Bridging Chronic Inflammation and Digestive Cancer: The Critical Role of Innate Lymphoid Cells in Tumor Microenvironments.

The incidence and mortality of digestive system-related cancers have always been high and attributed to the heterogeneity and complexity of the immune microenvironment of the digestive system. Furthermore, several studies have shown that chronic inflammation in the digestive system is responsible for cancer incidence; therefore, controlling inflammation is a potential strategy to stop the development of cancer. Innate Lymphoid Cells (ILC) represent a heterogeneous group of lymphocytes that exist in contrast to T cells. They function by interacting with cytokines and immune cells in an antigen-independent manner. In the digestive system cancer, from the inflammatory phase to the development, migration, and metastasis of tumors, ILC have been found to interact with the immune microenvironment and either control or promote these processes. The conventional treatments for digestive tumors have limited efficacy, therefore, ILC-associated immunotherapies are promising strategies. This study reviews the characterization of different ILC subpopulations, how they interact with and influence the immune microenvironment as well as chronic inflammation, and their promotional or inhibitory role in four common digestive system tumors, including pancreatic, colorectal, gastric, and hepatocellular cancers. In particular, the review emphasizes the role of ILC in associating chronic inflammation with cancer and the potential for enhanced immunotherapy with cytokine therapy and adoptive immune cell therapy.

Utilizing machine learning algorithms for predicting risk factors for bone metastasis from right-sided colon carcinoma after complete mesocolic excision: a 10-year retrospective multicenter study.

BACKGROUND: Bone metastasis (BM) occurs when colon cancer cells disseminate from the primary tumor site to the skeletal system via the bloodstream or lymphatic system. The emergence of such bone metastases typically heralds a significantly poor prognosis for the patient. This study's primary aim is to develop a machine learning model to identify patients at elevated risk of bone metastasis among those with right-sided colon cancer undergoing complete mesocolonectomy (CME). PATIENTS AND METHODS: The study cohort comprised 1,151 individuals diagnosed with right-sided colon cancer, with a subset of 73 patients presenting with bone metastases originating from the colon. We used univariate and multivariate regression analyses as well as four machine learning algorithms to screen variables for 38 characteristic variables such as patient demographic characteristics and surgical information. The study employed four distinct machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), to develop the predictive model. Additionally, the model was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), while Shapley additive explanation (SHAP) was utilized to visualize and analyze the model. RESULTS: The XGBoost algorithm performed the best performance among the four prediction models. In the training set, the XGBoost algorithm had an area under curve (AUC) value of 0.973 (0.953-0.994), an accuracy of 0.925 (0.913-0.936), a sensitivity of 0.921 (0.902-0.940), and a specificity of 0.908 (0.894-0.922). In the validation set, the XGBoost algorithm had an AUC value of 0.922 (0.833-0.995), an accuracy of 0.908 (0.889-0.926), a sensitivity of 0.924 (0.873-0.975), and a specificity of 0.883 (0.810-0.956). Furthermore, the AUC value of 0.83 for the external validation set suggests that the XGBoost prediction model possesses strong extrapolation capabilities. The results of SHAP analysis identified alkaline phosphatase (ALP) levels, tumor size, invasion depth, lymph node metastasis, lung metastasis, and postoperative neutrophil-to-lymphocyte ratio (NLR) levels as significant risk factors for BM from right-sided colon cancer subsequent to CME. CONCLUSION: The prediction model for BM from right-sided colon cancer developed using the XGBoost machine learning algorithm in this study is both highly precise and clinically valuable.

Exploring the prognostic value of BRMS1 + microglia based on single-cell anoikis regulator patterns in the immunologic microenvironment of GBM.

