Prognostic value of prostaglandin I2 synthase and its correlation with tumor-infiltrating immune cells in lung cancer, ovarian cancer, and gastric cancer

The endoplasmic reticulum stress-related genes and molecular typing predicts prognosis and reveals characterization of tumor immune microenvironment in lung squamous cell carcinoma

BACKGROUND

Endoplasmic reticulum stress (ERS) acts critical roles on cell growth, proliferation, and metastasis in various cancers. However, the relationship between ERs and lung squamous cell carcinoma (LUSC) prognoses still remains unclear.

METHODS

The consensus clustering analysis of ERS-related genes and the differential expression analysis between clusters were investigated in LUSC based on TCGA database. Furthermore, ERS-related prognostic risk models were constructed by LASSO regression and Cox regression analyses. Then, the predictive effect of the risk model was evaluated by Kaplan-Meier, Cox regression, and ROC Curve analyses, as well as validated in the GEO cohort. According to the optimal threshold, patients with LUSC were divided into high- and low- risk groups, and somatic mutations, immune cell infiltration, chemotherapy response and immunotherapy effect were systematically analyzed.

RESULTS

Two ERS-related clusters were identified in patients with LUSC that had distinct patterns of immune cell infiltration. A 5-genes ERS-related prognostic risk model and nomogram were constructed and validated. Kaplan-Meier curves and Cox regression analysis showed that ERS risk score was an independent prognostic factor (p < 0.001, HR = 1.317, 95% CI = 1.159-1.496). Patients with low-risk scores presented significantly lower TIDE scores and significantly lower IC50 values for common chemotherapy drugs such as cisplatin and gemcitabine.

CONCLUSION

ERS-related risk signature has certain prognostic value and may be a potential therapeutic target and prognostic biomarker for LUSC patients.

Analysis and experimental validation of fatty acid metabolism-related genes prostacyclin synthase (PTGIS) in endometrial cancer

The deregulation of fatty acid metabolism plays a pivotal role in cancer. Our objective is to construct a prognostic model for patients with endometrial carcinoma (EC) based on genes related to fatty acid metabolism-related genes (FAMGs). RNA sequencing and clinical data for EC were obtained from The Cancer Genome Atlas (TCGA). Lasso-Penalized Cox regression was employed to derive the risk formula for the model, the score = esum(corresponding coefficient × each gene's expression). Gene set enrichment analysis (GSEA) was utilized to examine the enrichment of KEGG and GO pathways within this model. Correlation analysis of immune function was conducted using Single-sample GSEA (ssGSEA). The "ESTIMATE" package in R was utilized to evaluate the tumor microenvironment. The support vector machine recursive feature elimination (SVM-RFE) and randomforest maps were employed to identify key genes. The effects of PTGIS on the malignant biological behavior of EC were assessed through CCK-8 assay, transwell invasion assay, cell cycle analysis, apoptosis assay, and tumor xenografts in nude mice. A novel prognostic signature comprising 10 FAMGs (INMT, ACACB, ACOT4, ACOXL, CYP4F3, FAAH, GPX1, HPGDS, PON3, PTGIS) was developed. This risk score serves as an independent prognostic marker validated for EC. According to ssGSEA analysis, the low- and high-risk groups exhibited distinct immune enrichments. The key gene PTGIS was screened by SVM-RFE and randomforest method. Furthermore, we validated the expression of PTGIS through qRT-PCR. In vitro and in vivo experiments also confirmed the effect of PTGIS on the malignant biological behavior of EC.

Validating the role of PTGIS gene in colorectal cancer by bioinformatics analysis and in vitro experiments

Prostaglandin I2 synthase (PTGIS) is a member of the cytochrome P450 family. Studies have revealed that differential expression of the PTGIS gene is closely related to the pathological and physiological processes of many diseases, including breast cancer, oral squamous cell carcinoma, and head and neck cancer. However, the mechanism of action of the PTGIS gene in colorectal cancer is not fully understood. This study explored the role of PTGIS in colorectal cancer through comprehensive bioinformatics analysis and in vitro experiments, and found that the expression of PTGIS gene in colorectal cancer tissue was significantly lower than that in normal colorectal tissue (P < 0.05), and high expression of PTGIS gene was associated with poor prognosis in patients (P < 0.05). The KEGG results showed that PTGIS-related genes were mainly enriched in metabolic pathways, arachidonic acid metabolism, steroid biosynthesis, and cancer pathways. The expression of PTGIS may be related to immune infiltration. Cell experiments showed that PTGIS was expressed at a lower level in cancer. Overexpression of PTGIS inhibited apoptosis and promoted proliferation, invasion, and migration ability of SW480 colorectal cancer cells. Analysis of the PTGIS gene in this study provides a theoretical basis for further exploring the pathogenesis of colorectal cancer and finding more accurate new targets for early screening and treatment of the cancer.

Serum exosomal m6A demethylase FTO promotes gefitinib resistance in non-small cell lung cancer by up-regulating FLRT3, PTGIS and SIRPα expression

This study investigates the molecular mechanism of FTO m6A demethylase in non-small cell lung cancer (NSCLC) and gefitinib resistance using GEO and TCGA databases. Differentially expressed genes (DEGs) were screened from RNA-seq data sets of serum exosomes of gefitinib-resistant NSCLC patients in the GEO database and the NSCLC data set in the GEPIA2 database. From this analysis, FTO m6A demethylase was found to be significantly upregulated in the serum exosomes of gefitinib-resistant NSCLC patients. To identify downstream genes affected by FTO m6A demethylase, weighted correlation network analysis and differential expression analysis were performed, resulting in the identification of three key downstream genes (FLRT3, PTGIS, and SIRPA). Using these genes, the authors constructed a prognostic risk assessment model. Patients with high-risk scores exhibited a significantly worse prognosis. The model could predict the prognosis of NSCLC with high accuracy measured by AUC values of 0.588, 0.608, and 0.603 at 1, 3, and 5 years respectively. Furthermore, m6A sites were found in FLRT3, PTGIS, and SIRPA genes, and FTO was significantly positively correlated with the expression of these downstream genes. Overall, FTO m6A demethylase promotes gefitinib resistance in NSCLC patients by upregulating downstream FLRT3, PTGIS, and SIRPA expression, with these three downstream genes serving as strong prognostic indicators.

