plays a vital role in mediating MCs tolerogenic priming, thus ensuring immune homeostasis across organs. Conversely, intestinal dysbiosis of various etiologies promotes MC-oriented inflammation along major body axes, including gut-skin, gut-lung, gut-liver, and gut-brain. This review of international scientific literature provides a comprehensive overview of the cross-talk under investigation. This process is a key biological event involved in disease development across clinical fields, with significant prognostic and therapeutic implications for future research.

Identification of intratumoral microbiome-driven immune modulation and therapeutic implications in diffuse large B-cell lymphoma

OBJECTIVE

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma, with significant clinical heterogeneity. Recent studies suggest that the intratumoral microbiome may influence the tumor microenvironment, affecting patient prognosis and therapeutic responses. This study aims to identify microbiome-related subtypes in DLBCL and assess their impact on prognosis, immune infiltration, and therapeutic sensitivity.

METHODS

Transcriptomic and microbiome data from 48 DLBCL patients were obtained from public databases. Consensus clustering was used to classify patients into distinct microbiome-related subtypes. Functional enrichment analysis, immune infiltration assessments, and single-cell RNA sequencing were performed to explore the biological characteristics of these subtypes. Drug sensitivity predictions were made using the OncoPredict tool. Hub genes' expression and biological function were validated and inferred in cell lines and independent cohorts of DLBCL.

RESULTS

Two distinct microbiome-related subtypes were identified. Patients in Cluster 1 exhibited significantly better overall survival (P < 0.05), with higher immune infiltration of regulatory T cells and M0 macrophages compared to Cluster 2, which was associated with poorer outcomes. Functional enrichment analysis revealed that genes in Cluster 1 were involved in immune regulatory pathways, including cytokine-cytokine receptor interactions and chemokine signaling, suggesting enhanced anti-tumor immune responses. In contrast, genes in Cluster 2 were enriched in immunosuppressive pathways, contributing to a less favorable prognosis. Single-cell RNA sequencing analysis revealed significant heterogeneity in immune cell populations within the tumor microenvironment. B cells exhibited the most notable heterogeneity, as indicated by stemness and differentiation potential scoring. Intercellular communication analysis demonstrated that B cells played a key role in immune cell interactions, with significant differences observed in MIF signaling between B-cell subgroups. Pseudo-time analysis further revealed distinct differentiation trajectories of B cells, highlighting their potential heterogeneity across different immune environments. Metabolic pathway analysis showed significant differences in the average expression levels of metabolic pathways among B-cell subgroups, suggesting functional specialization. Furthermore, interaction analysis between core genes involved in B-cell differentiation and microbiome-driven differentially expressed genes identified nine common genes (GSTM5, LURAP1, LINC02802, MAB21L3, C2CD4D, MMEL1, TSPAN2, and CITED4), which were found to play critical roles in B-cell differentiation and were influenced by the intratumoral microbiome. DLBCL cell lines and clinical cohorts validated that MMEL1 and CITED4 with important biologically function in DLBCL cell survival and subtype classification.

CONCLUSIONS

This study demonstrates the prognostic significance of the intratumoral microbiome in DLBCL, identifying distinct microbiome-related subtypes that impact immune infiltration, metabolic activity, and therapeutic responses. The findings provide insights into the immune heterogeneity within the tumor microenvironment, focusing on B cells and their differentiation dynamics. These results lay the foundation for microbiome-based prognostic biomarkers and personalized treatment approaches, ultimately aiming to enhance patient outcomes in DLBCL.

A stumbling block in pancreatic cancer treatment: drug resistance signaling networks

The primary node molecules in the cell signaling network in cancer tissues are maladjusted and mutated in comparison to normal tissues, which promotes the occurrence and progression of cancer. Pancreatic cancer (PC) is a highly fatal cancer with increasing incidence and low five-year survival rates. Currently, there are several therapies that target cell signaling networks in PC. However, PC is a "cold tumor" with a unique immunosuppressive tumor microenvironment (poor effector T cell infiltration, low antigen specificity), and targeting a single gene or pathway is basically ineffective in clinical practice. Targeted matrix therapy, targeted metabolic therapy, targeted mutant gene therapy, immunosuppressive therapy, cancer vaccines, and other emerging therapies have shown great therapeutic potential, but results have been disappointing. Therefore, we summarize the identified and potential drug-resistant cell signaling networks aimed at overcoming barriers to existing PC therapies.