BACKGROUND: Anoikis is a specialized form of programmed cell death induced by the loss of cell adhesion to the extracellular matrix (ECM). Acquisition of anoikis resistance is a significant marker for cancer cell invasion, metastasis, therapy resistance, and recurrence. Although current research has identified multiple factors that regulate anoikis resistance, the pathological mechanisms of anoikis-mediated tumor microenvironment (TME) in glioblastoma (GBM) remain largely unexplored. METHODS: Utilizing single-cell RNA sequencing (scRNA-seq) data and employing non-negative matrix factorization (NMF), we identified and characterized TME cell clusters with distinct anoikis-associated gene signatures. Prognostic and therapeutic response analyses were conducted using TCGA and CGGA datasets to assess the clinical significance of different TME cell clusters. The spatial relationship between BRMS1 + microglia and tumor cells was inferred from spatial transcriptome RNA sequencing (stRNA-seq) data. To simulate the tumor immune microenvironment, co-culture experiments were performed with microglia (HMC3) and GBM cells (U118/U251), and microglia were transfected with a BRMS1 overexpression lentivirus. Western blot or ELISA were used to detect BRMS1, M2 macrophage-specific markers, PI3K/AKT signaling proteins, and apoptosis-related proteins. The proliferation and apoptosis capabilities of tumor cells were evaluated using CCK-8, colony formation, and apoptosis assays, while the invasive and migratory abilities of tumor cells were assessed using Transwell assays. RESULTS: NMF-based analysis successfully identified CD8 + T cell and microglia cell clusters with distinct gene signature characteristics. Trajectory analysis, cell communication, and gene regulatory network analyses collectively indicated that anoikis-mediated TME cell clusters can influence tumor cell development through various mechanisms. Notably, BRMS1 + AP-Mic exhibited an M2 macrophage phenotype and had significant cell communication with malignant cells. Moreover, high expression of BRMS1 + AP-Mic in TCGA and CGGA datasets was associated with poorer survival outcomes, indicating its detrimental impact on immunotherapy. Upregulation of BRMS1 in microglia may lead to M2 macrophage polarization, activate the PI3K/AKT signaling pathway through SPP1/CD44-mediated cell interactions, inhibit tumor cell apoptosis, and promote tumor proliferation and invasion. CONCLUSION: This pioneering study used NMF-based analysis to reveal the important predictive value of anoikis-regulated TME in GBM for prognosis and immunotherapeutic response. BRMS1 + microglial cells provide a new perspective for a deeper understanding of the immunosuppressive microenvironment of GBM and could serve as a potential therapeutic target in the future.

Leveraging single-cell sequencing analysis and bulk-RNA sequencing analysis to forecast necroptosis in cutaneous melanoma prognosis.

Cutaneous melanoma, a malignancy of melanocytes, presents a significant challenge due to its aggressive nature and rising global incidence. Despite advancements in treatment, the variability in patient responses underscores the need for further research into novel therapeutic targets, including the role of programmed cell death pathways such as necroptosis. The melanoma datasets used for analysis, GSE215120, GSE19234, GSE22153 and GSE65904, were downloaded from the GEO database. The melanoma data from TCGA were downloaded from the UCSC website. Using single-cell sequencing, we assess the heterogeneity of necroptosis in cutaneous melanoma, identifying distinct cell clusters and necroptosis-related gene expression patterns. A combination of 101 machine learning algorithms was employed to construct a necroptosis-related signature (NRS) based on key genes associated with necroptosis. The prognostic value of NRS was evaluated in four cohorts (one TCGA and three GEO cohorts), and the tumour microenvironment (TME) was analysed to understand the relationship between necroptosis, tumour mutation burden (TMB) and immune infiltration. Finally, we focused on the role of key target TSPAN10 in the prognosis, pathogenesis, immunotherapy relevance and drug sensitivity of cutaneous melanoma. Our study revealed significant heterogeneity in necroptosis among melanoma cells, with a higher prevalence in epithelial cells, myeloid cells and fibroblasts. The NRS, developed through rigorous machine learning techniques, demonstrated robust prognostic capabilities, distinguishing high-risk patients with poorer outcomes in all cohorts. Analysis of the TME showed that high NRS scores correlated with lower TMB and reduced immune cell infiltration, indicating a potential mechanism through which necroptosis influences melanoma progression. Finally, TSPAN10 has been identified as a key target for cutaneous melanoma and is highly associated with poor prognosis. The findings highlight the complex role of necroptosis in cutaneous melanoma and introduce the NRS as a novel prognostic tool with potential to guide therapeutic decisions.

Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.

