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PeerJ 2024Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related deaths, with very limited therapeutic options available. This study aims to...
BACKGROUND
Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related deaths, with very limited therapeutic options available. This study aims to comprehensively depict the heterogeneity and identify prognostic targets for PDAC with single-cell RNA sequencing (scRNA-seq) analysis.
METHODS
ScRNA-seq analysis was performed on 16 primary PDAC and three adjacent lesions. A series of analytical methods were applied for analysis in cell clustering, gene profiling, lineage trajectory analysis and cell-to-cell interactions. experiments including colony formation, wound healing and sphere formation assay were performed to assess the role of makers.
RESULTS
A total of 32,480 cells were clustered into six major populations, among which the ductal cell cluster expressing high copy number variants (CNVs) was defined as malignant cells. Malignant cells were further subtyped into five subgroups which exhibited specific features in immunologic and metabolic activities. Pseudotime trajectory analysis indicated that components of various oncogenic pathways were differentially expressed along tumor progression. Furthermore, intensive substantial crosstalk between ductal cells and stromal cells was identified. Finally, genes (REG4 and SPINK1) screened out of differentially expressed genes (DEGs) were upregulated in PDAC cell lines. Silencing either of them significantly impaired proliferation, invasion, migration and stemness of PDAC cells.
CONCLUSIONS
Our findings offer a valuable resource for deciphering the heterogeneity of malignant ductal cells in PDAC. REG4 and SPINK1 are expected to be promising targets for PDAC therapy.
Topics: Humans; Carcinoma, Pancreatic Ductal; Trypsin Inhibitor, Kazal Pancreatic; Pancreatic Neoplasms; Prognosis; Single-Cell Analysis; Transcriptome; Lectins, C-Type; Cell Line, Tumor; Gene Expression Regulation, Neoplastic; Biomarkers, Tumor; Female; Male; Pancreatitis-Associated Proteins
PubMed: 38827297
DOI: 10.7717/peerj.17350 -
Cureus Apr 2024Leiomyosarcomas (LMSs) account for 10-20% of all soft-tissue sarcomas (STSs). Soft-tissue sarcomas, and more specifically LMS, typically originate from the uterus,...
Leiomyosarcomas (LMSs) account for 10-20% of all soft-tissue sarcomas (STSs). Soft-tissue sarcomas, and more specifically LMS, typically originate from the uterus, extremity, retroperitoneal, or lower intraabdominal gastrointestinal organs. Due to the rarity and variability in presentation, it is difficult to describe identifiable risk factors, determine etiology, predict disease progression, and prognosticate these types of neoplasms. We present the case of a 77-year-old woman presenting to the emergency department with shortness of breath. After being diagnosed and treated for mild exacerbation of congestive heart failure, she was incidentally found to be anemic. Further workup, including an esophagogastroduodenoscopy, revealed a bleeding gastric mass, which was biopsied. Histopathology and immunohistochemistry confirmed the mass to be primary gastric LMS. Due to its rarity, an interdisciplinary approach involving clinical, histopathologic, and immunohistochemical data is necessary to successfully identify and diagnose gastrointestinal LMS. This case report aims to contribute to the paucity of information available in the literature regarding gastric LMS so that it may be better understood.
PubMed: 38826607
DOI: 10.7759/cureus.59406 -
Journal of Experimental & Clinical... Jun 2024Phosphoinositide-3-kinase γ (PI3Kγ) plays a critical role in pancreatic ductal adenocarcinoma (PDA) by driving the recruitment of myeloid-derived suppressor cells...