Pathomic and bioinformatics analysis of clinical-pathological and genomic factors for pancreatic cancer prognosis

Pancreatic cancer exhibits a high degree of malignancy with a poor prognosis, lacking effective prognostic targets. Utilizing histopathological methodologies, this study endeavors to predict the expression of pathological features in pancreatic ductal adenocarcinoma (PAAD) and investigate their underlying molecular mechanisms. Pathological images, transcriptomic, and clinical data from TCGA-PAAD were collected for survival analysis. Image segmentation using unsupervised machine learning was employed to extract features, perform clustering, and establish models. The prognostic value of pathological features and associated clinical risk factors were evaluated; the correlation between pathological features and molecular mechanisms, gene mutations, and immune infiltration was analyzed. By clustering 45 effective pathological features, we divided PAAD patients into two groups: cluster 1 and cluster 2. Significant associations with poor prognosis were found for cluster 2 in both the training group (n = 113) and validation group (n = 75) (p = 0.006), with pathological stages II-IV identified as potential synergistic risk factors (HR = 2.421, 95% CI = 1.263-4.639, p = 0.008). Subsequently, through multi-omics correlation analysis, we further revealed a close association between cluster 2 and the oxidative phosphorylation mechanism. Within the cluster 2 group, 28 oxidative phosphorylation genes exhibited reduced expression, CDKN2A gene mutations were upregulated, and there was significant downregulation of Tregs infiltration and related immune gene expression. The pathomic model constructed using machine learning serves as a valuable prognostic target for PAAD. The histopathological features cluster 2 are closely associated with the downregulation of oxidative phosphorylation levels and Tregs immune infiltration.

Comparison of multiple machine learning models for predicting prognosis of pancreatic ductal adenocarcinoma based on contrast-enhanced CT radiomics and clinical features

Objective

The aim of this study was to evaluate the prognostic potential of combining clinical features and radiomics with multiple machine learning (ML) algorithms in pancreatic ductal adenocarcinoma (PDAC).

Methods

A total of 116 patients with PDAC who met the eligibility criteria were randomly assigned to a training or validation cohort. Seven ML algorithms, including Supervised Principal Components, stepwise Cox, Random Survival Forest, CoxBoost, Least absolute shrinkage and selection operation (Lasso), Ridge, and Elastic network, were integrated into 43 algorithm combinations. Forty-three radiomics models were constructed separately using radiomics features extracted from arterial phase (AP), venous phase (VP), and combined arterial and venous phase (AP+VP) images. The concordance index (C-index) of each model was calculated. The model with the highest mean C-index was identified as the best model for calculating the radiomics score (Radscore). Univariate and multivariate Cox analyses were used to identify independent prognostic indicators and create a clinical model for prognosis prediction. The multivariable Cox regression was used to combine Radscore with clinical features to create a combined model. The efficacy of the model was evaluated using the C-index, calibration curves, and decision curve analysis (DCA).

Results

The model based on the Lasso+StepCox[both] algorithm constructed using AP+VP radiomics features showed the best predictive ability among the 114 radiomics models. The C-indices of the model in the training and validation cohorts were 0.742 and 0.722, respectively. Based on the results of the univariate and multivariate Cox regression analyses, sex, Tumor-Node-Metastasis (TNM) stage, and systemic inflammation response index were included to build the clinical model. The combined model, incorporating three clinical factors and AP+VP-Radscore, achieved the highest C-indices of 0.764 and 0.746 in the training and validation cohorts, respectively. In terms of preoperative prognosis prediction for PDAC, the calibration curve and DCA showed that the combined model had a good consistency and greatest net benefit.

Conclusion

A combined model of clinical features and AP+VP-Radscore screened using multiple ML algorithms has an excellent ability to predict the prognosis of PDAC and may provide a noninvasive and effective method for clinical decision-making.

Integrated bulk and single-cell profiling characterize sphingolipid metabolism in pancreatic cancer

BACKGROUND

Abnormal sphingolipid metabolism (SM) is closely linked to the incidence of cancers. However, the role of SM in pancreatic cancer (PC) remains unclear. This study aims to explore the significance of SM in the prognosis, immune microenvironment, and treatment of PC.