This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE22153, and GSE65904 cohorts from GEO database were processed and harmonized to mitigate batch effects. Lactate metabolism scores were assigned to individual cells using the 'AUCell' package. Weighted Co-expression Network Analysis (WGCNA) was employed to identify gene modules correlated with lactate metabolism. Machine learning algorithms were applied to construct a prognostic model, and its performance was evaluated in multiple cohorts. Immune correlation, mutation analysis, and enrichment analysis were conducted to further characterize the prognostic model's biological implications. Finally, the function of key gene NDUFS7 was verified by cell experiments. Machine learning resulted in an optimal prognostic model, demonstrating significant prognostic value across various cohorts. In the different cohorts, the high-risk group showed a poor prognosis. Immune analysis indicated differences in immune cell infiltration and checkpoint gene expression between risk groups. Mutation analysis identified genes with high mutation loads in SKCM. Enrichment analysis unveiled enriched pathways and biological processes in high-risk SKCM patients. NDUFS7 was found to be a hub gene in the protein-protein interaction network. After the expression of NDUFS7 was reduced by siRNA knockdown, CCK-8, colony formation, transwell and wound healing tests showed that the activity, proliferation and migration of A375 and WM115 cell lines were significantly decreased. This study offers insights into the prognostic significance of lactate metabolism-related genes in SKCM.

Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data.

BACKGROUND: Colorectal cancer is a malignant tumor of the digestive system originating from abnormal cell proliferation in the colon or rectum, often leading to gastrointestinal symptoms and severe health issues. Nucleotide metabolism, which encompasses the synthesis of DNA and RNA, is a pivotal cellular biochemical process that significantly impacts both the progression and therapeutic strategies of colorectal cancer METHODS: For single-cell RNA sequencing (scRNA-seq), five functions were employed to calculate scores related to nucleotide metabolism. Cell developmental trajectory analysis and intercellular interaction analysis were utilized to explore the metabolic characteristics and communication patterns of different epithelial cells. These findings were further validated using spatial transcriptome RNA sequencing (stRNA-seq). A risk model was constructed using expression profile data from TCGA and GEO cohorts to optimize clinical decision-making. Key nucleotide metabolism-related genes (NMRGs) were functionally validated by further in vitro experiments. RESULTS: In both scRNA-seq and stRNA-seq, colorectal cancer (CRC) exhibited unique cellular heterogeneity, with myeloid cells and epithelial cells in tumor samples displaying higher nucleotide metabolism scores. Analysis of intercellular communication revealed enhanced signaling pathways and ligand-receptor interactions between epithelial cells with high nucleotide metabolism and fibroblasts. Spatial transcriptome sequencing confirmed elevated nucleotide metabolism states in the core region of tumor tissue. After identifying differentially expressed NMRGs in epithelial cells, a risk prognostic model based on four genes effectively predicted overall survival and immunotherapy outcomes in patients. High-risk group patients exhibited an immunosuppressive microenvironment and relatively poorer prognosis and responses to chemotherapy and immunotherapy. Finally, based on data analysis and a series of cellular functional experiments, ACOX1 and CPT2 were identified as novel therapeutic targets for CRC. CONCLUSION: In this study, a comprehensive analysis of NMRGs in CRC was conducted using a combination of single-cell sequencing, spatial transcriptome sequencing, and high-throughput data. The prognostic model constructed with NMRGs shows potential as a standalone prognostic marker for colorectal cancer patients and may significantly influence the development of personalized treatment approaches for CRC.

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms.

Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.

Physical modulation of mesenchymal stem cell exosomes: A new perspective for regenerative medicine.

Mesenchymal stem cell-derived exosomes (MSC-Exo) offer promising therapeutic potential for various refractory diseases, presenting a novel therapeutic strategy. However, their clinical application encounters several obstacles, including low natural secretion, uncontrolled biological functions and inherent heterogeneity. On the one hand, physical stimuli can mimic the microenvironment dynamics where MSC-Exo reside. These factors influence not only their secretion but also, significantly, their biological efficacy. Moreover, physical factors can also serve as techniques for engineering exosomes. Therefore, the realm of physical factors assumes a crucial role in modifying MSC-Exo, ultimately facilitating their clinical translation. This review focuses on the research progress in applying physical factors to MSC-Exo, encompassing ultrasound, electrical stimulation, light irradiation, intrinsic physical properties, ionizing radiation, magnetic field, mechanical forces and temperature. We also discuss the current status and potential of physical stimuli-affected MSC-Exo in clinical applications. Furthermore, we address the limitations of recent studies in this field. Based on this, this review provides novel insights to advance the refinement of MSC-Exo as a therapeutic approach in regenerative medicine.