Co-Occurrence of Germline Genomic Variants and Copy Number Variations in Hereditary Breast and Colorectal Cancer Patients

Hereditary Breast and Ovarian Cancer (HBOC) syndrome is an autosomal dominant disease associated with a high risk of developing breast, ovarian, and other malignancies. Lynch syndrome is caused by mutations in mismatch repair genes predisposing to colorectal and endometrial cancers, among others. A rare phenotype overlapping hereditary colorectal and breast cancer syndromes is poorly characterized. Three breast and colorectal cancer unrelated patients fulfilling clinical criteria for HBOC were tested by whole exome sequencing. A family history of colorectal cancer was reported in two patients (cases 2 and 3). Several variants and copy number variations were identified, which potentially contribute to the cancer risk or prognosis. All patients presented copy number imbalances encompassing PMS2 (two deletions and one duplication), a known gene involved in the DNA mismatch repair pathway. Two patients showed gains covering the POLE2 (cases 1 and 3), which is associated with DNA replication. Germline potentially damaging variants were found in PTCH1 (patient 3), MAT1A, and WRN (patient 2). Overall, concurrent genomic alterations were described that may increase the risk of cancer appearance in HBOC patients with breast and colorectal cancers.

Deciphering a cell death-associated signature for predicting prognosis and response to immunotherapy in lung squamous cell carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) is a subtype of non-small cell carcinoma, accounting for about 30% of all lung cancers. Yet, the evaluation of prognostic outcome and therapy response of patients with LUSC remains to be resolved. This study aimed to explore the prognostic value of cell death pathways and develop a cell death-associated signature for predicting prognosis and guiding treatment in LUSC.

METHODS

Transcriptome profiles and corresponding clinical information of LUSC patients were gathered from The Cancer Genome Atlas (TCGA-LUSC, n = 493) and Gene Expression Omnibus database (GSE74777, n = 107). The cell death-related genes including autophagy (n = 348), apoptosis (n = 163), and necrosis (n = 166) were retrieved from the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology databases. In the training cohort (TCGA-LUSC), LASSO Cox regression was used to construct four prognostic signatures of respective autophagy, apoptosis, and necrosis pathway and genes of three pathways. After comparing the four signatures, the cell death index (CDI), the signature of combined genes, was further validated in the GSE74777 dataset. We also investigated the clinical significance of the CDI signature in predicting the immunotherapeutic response of LUSC patients.

RESULTS

The CDI signature was significantly associated with the overall survival of LUSC patients in the training cohort (HR, 2.13; 95% CI, 1.62‒2.82; P < 0.001) and in the validation cohort (HR, 1.94; 95% CI, 1.01‒3.72; P = 0.04). The differentially expressed genes between the high- and low-risk groups contained cell death-associated cytokines and were enriched in immune-associated pathways. We also found a higher infiltration of naive CD4⁺ T cells, monocytes, activated dendritic cells, neutrophils, and lower infiltration of plasma cells and resting memory CD4⁺ T cells in the high-risk group. Tumor stemness indices, mRNAsi and mDNAsi, were both negatively correlated with the risk score of the CDI. Moreover, LUSC patients in the low-risk group are more likely to respond to immunotherapy than those in the high-risk group (P = 0.002).

CONCLUSIONS

This study revealed a reliable cell death-associated signature (CDI) that closely correlated with prognosis and the tumor microenvironment in LUSC, which may assist in predicting the prognosis and response to immunotherapy for patients with LUSC.

Regulations of Tumor Microenvironment by Prostaglandins

Prostaglandins, the bioactive lipids generated from the metabolism of arachidonic acid through cyclooxygenases, have potent effects on many constituents of tumor microenvironments. In this review, we will describe the formation and activities of prostaglandins in the context of the tumor microenvironment. We will discuss the regulation of cancer-associated fibroblasts and immune constituents by prostaglandins and their roles in immune escapes during tumor progression. The review concludes with future perspectives on improving the efficacy of immunotherapy through repurposing non-steroid anti-inflammatory drugs and other prostaglandin modulators.

A Prognostic Model Based on Metabolism-Related Genes for Patients with Ovarian Cancer

Metabolism-associated genes (MAGs) are important regulators of tumor progression and can affect a variety of physiological processes. In this study, we focused on the relationship between MAGs and Ovarian cancer (OC) prognosis.

METHOD

Metabolism-related genes were extracted from the Cancer Genome Atlas (TCGA) database. Through univariate COX and lasso regression models, a dynamic risk model based on MAGs was established. Compared with other clinical factors, demonstrated the ability of the model to predict the prognosis of patients with OC. The clinical samples were used to verify the expression of these MAGs.

RESULTS

A metabolism-associated gene signature was constructed by LASSO Cox regression analysis in OC, which was composed of 3-MAGs (PTGIS, AOC3, and IDO1). The signature was used to classify the OC patients into high-risk and low-risk groups. The overall survival of the low-risk group was significantly better than that of the high-risk group. The analysis of the therapeutic effect of bevacizumab showed that bevacizumab was not conducive to improving the prognosis of the low-risk group.

CONCLUSIONS

We constructed a prognostic model of MAGs in OC, which can be used to predict the prognosis of OC patients and may have a good guiding significance in the individualized treatment of patients.