METHODS

Single-cell and bulk transcriptome data of PC were acquired via TCGA and GEO databases. SM-related genes (SMRGs) were obtained via MSigDB database. Consensus clustering was utilized to construct SM-related molecular subtypes. LASSO and Cox regression were utilized to build SM-related prognostic signature. ESTIMATE and CIBERSORT algorithms were employed to assess the tumour immune microenvironment. OncoPredict package was used to predict drug sensitivity. CCK-8, scratch, and transwell experiments were performed to analyze the function of ANKRD22 in PC cell line PANC-1 and BxPC-3.

RESULTS

A total of 153 SMRGs were acquired, of which 48 were linked to PC patients' prognosis. Two SM-related subtypes (SMRGcluster A and B) were identified in PC. SMRGcluster A had a poorer outcome and more active SM process compared to SMRGcluster B. Immune analysis revealed that SMRGcluster B had higher immune and stromal scores and CD8 + T cell abundance, while SMRGcluster A had a higher tumour purity score and M0 macrophages and activated dendritic cell abundance. PC with SMRGcluster B was more susceptible to gemcitabine, paclitaxel, and oxaliplatin. Then SM-related prognostic model (including ANLN, ANKRD22, and DKK1) was built, which had a very good predictive performance. Single-cell analysis revealed that in PC microenvironment, macrophages, epithelial cells, and endothelial cells had relatively higher SM activity. ANKRD22, DKK1, and ANLN have relatively higher expression levels in epithelial cells. Cell subpopulations with high expression of ANKRD22, DKK1, and ANLN had more active SM activity. In vitro experiments showed that ANKRD22 knockdown can inhibit the proliferation, migration, and invasion of PC cells.

CONCLUSION

This study revealed the important significance of SM in PC and identified SM-associated molecular subtypes and prognostic model, which provided novel perspectives on the stratification, prognostic prediction, and precision treatment of PC patients.

Identification of basement membrane-related prognostic model associated with the immune microenvironment and synthetic therapy response in pancreatic cancer: integrated bioinformatics analysis and clinical validation

Pancreatic cancer (PC) is a common and highly malignant tumor. Basement membrane (BM) is formed by the crosslinking of extracellular matrix macromolecules and acts as a barrier against tumor cell metastasis. However, the role of BM in PC prognosis, immune infiltration, and treatment remains unclear. This study collected transcriptome and clinical survival data of PC via TCGA, GEO, and ICGC databases. PC patients (PCs) from the First Affiliated Hospital of Dalian Medical University were obtained as the clinical validation cohort. BM-related genes (BMRGs) were acquired from GeneCards and basement membraneBASE databases. A total of 46 differential-expressed BMRGs were identified. Then the BM-related prognostic model (including DSG3, MET, and PLAU) was built and validated. PCs with a low BM-related score had a better outcome and were more likely to benefit from oxaliplatin, irinotecan, and KRAS(G12C) inhibitor-12, and immunotherapy. Immune analysis revealed that BM-related score was positively correlated with neutrophils, cancer-associated fibroblasts, and macrophages infiltration, but negatively correlated with CD8+ T cells, NK cells, and B cells infiltration. PCs from the clinical cohort further verified that BM-related model could accurately predict PCs' outcomes. DSG3, MET, and PLAU were notably up-regulated within PC tissues and linked to a poor prognosis. In vitro experiments showed that DSG3 knockdown markedly suppressed the proliferation, migration, and invasion of PC cells. Molecular docking indicated that epigallocatechin gallate had a strong binding activity with DSG3, MET, and PLAU and may be used as a potential therapeutic agent for PC. In conclusion, this study developed a BM-related model associated with PC prognosis, immune infiltration, and treatment, which provided new insights into PC stratification and drug intervention.

Regulatory T cells subgroups in the tumor microenvironment cannot be overlooked: Their involvement in prognosis and treatment strategy in melanoma

BACKGROUND

Melanoma, the most lethal form of skin cancer, presents substantial challenges despite effective surgical interventions for in situ lesions. Regulatory T cells (Tregs) wield a pivotal immunomodulatory influence within the tumor microenvironment, yet their impact on melanoma prognosis and direct molecular interactions with melanoma cells remain elusive. This investigation employs single-cell analysis to unveil the intricate nature of Tregs in human melanoma.