Phosphoinositide-3-kinase γ (PI3Kγ) plays a critical role in pancreatic ductal adenocarcinoma (PDA) by driving the recruitment of myeloid-derived suppressor cells (MDSC) into tumor tissues, leading to tumor growth and metastasis. MDSC also impair the efficacy of immunotherapy. In this study we verify the hypothesis that MDSC targeting, via PI3Kγ inhibition, synergizes with α-enolase (ENO1) DNA vaccination in counteracting tumor growth.Mice that received ENO1 vaccination followed by PI3Kγ inhibition had significantly smaller tumors compared to those treated with ENO1 alone or the control group, and correlated with i) increased circulating anti-ENO1 specific IgG and IFNγ secretion by T cells, ii) increased tumor infiltration of CD8 T cells and M1-like macrophages, as well as up-modulation of T cell activation and M1-like related transcripts, iii) decreased infiltration of Treg FoxP3 T cells, endothelial cells and pericytes, and down-modulation of the stromal compartment and T cell exhaustion gene transcription, iv) reduction of mature and neo-formed vessels, v) increased follicular helper T cell activation and vi) increased "antigen spreading", as many other tumor-associated antigens were recognized by IgG2c "cytotoxic" antibodies. PDA mouse models genetically devoid of PI3Kγ showed an increased survival and a pattern of transcripts in the tumor area similar to that of pharmacologically-inhibited PI3Kγ-proficient mice. Notably, tumor reduction was abrogated in ENO1 + PI3Kγ inhibition-treated mice in which B cells were depleted.These data highlight a novel role of PI3Kγ in B cell-dependent immunity, suggesting that PI3Kγ depletion strengthens the anti-tumor response elicited by the ENO1 DNA vaccine.
Topics: Animals; Mice; Vaccines, DNA; Pancreatic Neoplasms; B-Lymphocytes; Class Ib Phosphatidylinositol 3-Kinase; Humans; Cell Line, Tumor; Cancer Vaccines; Disease Models, Animal; Myeloid-Derived Suppressor Cells
PubMed: 38824552
DOI: 10.1186/s13046-024-03080-1 -
Scientific Reports Jun 2024This study aimed to identify factors that affect lymphovascular space invasion (LVSI) in endometrial cancer (EC) using machine learning technology, and to build a...
Construction and validation of a clinical risk model based on machine learning for screening characteristic factors of lymphovascular space invasion in endometrial cancer.
This study aimed to identify factors that affect lymphovascular space invasion (LVSI) in endometrial cancer (EC) using machine learning technology, and to build a clinical risk assessment model based on these factors. Samples were collected from May 2017 to March 2022, including 312 EC patients who received treatment at Xuzhou Medical University Affiliated Hospital of Lianyungang. Of these, 219 cases were collected for the training group and 93 for the validation group. Clinical data and laboratory indicators were analyzed. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used to analyze risk factors and construct risk models. The LVSI and non-LVSI groups showed statistical significance in clinical data and laboratory indicators (P < 0.05). Multivariable logistic regression analysis identified independent risk factors for LVSI in EC, which were myometrial infiltration depth, cervical stromal invasion, lymphocyte count (LYM), monocyte count (MONO), albumin (ALB), and fibrinogen (FIB) (P < 0.05). LASSO regression identified 19 key feature factors for model construction. In the training and validation groups, the risk scores for the logistic and LASSO models were significantly higher in the LVSI group compared with that in the non-LVSI group (P < 0.001). The model was built based on machine learning and can effectively predict LVSI in EC and enhance preoperative decision-making. The reliability of the model was demonstrated by the significant difference in risk scores between LVSI and non-LVSI patients in both the training and validation groups.
Topics: Humans; Female; Machine Learning; Endometrial Neoplasms; Middle Aged; Risk Factors; Neoplasm Invasiveness; Risk Assessment; Aged; Lymphatic Metastasis; Logistic Models
PubMed: 38824215
DOI: 10.1038/s41598-024-63436-7 -
Biomedicine & Pharmacotherapy =... Jul 2024In contemporary times, tumors have emerged as the primary cause of mortality in the global population. Ongoing research has shed light on the significance of... (Review)
Review
In contemporary times, tumors have emerged as the primary cause of mortality in the global population. Ongoing research has shed light on the significance of neurotransmitters in the regulation of tumors. It has been established that neurotransmitters play a pivotal role in tumor cell angiogenesis by triggering the transformation of stromal cells into tumor cells, modulating receptors on tumor stem cells, and even inducing immunosuppression. These actions ultimately foster the proliferation and metastasis of tumor cells. Several major neurotransmitters have been found to exert modulatory effects on tumor cells, including the ability to restrict emergency hematopoiesis and bind to receptors on the postsynaptic membrane, thereby inhibiting malignant progression. The abnormal secretion of neurotransmitters is closely associated with tumor progression, suggesting that focusing on neurotransmitters may yield unexpected breakthroughs in tumor therapy. This article presents an analysis and outlook on the potential of targeting neurotransmitters in tumor therapy.