Probing the Potential of Defense Response-Associated Genes for Predicting the Progression, Prognosis, and Immune Microenvironment of Osteosarcoma

BACKGROUND

The defense response is a type of self-protective response of the body that protects it from damage by pathogenic factors. Although these reactions make important contributions to the occurrence and development of tumors, the role they play in osteosarcoma (OS), particularly in the immune microenvironment, remains unpredictable.

METHODS

This study included the clinical information and transcriptomic data of 84 osteosarcoma samples and the microarray data of 12 mesenchymal stem cell samples and 84 osteosarcoma samples. We obtained 129 differentially expressed genes related to the defense response (DRGs) by taking the intersection of differentially expressed genes with genes involved in the defense response pathway, and prognostic genes were screened using univariate Cox regression. Least absolute shrinkage and selection operator (LASSO) penalized Cox regression and multivariate Cox regression were then used to establish a DRG prognostic signature (DGPS) via the stepwise method. DGPS performance was examined using independent prognostic analysis, survival curves, and receiver operating characteristic (ROC) curves. In addition, the molecular and immune mechanisms of adverse prognosis in high-risk populations identified by DGPS were elucidated. The results were well verified by experiments.

RESULT

BNIP3, PTGIS, and ZYX were identified as the most important DRGs for OS progression (hazard ratios of 2.044, 1.485, and 0.189, respectively). DGPS demonstrated outstanding performance in the prediction of OS prognosis (area under the curve (AUC) values of 0.842 and 0.787 in the training and test sets, respectively, adj-p < 0.05 in the survival curve). DGPS also performed better than a recent clinical prognostic approach with an AUC value of only 0.674 [metastasis], which was certified in the subsequent experimental results. These three genes regulate several key biological processes, including immune receptor activity and T cell activation, and they also reduce the infiltration of some immune cells, such as B cells, CD8+ T cells, and macrophages. Encouragingly, we found that DGPS was associated with sensitivity to chemotherapeutic drugs including JNK Inhibitor VIII, TGX221, MP470, and SB52334. Finally, we verified the effect of BNIP3 on apoptosis, proliferation, and migration of osteosarcoma cells through experiments.

CONCLUSIONS

This study elucidated the role and mechanism of BNIP3, PTGIS, and ZYX in OS progression and was well verified by the experimental results, enabling reliable prognostic means and treatment strategies to be proposed for OS patients.

PTGIS May Be a Predictive Marker for Ovarian Cancer by Regulating Fatty Acid Metabolism

Background

Ovarian cancer tends to metastasize to the omentum, which is an organ mainly composed of adipose tissue. Many studies have found that fatty acid metabolism is related to the occurrence and metastasis of cancers. Therefore, it is possible that fatty acid metabolism-related genes (FAMRG) affect the prognosis of ovarian cancer patients.

Methods

First, profiles of ovarian cancer and normal ovarian tissue transcriptomes were acquired from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. A LASSO regression predictive model was developed via the "glmnet" R package. The nomogram was created via the "regplot." Gene Set Variation Analysis (GSVA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) analyses were conducted to determine the FAMRGs' roles. The percentage of immunocyte infiltration was calculated via CIBERSORT. Using "pRRophetic," the sensitivity of eight regularly used medications and immunotherapy was anticipated.

Results

125 genes were determined as different expression genes (DEGs). Based on RXRA, ECI2, PTGIS, and ACACB, a prognostic model is created and the risk score is calculated. Analyses of univariate and multivariate regressions revealed that the risk score was a distinct prognostic factor (univariate: HR: 2.855, 95% CI: 1.756-4.739, P < 0.001; multivariate: HR: 2.943, 95% CI: 1.800-4.812, P < 0.001). The nomogram demonstrated that it properly predicted the 1-year survival rate. The expression of memory B molecular units, follicular helper T molecular units, regulatory T molecular units, and M1 macrophages differed remarkably between the groups at high and low risk (P < 0.05). Adipocytokine signaling pathways, cancer pathways, and degradation of valine, leucine, and isoleucine vary between high- and low-risk populations. The findings of the GO enrichment revealed that the extracellular matrix and cellular structure were the two most enriched pathways. PTGIS, which is an important gene in fatty acid metabolism, was identified as the hub gene. This result was verified in ovarian cancer and ovarian tissues. The connection between the gene and survival was statistically remarkable (P = 0.015). The pRRophetic algorithm revealed that the low-risk group was more adaptable to cisplatin, doxorubicin, 5-fluorouracil, and etoposide (P < 0.001).

Conclusion

PTGIS may be an indicator of prognosis and a possible therapeutic target for the therapy of ovarian cancer patients. The fatty acid metabolism of immune cells may be controlled, which has an indirect effect on cancer cell growth.

Effects of Lipid Metabolism-Related Genes PTGIS and HRASLS on Phenotype, Prognosis, and Tumor Immunity in Lung Squamous Cell Carcinoma

Background

Lipid metabolism reprogramming played an important role in cancer occurrence, development, and immune regulation. The aim of this study was to identify and validate lipid metabolism-related genes (LMRGs) associated with the phenotype, prognosis, and immunological characteristics of lung squamous cell carcinoma (LUSC).

Methods

In the TCGA cohort, bioinformatics and survival analysis were used to identify lipid metabolism-related differentially expressed genes (DEGs) associated with the prognosis of LUSC. PTGIS/HRASLS knockdown and overexpression effects on the LUSC phenotype were analyzed in vitro experiments. Based on the expression distribution of PTGIS/HRASLS, LUSC patients were divided into two clusters by consensus clustering. Clinical information, prognosis, immune infiltration, expression of immune checkpoints, and tumor mutation burden (TMB) level were compared between the TCGA and GSE4573 cohorts. The genes related to clustering and tumor immunity were screened by weighted gene coexpression network analysis (WGCNA), and the target module genes were analyzed by functional enrichment analysis, protein-protein interaction (PPI) analysis, and immune correlation analysis.