METHODS

Single-cell RNA and bulk sequencing data, alongside clinical information, were obtained from public repositories. Initially, GO and GSEA analyses were employed to delineate functional disparities among distinct cell subsets. Pseudotime and cell-cell interconnection analyses were conducted, followed by an endeavor to construct a prognostic model grounded in Treg-associated risk scores. This model's efficacy was demonstrated via PCA and K-M analyses, with multivariate Cox regression affirming its independent prognostic value in melanoma patients. Furthermore, immune infiltration analysis, immune checkpoint gene expression scrutiny, and drug sensitivity assessments were performed to ascertain the clinical relevance of this prognostic model.

RESULTS

Following batch effect correction, 80 025 cells partitioned into 31 clusters, encompassing B cells, plasma cells, endothelial cells, fibroblasts, melanoma cells, monocytes, macrophages, and T_NK cells. Within these, 4240 CD4+ T cells were subclassified into seven distinct types. Functional analysis underscored the immunomodulatory function of Tregs within the melanoma tumor microenvironment, elucidating disparities among Treg subpopulations. Notably, the ITGB2 signaling pathway emerged as a plausible molecular nexus linking Tregs to melanoma cells. Our prognostic signature exhibited robust predictive capacities for melanoma prognosis and potential implications in evaluating immunotherapy response.

CONCLUSION

Tregs exert a critical role in immune suppression within the melanoma tumor microenvironment, revealing a potential molecular-level association with melanoma cells. Our innovative Treg-centered signature introduces a promising prognostic marker for melanoma, holding potential for future clinical prognostic assessments.

CD8+ T cell exhaustion and its regulatory mechanisms in the tumor microenvironment: key to the success of immunotherapy

A steady dysfunctional state caused by chronic antigen stimulation in the tumor microenvironment (TME) is known as CD8+ T cell exhaustion. Exhausted-like CD8+ T cells (CD8+ Tex) displayed decreased effector and proliferative capabilities, elevated co-inhibitory receptor generation, decreased cytotoxicity, and changes in metabolism and transcription. TME induces T cell exhaustion through long-term antigen stimulation, upregulation of immune checkpoints, recruitment of immunosuppressive cells, and secretion of immunosuppressive cytokines. CD8+ Tex may be both the reflection of cancer progression and the reason for poor cancer control. The successful outcome of the current cancer immunotherapies, which include immune checkpoint blockade and adoptive cell treatment, depends on CD8+ Tex. In this review, we are interested in the intercellular signaling network of immune cells interacting with CD8+ Tex. These findings provide a unique and detailed perspective, which is helpful in changing this completely unpopular state of hypofunction and intensifying the effect of immunotherapy.

Comprehensive pan-cancer analysis reveals EPHB2 is a novel predictive biomarker for prognosis and immunotherapy response

PURPOSE

Recent studies have increasingly linked Ephrin receptor B2 (EPHB2) to cancer progression. However, comprehensive investigations into the immunological roles and prognostic significance of EPHB2 across various cancers remain lacking.

METHODS

We employed various databases and bioinformatics tools to investigate the impact of EPHB2 on prognosis, immune infiltration, genome instability, and response to immunotherapy. Validation of the correlation between EPHB2 expression and M2 macrophages included analyses using bulk and single-cell transcriptomic datasets, spatial transcriptomics, and multi-fluorescence staining. Moreover, we performed cMap web tool to screen for EPHB2-targeted compounds and assessed their potential through molecular docking and dynamics simulations. Additionally, in vitro validation using lung adenocarcinoma (LUAD) cell lines was conducted to confirm the bioinformatics predictions about EPHB2.

RESULTS

EPHB2 dysregulation was observed across multiple cancer types, where it demonstrated significant diagnostic and prognostic value. Gene Set Enrichment Analysis (GSEA) indicated that EPHB2 is involved in enhancing cellular proliferation, invasiveness of cancer cells, and modulation of the anti-cancer immune response. Furthermore, it is emerged as a pan-cancer marker for M2 macrophage infiltration, supported by integrated analyses of transcriptomics and multiple fluorescence staining. In LUAD cells, knockdown of EPHB2 expression led to a decrease in both cell proliferation and migratory activity.

CONCLUSION

EPHB2 expression may serve as a pivotal indicator of M2 macrophage infiltration, offering vital insights into tumor dynamics and progression across various cancers, including lung adenocarcinoma, highlighting its significant prognostic and therapeutic potential for further exploration.