Topics: Humans; Neurotransmitter Agents; Neoplasms; Animals; Disease Progression; Neovascularization, Pathologic
PubMed: 38823279
DOI: 10.1016/j.biopha.2024.116844 -
Journal of Translational Medicine May 2024Olfactory neuroblastoma is a rare malignancy of the anterior skull base typically treated with surgery and adjuvant radiation. Although outcomes are fair for low-grade...
BACKGROUND
Olfactory neuroblastoma is a rare malignancy of the anterior skull base typically treated with surgery and adjuvant radiation. Although outcomes are fair for low-grade disease, patients with high-grade, recurrent, or metastatic disease oftentimes respond poorly to standard treatment methods. We hypothesized that an in-depth evaluation of the olfactory neuroblastoma tumor immune microenvironment would identify mechanisms of immune evasion in high-grade olfactory neuroblastoma as well as rational targetable mechanisms for future translational immunotherapeutic approaches.
METHODS
Multispectral immunofluorescence and RNAScope evaluation of the tumor immune microenvironment was performed on forty-seven clinically annotated olfactory neuroblastoma samples. A retrospective chart review was performed and clinical correlations assessed.
RESULTS
A significant T cell infiltration was noted in olfactory neuroblastoma samples with a stromal predilection, presence of myeloid-derived suppressor cells, and sparse natural killer cells. A striking decrease was observed in MHC-I expression in high-grade olfactory neuroblastoma compared to low-grade disease, representing a mechanism of immune evasion in high-grade disease. Mechanistically, the immune effector stromal predilection appears driven by low tumor cell MHC class II (HLA-DR), CXCL9, and CXCL10 expression as those tumors with increased tumor cell expression of each of these mediators correlated with significant increases in T cell infiltration.
CONCLUSION
These data suggest that immunotherapeutic strategies that augment tumor cell expression of MHC class II, CXCL9, and CXCL10 may improve parenchymal trafficking of immune effector cells in olfactory neuroblastoma and augment immunotherapeutic responses.
Topics: Humans; Esthesioneuroblastoma, Olfactory; Chemokine CXCL10; Immunotherapy; Female; Male; Middle Aged; Chemokine CXCL9; Tumor Microenvironment; HLA-DR Antigens; Aged; Nose Neoplasms; Adult; Gene Expression Regulation, Neoplastic
PubMed: 38822345
DOI: 10.1186/s12967-024-05339-9 -
Frontiers in Immunology 2024Despite the efforts, pancreatic ductal adenocarcinoma (PDAC) is still highly lethal. Therapeutic challenges reside in late diagnosis and establishment of peculiar tumor... (Review)
Review
Despite the efforts, pancreatic ductal adenocarcinoma (PDAC) is still highly lethal. Therapeutic challenges reside in late diagnosis and establishment of peculiar tumor microenvironment (TME) supporting tumor outgrowth. This stromal landscape is highly heterogeneous between patients and even in the same patient. The organization of functional sub-TME with different cellular compositions provides evolutive advantages and sustains therapeutic resistance. Tumor progressively establishes a TME that can suit its own needs, including proliferation, stemness and invasion. Cancer-associated fibroblasts and immune cells, the main non-neoplastic cellular TME components, follow soluble factors-mediated neoplastic instructions and synergize to promote chemoresistance and immune surveillance destruction. Unveiling heterotypic stromal-neoplastic interactions is thus pivotal to breaking this synergism and promoting the reprogramming of the TME toward an anti-tumor milieu, improving thus the efficacy of conventional and immune-based therapies. We underscore recent advances in the characterization of immune and fibroblast stromal components supporting or dampening pancreatic cancer progression, as well as novel multi-omic technologies improving the current knowledge of PDAC biology. Finally, we put into context how the clinic will translate the acquired knowledge to design new-generation clinical trials with the final aim of improving the outcome of PDAC patients.