Results

191 lipid metabolism-related DEGs were identified, of which 5 genes were independent prognostic genes of LUSC. PTGIS/HRASLS were most closely related to LUSC prognosis and immunity. RT-qPCR, western blot (WB) analysis, and immunohistochemistry (IHC) showed that the expression of PTGIS was low in LUSC, while HRASLS was high. Functionally, PTGIS promoted LUSC proliferation, migration, and invasion, while HRASLS inhibited LUSC proliferation, migration, and invasion. The two clusters' expression and distribution of PTGIS/HRASLS had the opposite trend. Cluster 1 was associated with lower pathological staging (pT, pN, and pTNM stages), better prognosis, stronger immune infiltration, higher expression of immune checkpoints, and higher TMB level than cluster 2. WGCNA found that 28 genes including CD4 and IL10RA were related to the expression of PTGIS/HRASLS and tumor immune infiltration. PTGIS/HRASLS in the GSE4573 cohort had the same effect on LUSC prognosis and tumor immunity as the TCGA cohort.

Conclusions

PTGIS and HRASLS can be used as new therapeutic targets for LUSC as well as biomarkers for prognosis and tumor immunity, which has positive significance for guiding the immunotherapy of LUSC.

Prognostic value of nicotinamide adenine dinucleotide (NAD+) metabolic genes in patients with stomach adenocarcinoma based on bioinformatics analysis

Background

Because stomach adenocarcinoma (STAD) has a poor prognosis, it is necessary to explore new prognostic genes to stratify patients to guide existing individualized treatments.

Methods

Survival and clinical information, RNA-seq data and mutation data of STAD were acquired from The Cancer Genome Atlas (TCGA) database. Fifty-one nicotinamide adenine dinucleotide (NAD⁺) metabolism-related genes (NMRGs) were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Differentially expressed NMRGs (DE-NMRGs) between STAD and normal samples were screened, and consistent clustering analysis of STAD patients was performed based on the DE-NMRGs. Survival analysis, Gene Set Enrichment Analysis (GSEA), mutation frequency analysis, immune microenvironment analysis and drug prediction were performed among different clusters. Additionally, the differentially expressed genes (DEGs) among different clusters were selected, and the intersections of DEGs and DE-NMRGs were selected as the prognostic genes. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was performed on a human gastric mucosa epithelial cell line and cancer cell line to verify the expression of the prognostic genes.

Results

A total of 27 DE-NMRGs and two clusters were selected. There was a difference in survival between clusters 1 and 2. Furthermore, 18 DE-NMRGs were significantly different between clusters 1 and 2. The different Gene Ontology (GO) biological processes and KEGG pathways between clusters 1 and 2 were mainly enriched in cyclic nucleotide mediated signaling, synaptic signaling and hedgehog signaling pathway, etc. The somatic mutation frequencies were different between the two clusters, and TTN was the highest mutated gene in the patients of the clusters 1 and 2. Additionally, eight immune cells, immune score, stromal score, and estimate score were different between clusters 1 and 2. The patients in cluster 2 were sensitive to CTLA4 inhibitor treatment. Furthermore, the top five drugs (AP.24534, BX.795, Midostaurin, WO2009093927 and CCT007093) were significantly higher in cluster 1 than in cluster 2. Finally, three genes (AOX1, NNMT and PTGIS) were acquired as prognostic, and their expressions were consistent with the results of bioinformatics analysis.

Conclusions

Three prognostic genes related to NAD⁺ metabolism in STAD were screened out, which provides a theoretical basis and reference value for future treatment and prognosis of STAD.

Integrated bioinformatics analysis and experimental validation reveals fatty acid metabolism-related prognostic signature and immune responses for uterine corpus endometrial carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is the third most common gynecologic malignancy. Fatty acid metabolism (FAM) is an essential metabolic process in the immune microenvironment that occurs reprogramming in the presence of tumor signaling and nutrient competition. This study aimed to identify the fatty acid metabolism-related genes (FAMGs) to develop a risk signature for predicting UCEC.

Methods

The differentially expressed FAMGs between UCEC samples and controls from TCGA database were discovered. A prognostic signature was then constructed by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Based on the median risk score, UCEC samples were categorized into high- and low-FAMGs groups. Kaplan-Meier (K-M) curve was applied to determine patients’ overall survival (OS). The independent prognostic value was assessed by uni- and multivariate analyses. The associations between the risk score and immune status, immune score, and drug resistance were evaluated. Quantitative Real-time PCR (qRT-PCR) was utilized to confirm FAMGs expression levels in UCEC cells.

Results

We built a 10-FAMGs prognostic signature and examined the gene mutation and copy number variations (CNV). Patients with a high-FAMGs had a worse prognosis compared to low-FAMGs patients in TCGA train and test sets. We demonstrated that FAMGs-based risk signature was a significant independent prognostic predictor of UCEC. A nomogram was also created incorporating this risk model and clinicopathological features, with high prognostic performance for UCEC. The immune status of each group was varied, and immune score was higher in a low-FAMGs group. HLA-related genes such as DRB1, DMA, DMB, and DQB2 had higher expression levels in the low-FAMGs group. Meanwhile, high-FAMGs patients were likely to response more strongly to the targeted drugs Bortezomib, Foretinib and Gefitinib. The qRT-PCR evidence further verified the significant expression of FAMGs in this signature.

Conclusions

A FAMGs-based risk signature might be considered as an independent prognostic indicator to predict UCEC prognosis, evaluate immune status and provide a new direction for therapeutic strategies.

Metabolism reprogramming signature associated with stromal cells abundance in tumor microenvironment improve prognostic risk classification for gastric cancer

Background

Stromal cells play an important role in the process of tumor progression, but the relationship between stromal cells and metabolic reprogramming is not very clear in gastric cancer (GC).