Senescence-related genes analysis in breast cancer reveals the immune microenvironment and implications for immunotherapy.

Despite the advent of precision therapy for breast cancer (BRCA) treatment, some individuals are still unable to benefit from it and have poor survival prospects as a result of the disease's high heterogeneity. Cell senescence plays a crucial role in the tumorigenesis, progression, and immune regulation of cancer and has a major impact on the tumor microenvironment. To find new treatment strategies, we aimed to investigate the potential significance of cell senescence in BRCA prognosis and immunotherapy. We created a 9-gene senescence-related signature. We evaluated the predictive power and the role of signatures in the immune microenvironment and infiltration. In vitro tests were used to validate the expression and function of the distinctive critical gene ACTC1. Our risk signature allows BRCA patients to receive a Predictive Risk Signature (PRS), which may be used to further categorize a patient's response to immunotherapy. Compared to conventional clinicopathological characteristics, PRS showed strong predictive efficacy and precise survival prediction. Moreover, PRS subgroups were examined for altered pathways, mutational patterns, and possibly useful medicines. Our research offers suggestions for incorporating senescence-based molecular classification into risk assessment and ICI therapy decision-making.

PANoptosis-related signature in melanoma: Transcriptomic mapping and clinical prognostication.

Programmed cell death plays a pivotal role in maintaining tissue homeostasis, and recent advancements in cell biology have uncovered PANoptosis-a novel paradigm integrating pyroptosis, apoptosis, and necroptosis. This study investigates the implications of PANoptosis in melanoma, a formidable skin cancer known for its metastatic potential and resistance to conventional therapies. Leveraging bulk and single-cell transcriptome analyses, machine learning modeling, and immune correlation assessments, we unveil the molecular intricacies of PANoptosis in melanoma. Single-cell sequencing identifies diverse cell types involved in PANoptosis, while bulk transcriptome analysis reveals key gene sets correlated with PANoptosis. Machine learning algorithms construct a robust prognostic model, demonstrating consistent predictive power across diverse cohorts. Patients with different cohorts can be divided into high-risk and low-risk groups according to this PANoptosis score, with the high-risk group having a significantly worse prognosis. Immune correlation analyses unveil a link between PANoptosis and immunotherapy response, with potential therapeutic implications. Mutation analysis and enrichment studies provide insights into the mutational landscape associated with PANoptosis. Finally, we used cell experiments to verify the expression and function of key gene PARVA, showing that PARVA was highly expressed in melanoma cell lines, and after PARVA is knocked down, cell invasion, migration, and colony formation ability were significantly decreased. This study advances our understanding of PANoptosis in melanoma, offering a comprehensive framework for targeted therapeutic interventions and personalized medicine strategies in combating this aggressive malignancy.

Advances and future directions in keloid research: Pathogenesis, diagnosis and personalized treatment strategies.

Keloids, which are abnormal manifestations of wound healing, can result in significant functional impairment and aesthetic deformities. The pathogenesis of keloids is multifaceted and complex and influenced by various factors, such as genetics, the environment, and immune responses. The evolution of keloid treatment has progressed from traditional surgical excision to a contemporary combination of therapies including injection and radiation treatments, among others. This article provides a comprehensive review of keloid pathogenesis and treatment, emphasizing the latest advances in the field. Ultimately, this review underscores the necessity for continued research to enhance our understanding of keloid pathogenesis and to devise more effective treatments for this challenging condition.

Digging out the biology properties of tRNA-derived small RNA from black hole.

An unique subclass of functional non-coding RNAs generated by transfer RNA (tRNA) under stress circumstances is known as tRNA-derived small RNA (tsRNA). tsRNAs can be divided into tRNA halves and tRNA-derived fragments (tRFs) based on the different cleavage sites. Like microRNAs, tsRNAs can attach to Argonaute (AGO) proteins to target downstream mRNA in a base pairing manner, which plays a role in rRNA processing, gene silencing, protein expression and viral infection. Notably, tsRNAs can also directly bind to protein and exhibit functions in transcription, protein modification, gene expression, protein stabilization, and signaling pathways. tsRNAs can control the expression of tumor suppressor genes and participate in the initiation of cancer. It can also mediate the progression of diseases by regulating cell viability, migration ability, inflammatory factor content and autophagy ability. Precision medicine targeting tsRNAs and drug therapy of plant-derived tsRNAs are expected to be used in clinical practice. In addition, liquid biopsy technology based on tsRNAs indicates a new direction for the non-invasive diagnosis of diseases.