Intratumoral Microbiota: Metabolic Influences and Biomarker Potential in Gastrointestinal Cancer

Gastrointestinal (GI) cancers impose a substantial global health burden, highlighting the necessity for deeper understanding of their intricate pathogenesis and treatment strategies. This review explores the interplay between intratumoral microbiota, tumor metabolism, and major types of GI cancers (including esophageal, gastric, liver, pancreatic, and colorectal cancers), summarizing recent studies and elucidating their clinical implications and future directions. Recent research revealed altered microbial signatures within GI tumors, impacting tumor progression, immune responses, and treatment outcomes. Dysbiosis-induced alterations in tumor metabolism, including glycolysis, fatty acid metabolism, and amino acid metabolism, play critical roles in cancer progression and therapeutic resistance. The integration of molecular mechanisms and potential biomarkers into this understanding further enhances the prognostic significance of intratumoral microbiota composition and therapeutic opportunities targeting microbiota-mediated tumor metabolism. Despite advancements, challenges remain in understanding the dynamic interactions within the tumor microenvironment (TME). Future research directions, including advanced omics technologies and prospective clinical studies, offer promising avenues for precision oncology and personalized treatment interventions in GI cancer. Overall, integrating microbiota-based approaches and molecular biomarkers into GI cancer management holds promise for improving patient outcomes and survival.

Comprehensive analysis of PPP4C's impact on prognosis, immune microenvironment, and immunotherapy response in lung adenocarcinoma using single-cell sequencing and multi-omics

Background

Elevated PPP4C expression has been associated with poor prognostic implications for patients suffering from lung adenocarcinoma (LUAD). The extent to which PPP4C affects immune cell infiltration in LUAD, as well as the importance of associated genes in clinical scenarios, still requires thorough investigation.

Methods

In our investigation, we leveraged both single-cell and comprehensive RNA sequencing data, sourced from LUAD patients, in our analysis. This study also integrated datasets of immune-related genes from InnateDB into the framework. Our expansive evaluation employed various analytical techniques; these included pinpointing differentially expressed genes, constructing WGCNA, implementing Cox proportional hazards models. We utilized these methods to investigate the gene expression profiles of PPP4C within the context of LUAD and to clarify its potential prognostic value for patients. Subsequent steps involved validating the observed enhancement of PPP4C expression in LUAD samples through a series of experimental approaches. The array comprised immunohistochemistry staining, Western blotting, quantitative PCR, and a collection of cell-based assays aimed at evaluating the influence of PPP4C on the proliferative and migratory activities of LUAD cells.

Results

In lung cancer, elevated expression levels of PPP4C were observed, correlating with poorer patient prognoses. Validation of increased PPP4C levels in LUAD specimens was achieved using immunohistochemical techniques. Experimental investigations have substantiated the role of PPP4C in facilitating cellular proliferation and migration in LUAD contexts. Furthermore, an association was identified between the expression of PPP4C and the infiltration of immune cells in these tumors. A prognostic framework, incorporating PPP4C and immune-related genes, was developed and recognized as an autonomous predictor of survival in individuals afflicted with LUAD. This prognostic tool has demonstrated considerable efficacy in forecasting patient survival and their response to immunotherapeutic interventions.

Conclusion

The involvement of PPP4C in LUAD is deeply intertwined with the tumor's immune microenvironment. PPP4C's over-expression is associated with negative clinical outcomes, promoting both tumor proliferation and spread. A prognostic framework based on PPP4C levels may effectively predict patient prognoses in LUAD, as well as the efficacy of immunotherapy strategy. This research sheds light on the mechanisms of immune interaction in LUAD and proposes a new strategy for treatment.

Multi-omic validation of the cuproptosis-sphingolipid metabolism network: modulating the immune landscape in osteosarcoma

Background

The current understanding of the mechanisms by which metal ion metabolism promotes the progression and drug resistance of osteosarcoma remains incomplete. This study aims to elucidate the key roles and mechanisms of genes involved in cuproptosis-related sphingolipid metabolism (cuproptosis-SPGs) in regulating the immune landscape, tumor metastasis, and drug resistance in osteosarcoma cells.