Methods

Metabolism-related genes associated with stromal cells were identified in The Cancer Genome Atlas (TCGA) and GSE84437 datasets, and the two datasets with 804 GC patients were integrated into a training cohort to establish the prognostic signature. Univariate Cox regression analysis was used to screen for prognosis-related genes. A risk score was constructed by LASSO regression analysis combined with multivariate Cox regression analysis. The patients were classified into groups with high and low risk according to the median value. Two independent cohorts, GSE62254 (n = 300) and GSE15459 (n = 191), were used to externally verify the risk score performance. The CIBERSORT method was applied to quantify the immune cell infiltration of all included samples.

Results

A risk score consisting of 24 metabolic genes showed good performance in predicting the overall survival (OS) of GC patients in both the training (TCGA and GSE84437) and testing cohorts (GSE62254 and GSE15459). As the risk score increased, the patients’ risk of death increased. The risk score was an independent prognostic indicator in both the training and testing cohorts suggested by the univariate and multivariate Cox regression analyses. The patients were clustered into four subtypes according to the quantification of 22 kinds of immune cell infiltration (ICI). The proportion of ICI Cluster C with the best prognosis in the low-risk group was approximately twice as high as that in the high-risk group, and the risk score of ICI Cluster C was significantly lower than that of the other three subtypes.

Conclusion

Our study proposed the first scheme for prognostic risk classification of GC from the perspective of tumor stromal cells and metabolic reprogramming, which may contribute to the development of therapeutic strategies for GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02451-2.

Bioinformatics evaluation of a novel angiogenesis related genes-based signature for predicting prognosis and therapeutic efficacy in patients with gastric cancer

OBJECTIVE

Tumor angiogenesis plays a pivotal role in the development and metastasis of tumors. This study aimed to elucidate the association between angiogenesis-related genes (ARGs) and the prognosis of patients with gastric cancer (GC).

METHODS

Transcriptomics and clinical data of GC samples were obtained from The Cancer Genome Atlas (TCGA) as the training group and those from Gene Expression Omnibus (GEO, including GSE26253, GSE26091 and GSE66229) as the validation groups. Single-sample gene set enrichment analysis (ssGSEA) was performed for gene set enrichment analysis on the gene set of angiogenesis and divided patients into high- or low-ARG group. Subsequently, to improve the availability of the ARG signature, a ARGs subtype predictor was then constructed by integrating of four machine learning methods, including support vector machine (SVM), least absolute shrinkage and selection operator (LASSO) regression, Random Forest and Boruta (RFB) and extreme gradient boosting (XGBoost). Kaplan-Meier and receiver operating characteristic curves were used to evaluate the performance of prognosis prediction. The EPIC and xCELL method were used to calculate the profile of tumor-infiltrated immune cells.

RESULTS

The expression levels of a total of 36 ARGs that correlated with the survival of patients with GC were identified and utilized to establish an ARG-related prognosis signature. The area under the curve for predicting overall survival (OS) in the training group at the 1-, 3- and 5-year was 0.61, 0.64 and 0.76, respectively, and this was further validated using three independent GEO datasets. Moreover, the ARG signatures were significantly correlated with cancer-associated fibroblasts (CAFs), and GC patients that exhibited both high ARG expression level and matrix CAFs level had the most inferior outcomes. The multiple machine learning algorithms were applied to establish a 10-gene ARG subtype predictor, and notably, a high ARG-subtype predictor score was associated with reduced efficacy of immunotherapy, and potential anti-HER2 or FGFR4 therapy, but an increased sensitivity to anti-angiogenesis-related therapy.

CONCLUSION

The novel ARGs-based classification may act as a potential prognostic predictor for GC and be used as a guidance for clinicians in selecting potential responders for immunotherapy and targeted therapy.

Eicosanoids in the pancreatic tumor microenvironment - a multicellular, multifaceted progression

BACKGROUND AND AIMS

Eicosanoids, oxidized fatty acids that serve as cell-signaling molecules, have been broadly implicated in tumorigenesis. Here, we aimed to identify eicosanoids associated with pancreatic tumorigenesis and the cell types responsible for their synthesis.

METHODS

We profiled normal pancreas and pancreatic ductal adenocarcinoma (PDAC) in mouse models and patient samples using mass spectrometry. We interrogated RNA sequencing datasets for eicosanoid synthase or receptor expression. Findings were confirmed by immunostaining.

RESULTS

In murine models, we identified elevated levels of PGD₂, prostacyclin, and thromboxanes in neoplasia while PGE₂, 12-HHTre, HETEs, and HDoHEs are elevated specifically in tumors. Analysis of scRNA-seq datasets suggests that PGE₂ and prostacyclins are derived from fibroblasts, PGD₂ and thromboxanes from myeloid cells, and PGD₂ and 5-HETE from tuft cells. In patient samples, we identified a transition from PGD₂ to PGE₂-producing enzymes in the epithelium during the transition to PDAC, fibroblast/tumor expression of PTGIS, and myeloid/tumor cell expression of TBXAS1.

CONCLUSIONS

Our analyses identify key changes in eicosanoid species during pancreatic tumorigenesis and the cell types that contribute to their synthesis. Thromboxane and prostacyclin expression is conserved between animal models and human disease and may represent new druggable targets.

Lifetime ovulatory years and ovarian cancer gene expression profiles

BACKGROUND

Greater ovulatory years is associated with increased ovarian cancer risk. Although ovulation leads to an acute pro-inflammatory local environment, how long-term exposure to ovulation impacts ovarian carcinogenesis is not fully understood. Thus, we examined the association between gene expression profiles of ovarian tumors and lifetime ovulatory years to enhance understanding of associated biological pathways.