Topics: Humans; Tumor Microenvironment; Carcinoma, Pancreatic Ductal; Drug Resistance, Neoplasm; Pancreatic Neoplasms; Animals; Cancer-Associated Fibroblasts; Immune Tolerance
PubMed: 38817612
DOI: 10.3389/fimmu.2024.1341079 -
Scientific Reports May 2024Gastrointestinal stromal tumors (GISTs) are a rare type of tumor that can develop liver metastasis (LIM), significantly impacting the patient's prognosis. This study...
Gastrointestinal stromal tumors (GISTs) are a rare type of tumor that can develop liver metastasis (LIM), significantly impacting the patient's prognosis. This study aimed to predict LIM in GIST patients by constructing machine learning (ML) algorithms to assist clinicians in the decision-making process for treatment. Retrospective analysis was performed using the Surveillance, Epidemiology, and End Results (SEER) database, and cases from 2010 to 2015 were assigned to the developing sets, while cases from 2016 to 2017 were assigned to the testing set. Missing values were addressed using the multiple imputation technique. Four algorithms were utilized to construct the models, comprising traditional logistic regression (LR) and automated machine learning (AutoML) analysis such as gradient boost machine (GBM), deep neural net (DL), and generalized linear model (GLM). We evaluated the models' performance using LR-based metrics, including the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA), as well as AutoML-based metrics, such as feature importance, SHapley Additive exPlanation (SHAP) Plots, and Local Interpretable Model Agnostic Explanation (LIME). A total of 6207 patients were included in this study, with 2683, 1780, and 1744 patients allocated to the training, validation, and test sets, respectively. Among the different models evaluated, the GBM model demonstrated the highest performance in the training, validation, and test cohorts, with respective AUC values of 0.805, 0.780, and 0.795. Furthermore, the GBM model outperformed other AutoML models in terms of accuracy, achieving 0.747, 0.700, and 0.706 in the training, validation, and test cohorts, respectively. Additionally, the study revealed that tumor size and tumor location were the most significant predictors influencing the AutoML model's ability to accurately predict LIM. The AutoML model utilizing the GBM algorithm for GIST patients can effectively predict the risk of LIM and provide clinicians with a reference for developing individualized treatment plans.
Topics: Humans; Gastrointestinal Stromal Tumors; Liver Neoplasms; Male; Female; Machine Learning; Middle Aged; SEER Program; Retrospective Studies; Aged; Prognosis; Adult; Algorithms; ROC Curve; Gastrointestinal Neoplasms
PubMed: 38816560
DOI: 10.1038/s41598-024-62311-9 -
Scientific Reports May 2024Fatty acid metabolism has been identified as an emerging hallmark of cancer, which was closely associated with cancer prognosis. Whether fatty acid metabolism-related...
Fatty acid metabolism has been identified as an emerging hallmark of cancer, which was closely associated with cancer prognosis. Whether fatty acid metabolism-related genes (FMGs) signature play a more crucial role in biological behavior of esophageal squamous cell carcinoma (ESCC) prognosis remains unknown. Thus, we aimed to identify a reliable FMGs signature for assisting treatment decisions and prognosis evaluation of ESCC. In the present study, we conducted consensus clustering analysis on 259 publicly available ESCC samples. The clinical information was downloaded from The Cancer Genome Atlas (TCGA, 80 ESCC samples) and Gene Expression Omnibus (GEO) database (GSE53625, 179 ESCC samples). A consensus clustering arithmetic was used to determine the FMGs molecular subtypes, and survival outcomes and immune features were evaluated among the different subtypes. Kaplan-Meier analysis and the receiver operating characteristic (ROC) was applied to evaluate the reliability of the risk model in training cohort, validation cohort and all cohorts. A nomogram to predict patients' 1-year, 3-year and 5-year survival rate was also studied. Finally, CCK-8 assay, wound healing assay, and transwell assay were implemented to evaluate the inherent mechanisms of FMGs for tumorigenesis in ESCC. Two subtypes were identified by consensus clustering, of which cluster 2 is preferentially associated with poor prognosis, lower immune cell infiltration. A fatty acid (FA) metabolism-related risk model containing eight genes (FZD10, TACSTD2, MUC4, PDLIM1, PRSS12, BAALC, DNAJA2 and ALOX12B) was established. High-risk group patients displayed worse survival, higher stromal, immune and ESTIMATE scores than in the low-risk group. Moreover, a nomogram revealed good predictive ability of clinical outcomes in ESCC patients. The results of qRT-PCR analysis revealed that the MUC4 and BAALC had high expression level, and FZD10, PDLIM1, TACSTD2, ALOX12B had low expression level in ESCC cells. In vitro, silencing MUC4 remarkably inhibited ESCC cell proliferation, invasion and migration. Our study fills the gap of FMGs signature in predicting the prognosis of ESCC patients. These findings revealed that cluster subtypes and risk model of FMGs had effects on survival prediction, and were expected to be the potential promising targets for ESCC.