Methods

This study employed multi-omics approaches to assess the impact of cuproptosis-SPGs on the prognosis of osteosarcoma patients. Lasso regression analysis was utilized to construct a prognostic model, while multivariate regression analysis was applied to identify key core genes and generate risk coefficients for these genes, thereby calculating a risk score for each osteosarcoma patient. Patients were then stratified into high-risk and low-risk groups based on their risk scores. The ESTIMATE and CIBERSORT algorithms were used to analyze the level of immune cell infiltration within these risk groups to construct the immune landscape. Single-cell analysis was conducted to provide a more precise depiction of the expression patterns of cuproptosis-SPGs among immune cell subtypes. Finally, experiments on osteosarcoma cells were performed to validate the role of the cuproptosis-sphingolipid signaling network in regulating cell migration and apoptosis.

Results

In this study, seven cuproptosis-SPGs were identified and used to construct a prognostic model for osteosarcoma patients. In addition to predicting survival, the model also demonstrated reliability in forecasting the response to chemotherapy drugs. The results showed that a high cuproptosis-sphingolipid metabolism score was closely associated with reduced CD8 T cell infiltration and indicated poor prognosis in osteosarcoma patients. Cellular functional assays revealed that cuproptosis-SPGs regulated the LC3B/ERK signaling pathway, thereby triggering cell death and impairing migration capabilities in osteosarcoma cells.

Conclusion

The impact of cuproptosis-related sphingolipid metabolism on the survival and migration of osteosarcoma cells, as well as on CD8 T cell infiltration, highlights the potential of targeting copper ion metabolism as a promising strategy for osteosarcoma patients.

A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification

Objective

This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors.

Methods

242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients' tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model.

Results

The performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96-0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86-0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility.

Conclusion

The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.

Anoikis resistance regulates immune infiltration and drug sensitivity in clear-cell renal cell carcinoma: insights from multi omics, single cell analysis and in vitro experiment

Background

Anoikis is a form of programmed cell death essential for preventing cancer metastasis. In some solid cancer, anoikis resistance can facilitate tumor progression. However, this phenomenon is underexplored in clear-cell renal cell carcinoma (ccRCC).

Methods

Using SVM machine learning, we identified core anoikis-related genes (ARGs) from ccRCC patient transcriptomic data. A LASSO Cox regression model stratified patients into risk groups, informing a prognostic model. GSVA and ssGSEA assessed immune infiltration, and single-cell analysis examined ARG expression across immune cells. Quantitative PCR and immunohistochemistry validated ARG expression differences between immune therapy responders and non-responders in ccRCC.

Results

ARGs such as CCND1, CDKN3, PLK1, and BID were key in predicting ccRCC outcomes, linking higher risk with increased Treg infiltration and reduced M1 macrophage presence, indicating an immunosuppressive environment facilitated by anoikis resistance. Single-cell insights showed ARG enrichment in Tregs and dendritic cells, affecting immune checkpoints. Immunohistochemical analysis reveals that ARGs protein expression is markedly elevated in ccRCC tissues responsive to immunotherapy.

Conclusion

This study establishes a novel anoikis resistance gene signature that predicts survival and immunotherapy response in ccRCC, suggesting that manipulating the immune environment through these ARGs could improve therapeutic strategies and prognostication in ccRCC.

Multi-omics analysis and experimental validation of the value of monocyte-associated features in prostate cancer prognosis and immunotherapy

Background

Monocytes play a critical role in tumor initiation and progression, with their impact on prostate adenocarcinoma (PRAD) not yet fully understood. This study aimed to identify key monocyte-related genes and elucidate their mechanisms in PRAD.

Method

Utilizing the TCGA-PRAD dataset, immune cell infiltration levels were assessed using CIBERSORT, and their correlation with patient prognosis was analyzed. The WGCNA method pinpointed 14 crucial monocyte-related genes. A diagnostic model focused on monocytes was developed using a combination of machine learning algorithms, while a prognostic model was created using the LASSO algorithm, both of which were validated. Random forest and gradient boosting machine singled out CCNA2 as the most significant gene related to prognosis in monocytes, with its function further investigated through gene enrichment analysis. Mendelian randomization analysis of the association of HLA-DR high-expressing monocytes with PRAD. Molecular docking was employed to assess the binding affinity of CCNA2 with targeted drugs for PRAD, and experimental validation confirmed the expression and prognostic value of CCNA2 in PRAD.