METHODS

RNA sequencing data was generated on 234 invasive ovarian cancer tumors that were high-grade serous, poorly differentiated, or high-grade endometrioid from the Nurses' Health Study (NHS), NHSII, and the New England Case Control Study. We used linear regression to identify differentially expressed genes by estimated ovulatory years, adjusted for birth decade and cohort, overall and stratified by menopausal status at diagnosis. We used false discovery rates (FDR) to account for multiple testing. Gene set enrichment analysis (GSEA) with Cancer Hallmarks, KEGG, and Reactome databases was used to identify biological pathways associated with ovulatory years.

RESULTS

No individual genes were significantly differentially expressed by ovulatory years (FDR > 0.19). However, GSEA identified several pathways that were significantly associated with ovulatory years, including downregulation of pathways related to inflammation and proliferation (FDR < 1.0 × 10-5). Greater ovulatory years were more strongly associated with downregulation of genes related to proliferation (e.g., E2F targets, FDR = 1.53 × 10-24; G2M checkpoints, FDR = 3.50 × 10-22) among premenopausal versus postmenopausal women at diagnosis. The association of greater ovulatory years with downregulation of genes involved in inflammatory response such as interferon gamma response pathways (FDR = 7.81 × 10-17) was stronger in postmenopausal women.

CONCLUSIONS

Our results provide novel insight into the biological pathways that link ovulatory years to ovarian carcinogenesis, which may lead to development of targeted prevention strategies for ovarian cancer.

Fatty Acid Synthase Is the Key Regulator of Fatty Acid Metabolism and Is Related to Immunotherapy in Bladder Cancer

Fatty acid metabolism (FAM) genes are potentially useful for predicting prognosis and immunotherapy response in bladder cancer (BC). To examine this, we constructed a prognostic model and identified key FAM genes in BC. Using transcriptional expression profiles and clinical data of BC patients from public datasets and Changhai (CH) hospital, we built and validated a risk-score model based on 13 prognostic FAM genes. Differential gene expression identified fatty acid synthase (FASN) as central to fatty acid metabolism in BC. FASN was differentially expressed between normal and tumor tissue, and was related to survival. In the CH dataset, FASN independently predicted muscle-invasive BC. FASN differential expression was significantly related to immune-cell infiltration and patients with low FASN expression responded better to immune checkpoint inhibitor (ICI) treatment. SREBF1 was predicted as the most significant transcription factor for FASN. Competing endogenous RNA network analysis suggested that lncRNA AC107027.3 may upregulate FASN by competitively binding miR-27A-3p, thereby regulating the immunotherapy response in BC. Dasatinib and temsirolimus are potential FASN-targeting drugs. Our model efficiently predicted prognosis in BC. FASN is central to fatty acid metabolism, and a potential indicator and regulator of ICI treatment.

Genome-Wide Analysis Indicates a Complete Prostaglandin Pathway from Synthesis to Inactivation in Pacific White Shrimp, Litopenaeus vannamei

Prostaglandins (PGs) play many essential roles in the development, immunity, metabolism, and reproduction of animals. In vertebrates, arachidonic acid (ARA) is generally converted to prostaglandin G₂ (PGG₂) and H₂ (PGH₂) by cyclooxygenase (COX); then, various biologically active PGs are produced through different downstream prostaglandin synthases (PGSs), while PGs are inactivated by 15-hydroxyprostaglandin dehydrogenase (PGDH). However, there is very limited knowledge of the PG biochemical pathways in invertebrates, particularly for crustaceans. In this study, nine genes involved in the prostaglandin pathway, including a COX, seven PGSs (PGES, PGES2, PGDS1/2, PGFS, AKR1C3, and TXA2S), and a PGDH were identified based on the Pacific white shrimp (Litopenaeus vannamei) genome, indicating a more complete PG pathway from synthesis to inactivation in crustaceans than in insects and mollusks. The homologous genes are conserved in amino acid sequences and structural domains, similar to those of related species. The expression patterns of these genes were further analyzed in a variety of tissues and developmental processes by RNA sequencing and quantitative real-time PCR. The mRNA expression of PGES was relatively stable in various tissues, while other genes were specifically expressed in distant tissues. During embryo development to post-larvae, COX, PGDS1, GDS2, and AKR1C3 expressions increased significantly, and increasing trends were also observed on PGES, PGDS2, and AKR1C3 at the post-molting stage. During the ovarian maturation, decreasing trends were found on PGES1, PGDS2, and PGDH in the hepatopancreas, but all gene expressions remained relatively stable in ovaries. In conclusion, this study provides basic knowledge for the synthesis and inactivation pathway of PG in crustaceans, which may contribute to the understanding of their regulatory mechanism in ontogenetic development and reproduction.

Membrane tension sensing molecule-FNBP1 is a prognostic biomarker related to immune infiltration in BRCA, LUAD and STAD

Background

Formin-binding protein 1/17 (FNBP1/FBP17), as a membrane-bound protein, is wildly expressed in eukaryotic cells and performs a critical role in tumor tumorigenesis and progression. However, the relationship between FNBP1 and immune infiltrating cells, prognostic value in patients still require comprehensive understanding. We purposed to explore the correlations of FNBP1 expression, prognosis and immune infiltration levels in various cancers.

Method

The expression and survival data of FNBP1 were collected from Oncomine, TIMER, GEPIA, Kaplan–Meier Plotter and PrognoScan databases. Correlations between FNBP1 and immune infiltrates were analyzed in TIMER and GEPIA databases.

Results

Compared with normal tissues, FNBP1 is significantly differentially expressed in a variety of tumor tissues. FNBP1 has significant and complex effects on the prognosis of kinds of cancers. High-expression was obviously correlated with better prognosis in breast carcinoma and lung adenocarcinoma, while worse prognosis in stomach adenocarcinoma. Besides, FNBP1 had a correlation with various immune infiltrating cells and diverse immune gene markers in breast invasive carcinoma (BRCA), lung adenocarcinoma (LUAD), and stomach adenocarcinoma (STAD). FNBP1 was also positively correlated with the adjustment of CD8+ cells, T cells, M2 macrophage, neutrophils, monocyte, Th1 cells, T regulatory cells (Treg) and Tumor-associated macrophages (TAMs). The expression level of FNBP1 is closely positively correlated with the expression level of multiple immune checkpoints in the three cancers. In addition, FNBP1 is significantly positively correlated with the expression levels of a variety of immunosuppressive molecules.