Interaction, immune infiltration characteristics and prognostic modeling of efferocytosis-related subtypes in glioblastoma.

BACKGROUND: Efferocytosis is a biological process in which phagocytes remove apoptotic cells and vesicles from tissues. This process is initiated by the release of inflammatory mediators from apoptotic cells and plays a crucial role in resolving inflammation. The signals associated with efferocytosis have been found to regulate the inflammatory response and the tumor microenvironment (TME), which promotes the immune escape of tumor cells. However, the role of efferocytosis in glioblastoma multiforme (GBM) is not well understood and requires further investigation. METHODS: In this study, we conducted a comprehensive analysis of 22 efferocytosis-related genes (ERGs) by searching for studies related to efferocytosis. Using bulk RNA-Seq and single-cell sequencing data, we analyzed the expression and mutational characteristics of these ERGs. By using an unsupervised clustering algorithm, we obtained ERG clusters from 549 GBM patients and evaluated the immune infiltration characteristics of each cluster. We then identified differential genes (DEGs) in the two ERG clusters and classified GBM patients into different gene clusters using univariate cox analysis and unsupervised clustering algorithms. Finally, we utilized the Boruta algorithm to screen for prognostic genes and reduce dimensionality, and the PCA algorithm was applied to create a novel efferocytosis-related scoring system. RESULTS: Differential expression of ERGs in glioma cell lines and normal cells was analyzed by rt-PCR. Cell function experiments, on the other hand, validated TIMD4 as a tumor risk factor in GBM. We found that different ERG clusters and gene clusters have distinct prognostic and immune infiltration profiles. The ERG signature we developed provides insight into the tumor microenvironment of GBM. Patients with lower ERG scores have a better survival rate and a higher likelihood of benefiting from immunotherapy. CONCLUSIONS: Our novel efferocytosis-related signature has the potential to be used in clinical practice for risk stratification of GBM patients and for selecting individuals who are likely to respond to immunotherapy. This can help clinicians design appropriate targeted therapies before initiating clinical treatment.

The role of ATP binding cassette (ABC) transporters in breast cancer: Evaluating prognosis, predicting immunity, and guiding treatment.

Breast cancer is currently the most prevalent form of cancer worldwide. Nevertheless, there remains limited clarity regarding our understanding of the tumor microenvironment and metabolic characteristics associated with it. ATP-binding cassette (ABC) transporters are the predominant transmembrane transporters found in organisms. Therefore, it is essential to investigate the role of ABC transporters in breast cancer. Transcriptome data from breast cancer patients were downloaded from the TCGA database. ABC transporter-related genes were obtained from the Genecards database. By LASSO regression, ABC-associated prognostic signature was constructed in breast cancer. Subsequently, immune microenvironment analysis was performed. Finally, cell experiments were performed to verify the function of ABCB7 in the breast cancer cell lines MDA-MB-231 and MCF-7. Using the ABC transporter-associated signature, we calculated a risk score for each breast cancer patient. Patients with breast cancer were subsequently categorized into high-risk and low-risk groups, utilizing the median risk score as the threshold. Notably, patients in the high-risk group exhibited significantly worse prognosis (P<0.05). Additionally, differences were observed in terms of immune cell infiltration levels, immune correlations, and gene expression of immune checkpoints between the two groups. Functional experiments conducted on breast cancer cell lines MDA-MB-231 and MCF-7 demonstrated that ABCB7 knockdown significantly diminished cell activity, proliferation, invasion, and migration. These findings emphasize the significance of understanding ABC transporter-mediated metabolic and transport characteristics in breast cancer, offering promising directions for further research and potential therapeutic interventions.

Combining single-cell sequencing and spatial transcriptome sequencing to identify exosome-related features of glioblastoma and constructing a prognostic model to identify BARD1 as a potential therapeutic target for GBM patients.