Result

Based on the identification of 14 monocyte-related genes by WGCNA, we developed a diagnostic model for PRAD using a combination of multiple machine learning algorithms. Additionally, we constructed a prognostic model using the LASSO algorithm, both of which demonstrated excellent predictive capabilities. Analysis with random forest and gradient boosting machine algorithms further supported the potential prognostic value of CCNA2 in PRAD. Gene enrichment analysis revealed the association of CCNA2 with the regulation of cell cycle and cellular senescence in PRAD. Mendelian randomization analysis confirmed that monocytes expressing high levels of HLA-DR may promote PRAD. Molecular docking results suggested a strong affinity of CCNA2 for drugs targeting PRAD. Furthermore, immunohistochemistry experiments validated the upregulation of CCNA2 expression in PRAD and its correlation with patient prognosis.

Conclusion

Our findings offer new insights into monocyte heterogeneity and its role in PRAD. Furthermore, CCNA2 holds potential as a novel targeted drug for PRAD.

Identification and validation of a costimulatory molecule-related signature to predict the prognosis for uveal melanoma patients

Uveal melanoma (UVM) is the most common primary tumor in adult human eyes. Costimulatory molecules (CMs) are important in maintaining T cell biological functions and regulating immune responses. To investigate the role of CMs in UVM and exploit prognostic signature by bioinformatics analysis. This study aimed to identify and validate a CMs associated signature and investigate its role in the progression and prognosis of UVM. The expression profile data of training cohort and validation cohort were downloaded from The Cancer Genome Atlas (TCGA) dataset and the Gene Expression Omnibus (GEO) dataset. 60 CM genes were identified, and 34 genes were associated with prognosis by univariate Cox regression. A prognostic signature was established with six CM genes. Further, high- and low-risk groups were divided by the median, and Kaplan-Meier (K-M) curves indicated that high-risk patients presented a poorer prognosis. We analyzed the correlation of gender, age, stage, and risk score on prognosis by univariate and multivariate regression analysis. We found that risk score was the only risk factor for prognosis. Through the integration of the tumor immune microenvironment (TIME), it was found that the high-risk group presented more immune cell infiltration and expression of immune checkpoints and obtained higher immune scores. Enrichment analysis of the biological functions of the two groups revealed that the differential parts were mainly related to cell-cell adhesion, regulation of T-cell activation, and cytokine-cytokine receptor interaction. No differences in tumor mutation burden (TMB) were found between the two groups. GNA11 and BAP1 have higher mutation frequencies in high-risk patients. Finally, based on the Genomics of Drug Sensitivity in Cancer 2 (GDSC2) dataset, drug sensitivity analysis found that high-risk patients may be potential beneficiaries of the treatment of crizotinib or temozolomide. Taken together, our CM-related prognostic signature is a reliable biomarker that may provide ideas for future treatments for the disease.

Unraveling the intricacies of glioblastoma progression and recurrence: insights into the role of NFYB and oxidative phosphorylation at the single-cell level

Background

Glioblastoma (GBM), with its high recurrence and mortality rates, makes it the deadliest neurological malignancy. Oxidative phosphorylation is a highly active cellular pathway in GBM, and NFYB is a tumor-associated transcription factor. Both are related to mitochondrial function, but studies on their relationship with GBM at the single-cell level are still scarce.

Methods

We re-analyzed the single-cell profiles of GBM from patients with different subtypes by single-cell transcriptomic analysis and further subdivided the large population of Glioma cells into different subpopulations, explored the interrelationships and active pathways among cell stages and clinical subtypes of the populations, and investigated the relationship between the transcription factor NFYB of the key subpopulations and GBM, searching for the prognostic genes of GBM related to NFYB, and verified by experiments.

Results

Glioma cells and their C5 subpopulation had the highest percentage of G2M staging and rGBM, which we hypothesized might be related to the higher dividing and proliferating ability of both Glioma and C5 subpopulations. Oxidative phosphorylation pathway activity is elevated in both the Glioma and C5 subgroup, and NFYB is a key transcription factor for the C5 subgroup, suggesting its possible involvement in GBM proliferation and recurrence, and its close association with mitochondrial function. We also identified 13 prognostic genes associated with NFYB, of which MEM60 may cause GBM patients to have a poor prognosis by promoting GBM proliferation and drug resistance. Knockdown of the NFYB was found to contribute to the inhibition of proliferation, invasion, and migration of GBM cells.

Conclusion

These findings help to elucidate the key mechanisms of mitochondrial function in GBM progression and recurrence, and to establish a new prognostic model and therapeutic target based on NFYB.