Conclusion

Our findings reveal FNBP1 can serve as a significant biomarker to influence the prognosis and the immune infiltrating levels in different cancers. The differential expression of FNBP1 might not only contribute to the judgment of metastatic and non-metastatic tumors but also in the immune escape by upregulating the expression of immune checkpoints.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12865-021-00475-z.

Prostaglandin Pathways: Opportunities for Cancer Prevention and Therapy

Because of profound effects observed in carcinogenesis, prostaglandins (PGs), prostaglandin-endoperoxide synthases, and PG receptors are implicated in cancer development and progression. Understanding the molecular mechanisms of PG actions has potential clinical relevance for cancer prevention and therapy. This review focuses on the current status of PG signaling pathways in modulating cancer progression and aims to provide insights into the mechanistic actions of PGs and their receptors in influencing tumor progression. We also examine several small molecules identified as having anticancer activity that target prostaglandin receptors. The literature suggests that targeting PG pathways could provide opportunities for cancer prevention and therapy.

Butyrophilin-like 9 expression is associated with outcome in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most prevalent non-small cell lung cancer (NSCLC). Patients with LUAD have a poor 5-year survival rate. The use of immune checkpoint inhibitors (ICIs) for the treatment of LUAD has been on the rise in the past decade. This study explored the prognostic role of butyrophilin-like 9 (BTNL9) in LUAD.

METHODS

Gene expression profile of buytrophilins (BTNs) was determined using the GEPIA database. The effect of BTNL9 on the survival of LUAD patients was assessed using Kaplan-Meier plotter and OncoLnc. Correlation between BTNL9 expression and tumor-infiltrating immune cells (TILs) was explored using TIMER and GEPIA databases. Further, the relationship between BTNL9 expression and drug response was evaluated using CARE. Besides, construction and evaluation of nomogram based on BTNL9 expression and TNM stage.

RESULTS

BTNL9 expression was downregulated in LUAD and was associated with a poor probability of 1, 3, 5-years overall survival (OS). In addition, BTNL9 expression was regulated at epigenetic and post-transcriptional modification levels. Moreover, BTNL9 expression was significantly positively correlated with ImmuneScore and ESTIMATEScore. Furthermore, BTNL9 expression was positively associated with infiltration levels of B cells, CD4+ T cells, and macrophages. Kaplan-Meier analysis showed that BTNL9 expression in B cells and dendritic cells (DCs) was significantly associated with OS. BTNL9 expression was significantly positively correlated with CARE scores.

CONCLUSIONS

These findings show that BTNL9 is a potential prognostic biomarker for LUAD. Low BTNL9 expression levels associated with low infiltration levels of naïve B cells, and DCs in the tumor microenvironment are unfavorable for OS in LUAD patients.

A Novel Immune-Related Seventeen-Gene Signature for Predicting Early Stage Lung Squamous Cell Carcinoma Prognosis

With the increasingly early stage lung squamous cell carcinoma (LUSC) being discovered, there is an urgent need for a comprehensive analysis of the prognostic characteristics of early stage LUSC. Here, we developed an immune-related gene signature for outcome prediction of early stage LUSC based on three independent cohorts. Differentially expressed genes (DEGs) were identified using CIBERSORT and ESTMATE algorithm. Then, a 17-immune-related gene (RPRM, APOH, SSX1, MSGN1, HPR, ISM2, FGA, LBP, HAS1, CSF2, RETN, CCL2, CCL21, MMP19, PTGIS, F13A1, C1QTNF1) signature was identified using univariate Cox regression, LASSO regression and stepwise multivariable Cox analysis based on the verified DEGs from 401 cases in The Cancer Genome Atlas (TCGA) database. Subsequently, a cohort of GSE74777 containing 107 cases downloaded from Gene Expression Omnibus (GEO) database and an independent data set consisting of 36 frozen tissues collected from National Cancer Center were used to validate the predictive value of the signature. Seventeen immune-related genes were identified from TCGA cohort, which were further used to establish a classification system to construct cases into high- and low-risk groups in terms of overall survival. This classifier was still an independent prognostic factor in multivariate analysis. In addition, another two independent cohorts and different clinical subgroups validated the significant predictive value of the signature. Further mechanism research found early stage LUSC patients with high risk had special immune cell infiltration characteristics and gene mutation profiles. In conclusion, we characterized the tumor microenvironment and established a highly predictive model for evaluating the prognosis of early stage LUSC, which may provide a lead for effective immunotherapeutic options tailored for each subtype.

Whole Transcriptome Analysis: Implication to Estrous Cycle Regulation

Simple Summary

The databases of mRNA and non-coding-RNAs (miRNA, circRNA, lncRNA) in the ovary of Xinong Sannen goat were reported in this study. The differential expression of mRNA and non-coding RNAs were analyzed, and the comprehensive analysis of the four databases provided RNA networks that regulate estrous cycle, which is essential to improve reproduction.