Background: Glioblastoma (GBM) is a malignant primary brain tumor. This study focused on exploring the exosome-related features of glioblastoma to better understand its cellular composition and molecular characteristics. Methods: Single-cell RNA sequencing (scRNA-seq) and spatial transcriptome RNA sequencing (stRNA-seq) were used to analyze the heterogeneity of glioblastomas. After data integration, cell clustering, and annotation, five algorithms were used to calculate scores for exosome-related genes(ERGs). Cell trajectory analysis and intercellular communication analysis were performed to explore exosome-related communication patterns. Spatial transcriptome sequencing data were analyzed to validate the findings. To further utilize exosome-related features to aid in clinical decision-making, a prognostic model was constructed using GBM's bulk RNA-seq. Results: Different cell subpopulations were observed in GBM, with Monocytes/macrophages and malignant cells in tumor samples showing higher exosome-related scores. After identifying differentially expressed ERGs in malignant cells, pseudotime analysis revealed the cellular status of malignant cells during development. Intercellular communication analysis highlighted signaling pathways and ligand-receptor interactions. Spatial transcriptome sequencing confirmed the high expression of exosome-related gene features in the tumor core region. A prognostic model based on six ERGs was shown to be predictive of overall survival and immunotherapy outcome in GBM patients. Finally, based on the results of scRNA-seq and prognostic modeling as well as a series of cell function experiments, BARD1 was identified as a novel target for the treatment of GBM. Conclusion: This study provides a comprehensive understanding of the exosome-related features of GBM in both scRNA-seq and stRNA-seq, with malignant cells with higher exosome-related scores exhibiting stronger communication with Monocytes/macrophages. In terms of spatial data, highly scored malignant cells were also concentrated in the tumor core region. In bulk RNA-seq, patients with a high exosome-related index exhibited an immunosuppressive microenvironment, which was accompanied by a worse prognosis as well as immunotherapy outcomes. Prognostic models constructed using ERGs are expected to be independent prognostic indicators for GBM patients, with potential implications for personalized treatment strategies for GBM. Knockdown of BARD1 in GBM cell lines reduces the invasive and value-added capacity of tumor cells, and thus BARD1-positively expressing malignant cells are a risk factor for GBM patients.

Significance of liquid-liquid phase separation (LLPS)-related genes in breast cancer: a multi-omics analysis.

Currently, the role of liquid-liquid phase separation (LLPS) in cancer has been preliminarily explained. However, the significance of LLPS in breast cancer is unclear. In this study, single cell sequencing datasets GSE188600 and GSE198745 for breast cancer were downloaded from the GEO database. Transcriptome sequencing data for breast cancer were downloaded from UCSC database. We divided breast cancer cells into high-LLPS group and low-LLPS group by down dimension clustering analysis of single-cell sequencing data set, and obtained differentially expressed genes between the two groups. Subsequently, weighted co-expression network analysis (WGCNA) was performed on transcriptome sequencing data, and the module genes most associated with LLPS were obtained. COX regression and Lasso regression were performed and the prognostic model was constructed. Subsequently, survival analysis, principal component analysis, clinical correlation analysis, and nomogram construction were used to evaluate the significance of the prognostic model. Finally, cell experiments were used to verify the function of the model's key gene, PGAM1. We constructed a LLPS-related prognosis model consisting of nine genes: POLR3GL, PLAT, NDRG1, HMGB3, HSPH1, PSMD7, PDCD2, NONO and PGAM1. By calculating LLPS-related risk scores, breast cancer patients could be divided into high-risk and low-risk groups, with the high-risk group having a significantly worse prognosis. Cell experiments showed that the activity, proliferation, invasion and healing ability of breast cancer cell lines were significantly decreased after knockdown of the key gene PGAM1 in the model. Our study provides a new idea for prognostic stratification of breast cancer and provides a novel marker: PGAM1.

Transcriptome analysis reveals tumor antigen and immune subtypes of melanoma.