Reprogramming of regulatory T cells in inflammatory tumor microenvironment: can it become immunotherapy turning point?

Overcoming the immunosuppressive tumor microenvironment and identifying widely used immunosuppressants with minimal side effects are two major challenges currently hampering cancer immunotherapy. Regulatory T cells (Tregs) are present in almost all cancer tissues and play an important role in preserving autoimmune tolerance and tissue homeostasis. The tumor inflammatory microenvironment causes the reprogramming of Tregs, resulting in the conversion of Tregs to immunosuppressive phenotypes. This process ultimately facilitates tumor immune escape or tumor progression. However, current systemic Treg depletion therapies may lead to severe autoimmune toxicity. Therefore, it is crucial to understand the mechanism of Treg reprogramming and develop immunotherapies that selectively target Tregs within tumors. This article provides a comprehensive review of the potential mechanisms involved in Treg cell reprogramming and explores the application of Treg cell immunotherapy. The interference with reprogramming pathways has shown promise in reducing the number of tumor-associated Tregs or impairing their function during immunotherapy, thereby improving anti-tumor immune responses. Furthermore, a deeper understanding of the mechanisms that drive Treg cell reprogramming could reveal new molecular targets for future treatments.

Gut microbiome: decision-makers in the microenvironment of colorectal cancer

Colorectal cancer (CRC) is a common malignancy of the gastrointestinal tract, accounting for the second most common cause of gastrointestinal tumors. As one of the intestinal barriers, gut bacteria form biofilm, participate in intestinal work, and form the living environment of intestinal cells. Metagenomic next-generation sequencing (mNGS) of the gut bacteria in a large number of CRC patients has been established, enabling specific microbial signatures to be associated with colorectal adenomato-carcinoma. Gut bacteria are involved in both benign precursor lesions (polyps), in situ growth and metastasis of CRC. Therefore, the term tumorigenic bacteria was proposed in 2018, such as Escherichia coli, Fusobacterium nucleatum, enterotoxigenic Bacteroides fragilis, etc. Meanwhile, bacteria toxins (such as cytolethal distending toxin (CDT), Colibactin (Clb), B. fragilis toxin) affect the tumor microenvironment and promote cancer occurrence and tumor immune escape. It is important to note that there are differences in the bacteria of different types of CRC. In this paper, the role of tumorigenic bacteria in the polyp-cancer transformation and the effects of their secreted toxins on the tumor microenvironment will be discussed, thereby further exploring new ideas for the prevention and treatment of CRC.

Identification of the methotrexate resistance-related diagnostic markers in osteosarcoma via adaptive total variation netNMF and multi-omics datasets

Osteosarcoma is one of the most common malignant bone tumors with high chemoresistance and poor prognosis, exhibiting abnormal gene regulation and epigenetic events. Methotrexate (MTX) is often used as a primary agent in neoadjuvant chemotherapy for osteosarcoma; However, the high dosage of methotrexate and strong drug resistance limit its therapeutic efficacy and application prospects. Studies have shown that abnormal expression and dysfunction of some coding or non-coding RNAs (e.g., DNA methylation and microRNA) affect key features of osteosarcoma progression, such as proliferation, migration, invasion, and drug resistance. Comprehensive multi-omics analysis is critical to understand its chemoresistant and pathogenic mechanisms. Currently, the network analysis-based non-negative matrix factorization (netNMF) method is widely used for multi-omics data fusion analysis. However, the effects of data noise and inflexible settings of regularization parameters affect its performance, while integrating and processing different types of genetic data is also a challenge. In this study, we introduced a novel adaptive total variation netNMF (ATV-netNMF) method to identify feature modules and characteristic genes by integrating methylation and gene expression data, which can adaptively choose an anisotropic smoothing scheme to denoise or preserve feature details based on the gradient information of the data by introducing an adaptive total variation constraint in netNMF. By comparing with other similar methods, the results showed that the proposed method could extract multi-omics fusion features more effectively. Furthermore, by combining the mRNA and miRNA data of methotrexate (MTX) resistance with the extracted feature genes, four genes, Carboxypeptidase E (CPE), LIM, SH3 protein 1 (LASP1), Pyruvate Dehydrogenase Kinase 1 (PDK1) and Serine beta-lactamase-like protein (LACTB) were finally identified. The results showed that the gene signature could reliably predict the prognostic status and immune status of osteosarcoma patients.