Topics: Humans; Esophageal Squamous Cell Carcinoma; Esophageal Neoplasms; Fatty Acids; Mucin-4; Prognosis; Gene Expression Regulation, Neoplastic; Cell Line, Tumor; Female; Male; Biomarkers, Tumor; Cell Proliferation; Middle Aged; Gene Expression Profiling; Nomograms; Kaplan-Meier Estimate
PubMed: 38816411
DOI: 10.1038/s41598-024-62917-z -
Turkish Journal of Medical Sciences 2023Cysteine and glycine-rich protein 1 (CSRP1) is involved in the cysteine-rich protein family and is a marker of smooth muscle lineages. In colon cancer, the expression of...
BACKGROUND/AIM
Cysteine and glycine-rich protein 1 (CSRP1) is involved in the cysteine-rich protein family and is a marker of smooth muscle lineages. In colon cancer, the expression of this gene is associated with poor prognosis. In this study, the aim was to reevaluate its prognostic relationship in independent cohorts and explore potential underlying biological processes that are linked to aggressive behavior in tumors with high CSRP1 expression, such as epithelial-to-mesenchymal transition (EMT), stromal fractions in the tumor microenvironment, and consensus molecular subtypes (CMSs).
MATERIALS AND METHODS
RNA sequencing (RNAseq)-, microarray-, and single-cell RNAseq (scRNAseq)-based transcriptomic data were obtained from public databases. The EMT score was calculated based on the expression of E-cadherin and vimentin genes using a previously published method. The stromal score generated by the ESTIMATE method was utilized for the analysis of correlation with the CSRP1 expression. The scRNAseq data were analyzed via the Seurat R package. The immunohistochemistry-based protein level expression of CSRP1 was evaluated using the Human Protein Atlas database.
RESULTS
Lower CSRP1 expression was noted in colon tumors compared to normal colon tissue. Patients with a high CSRP1 expression had shorter recurrence-free, overall, and disease-specific survivals in the GSE39582 and GSE17536 datasets (p < 0.05). The methylation level of the CSRP1 gene was negatively correlated (r = -0.57, p < 0.0001) with CSRP1 expression in The Cancer Genome Atlas colon adenocarcinoma dataset. CSRP1 expression was positively correlated with the expression of mesenchymal markers, EMT score, and stromal score obtained via the ESTIMATE method. CMS4 colon tumors had a significantly higher CSRP1 expression compared to other CMSs. Analysis of the scRNAseq data revealed that CSRP1 was expressed by epithelial cells and cancer-associated fibroblasts in the colorectal tumor microenvironment, which was also confirmed by the protein expression data from the Human Protein Atlas database.
CONCLUSION
CSRP1 expression is associated with CMS4, a more mesenchymal stroma-rich molecular profile, and poor prognosis in colon cancer.
Topics: Humans; Colonic Neoplasms; Prognosis; Epithelial-Mesenchymal Transition; Male; Biomarkers, Tumor; Female; Tumor Microenvironment; Middle Aged; Repressor Proteins; Aged
PubMed: 38813484
DOI: 10.55730/1300-0144.5736