Purpose: To screen potential tumor antigens for melanoma vaccine development and identify different immune subtypes. Methods: Transcriptional data (HTSEQ-FPKM) and clinical information of a 472 Melanoma cohort GDC TCGA Melanoma (SKCM) were downloaded from the UCSC XENA website (http://xena.ucsc.edu/). Subsequently, transcriptome data and clinical information of 210 melanoma cohort GSE65904 were downloaded from Gene Expression Omnibus (GEO), a large global public database. All the transcriptome expression data matrices were log2 transformed for subsequent analysis. GEPIA, TIMER, and IMMPORT databases are also used for analysis. Cell function experiments were performed to validate the role of the IDO1 gene in melanoma cell line A375. Results: Our study provides potential tumor antigens for vaccine development in melanoma patients: GZMB, GBP4, CD79A, APOBEC3F, IDO1, JCHAIN, LAG3, PLA2G2D, XCL2. In addition, we divide melanoma patients into two immune subtypes that have significant differences in tumor immunity and may have different responses to vaccination. In view of the unclear role of IDO1 in melanoma, we selected IDO1 for cell assay validation. Cell function assay showed that IDO1 was significantly overexpressed in the melanoma A375 cell line. After IDO1 knockdown, the activity, invasion, migration and healing ability of A375 cell lines were significantly decreased. Conclusion: Our study could provide a reference for the development of vaccines for melanoma patients.

Identification of high-risk factors associated with mortality at 1-, 3-, and 5-year intervals in gastric cancer patients undergoing radical surgery and immunotherapy: an 8-year multicenter retrospective analysis.

Background: Combining immunotherapy with surgical intervention is a prevailing and radical therapeutic strategy for individuals afflicted with gastric carcinoma; nonetheless, certain patients exhibit unfavorable prognoses even subsequent to this treatment regimen. This research endeavors to devise a machine learning algorithm to recognize risk factors with a high probability of inducing mortality among patients diagnosed with gastric cancer, both prior to and during their course of treatment. Methods: Within the purview of this investigation, a cohort of 1015 individuals with gastric cancer were incorporated, and 39 variables encompassing diverse features were recorded. To construct the models, we employed three distinct machine learning algorithms, specifically extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor algorithm (KNN). The models were subjected to internal validation through employment of the k-fold cross-validation technique, and subsequently, an external dataset was utilized to externally validate the models. Results: In comparison to other machine learning algorithms employed, the XGBoost algorithm demonstrated superior predictive capacity regarding the risk factors that affect mortality after combination therapy in gastric cancer patients for a duration of one year, three years, and five years posttreatment. The common risk factors that significantly impacted patient survival during the aforementioned time intervals were identified as advanced age, tumor invasion, tumor lymph node metastasis, tumor peripheral nerve invasion (PNI), multiple tumors, tumor size, carcinoembryonic antigen (CEA) level, carbohydrate antigen 125 (CA125) level, carbohydrate antigen 72-4 (CA72-4) level, and H. pylori infection. Conclusion: The XGBoost algorithm can assist clinicians in identifying pivotal prognostic factors that are of clinical significance and can contribute toward individualized patient monitoring and management.

Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma.

Background: Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs). Methods: We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD. Results: We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model's performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells. Conclusion: Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.

A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients.

The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. In the context of ovarian cancer immunotherapy, the development, and outcome of treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding of the features of TEX within the immune microenvironment of ovarian cancer is of paramount importance for the management of OC patients. To this end, we leveraged single-cell RNA data from OC to perform clustering and identify T-cell marker genes utilizing the Unified Modal Approximation and Projection (UMAP) approach. Through GSVA and WGCNA in bulk RNA-seq data, we identified 185 TEX-related genes (TEXRGs). Subsequently, we transformed ten machine learning algorithms into 80 combinations and selected the most optimal one to construct TEX-related prognostic features (TEXRPS) based on the mean C-index of the three OC cohorts. In addition, we explored the disparities in clinicopathological features, mutational status, immune cell infiltration, and immunotherapy efficacy between the high-risk (HR) and low-risk (LR) groups. Upon the integration of clinicopathological features, TEXRPS displayed robust predictive power. Notably, patients in the LR group exhibited a superior prognosis, higher tumor mutational load (TMB), greater immune cell infiltration abundance, and enhanced sensitivity to immunotherapy. Lastly, we verified the differential expression of the model gene CD44 using qRT-PCR. In conclusion, our study offers a valuable tool to guide clinical management and targeted therapy of OC.