Abstract

Estrous cycle is one of the placental mammal characteristics after sexual maturity, including estrus stage (ES) and diestrus stage (DS). Estrous cycle is important in female physiology and its disorder may lead to diseases, such as polycystic ovary syndrome, ovarian carcinoma, anxiety, and epilepsy. In the latest years, effects of non-coding RNAs and messenger RNA (mRNA) on estrous cycle have started to arouse much concern, however, a whole transcriptome analysis among non-coding RNAs and mRNA has not been reported. Here, we report a whole transcriptome analysis of goat ovary in estrus and diestrus periods. Estrus synchronization was conducted to induce the estrus phase and on day 32, the goats shifted into the diestrus stage. The ovary RNA of estrus and diestrus stages was respectively collected to perform RNA-sequencing. Then, the circular RNA (circRNA), microRNA (miRNA), long non-coding RNA (lncRNA), and mRNA databases of goat ovary were acquired, and the differential expressions between estrus and diestrus stages were screened to construct circRNA-miRNA-mRNA/lncRNA and lncRNA-miRNA/mRNA networks, thus providing potential pathways that are involved in the regulation of estrous cycle. Differentially expressed mRNAs, such as MMP9, TIMP1, 3BHSD, and PTGIS, and differentially expressed miRNAs that play key roles in the regulation of estrous cycle, such as miR-21-3p, miR-202-3p, and miR-223-3p, were extracted from the network. Our data provided the miRNA, circRNA, lncRNA, and mRNA databases of goat ovary and each differentially expressed profile between ES and DS. Networks among differentially expressed miRNAs, circRNAs, lncRNAs, and mRNAs were constructed to provide valuable resources for the study of estrous cycle and related diseases.

Downregulation of FPN1 acts as a prognostic biomarker associated with immune infiltration in lung cancer

Lung cancer morbidity and mortality remain the leading causes of tumor-associated death worldwide. The discovery of early diagnostic and prognostic markers of lung cancer could significantly improve the survival rate and decrease the mortality rate. FPN1 is the only known mammalian iron exporter. However, the molecular and biological functions of FPN1 in lung cancer remain unclear. Here, FPN1 mRNA expression in lung cancer was estimated using the TCGA, Oncomine, TIMER, and UALCAN databases. The prognostic role of FPN1 was evaluated using Kaplan-Meier plotter and PrognoScan. Associations between FPN1 and immune infiltration in lung cancer were evaluated by the TIMER and CIBERSORT algorithms. FPN1 mRNA and protein expressions were significantly downregulated in lung cancer. Low FPN1 expression was strongly related to worse prognosis in patients with lung cancer. GO and KEGG analyses and GSEA suggested that FPN1 was remarkably related to iron homeostasis and immunity. Importantly, FPN1 was remarkably associated with the infiltrating abundance of multiple immune cells. Moreover, FPN1 displayed a strong correlation with various immune marker sets. We investigated the clinical application value of FPN1 and provided a basis for the sensitive diagnosis, prognostication and targeted therapy of lung cancer.

A new insight into subinteractomes of functional antagonists: Thromboxane (CYP5A1) and prostacyclin (CYP8A1) synthases

The current article aims to summarize all possible spectrum of protein-protein interactions for thromboxane A synthase (CYP5A1) and prostacyclin synthase (CYP8A1). These enzymes metabolize the same substrate (prostaglandin H₂ ) and can participate in cardiovascular, inflammatory, immune processes, and apoptosis modulation, as well as significantly influence the risk of cancers. Binary protein-protein and multiprotein complexes are of great importance in enzyme-regulating and signal-transduction pathways. However, protein partners of CYP5A1 and CYP8A1 are not yet fully identified, although both synthases are considered as prospective drug targets. At least 36 novel protein partners of CYP5A1 and CYP8A1 were revealed from different tissue types using an approach based on affinity isolation and mass spectrometry. Enrichment analysis showed that these proteins have different molecular functions: folding (refolding), unfolded protein and chaperon binding, protein transport (export/import), posttranslational modification, protein domain-specific binding, antioxidant activity, and glutathione homeostasis. A significant part of them, belonging to molecular chaperones, were common partners for CYP5A1 and CYP8A1, while other proteins were unique with the tissue-dependent distribution. New aspects of CYP5A1 and CYP8A1 interactomics and hetero-complex formation with different protein partners, including cytochrome P450s are discussed.

Identification of Prognostic Biomarkers and Molecular Targets Among JAK Family in Breast Cancer

Background

Janus kinases (JAKs) are a family of non-receptor tyrosine kinases involved in multiple malignancies. However, clinical values of JAKs as prognostic markers and potential mechanism as molecular targets in breast invasive carcinoma (BC) are not completely clarified.

Methodology

TIMER, UALCAN and GEPIA were used to assess the expression and methylation levels of JAKs in BC. Kaplan–Meier Plotter, bc-GenExMiner, SurvExpress, TRGAted, MethSurv, and SurvivalMeth were used to assess the multilevel prognostic significance of JAKs in breast cancer patients. And cBioPortal, TIMER, STRING, GeneMANIA, NetworkAnalysis, LinkedOmics, DAVID 6.8, and Metascape were applied for multilayer networks and functional enrichment analyses. Correlations between immune cell infiltrates/their gene markers and JAKs were evaluated by TIMER.

Results

We first explored the expression and methylation level of JAKs in breast cancer and found significantly reduced JAK1 and JAK2 expression at mRNA and protein levels, significantly higher JAK3 protein expression, and significantly increased TYK2 expression at mRNA level but decreased at protein level. In addition, hypermethylation of JAK3 and TYK2 and hypomethylation of JAK1 were found in tumor samples. In terms of prognostic values of JAKs in BC patients, low transcriptional levels of JAK1, JAK2, JAK3, and TYK2 indicated worse OS/DMFS/PPS/RFS/DFS, inferior DFS, worse RFS, and shorter OS/DMFS/RFS, respectively. The mRNA signature analysis showed that high-risk group had unfavorable OS/RFS/MFS. Low JAK2 protein level indicated unfavorable DSS/PFS in BC patients. Five CpGs of JAK1, four CpGs of JAK2, 20 CpGs of JAK3, and 13 CpGs of TYK2 were significantly associated with prognosis in BC patients. The DNA methylation signature analysis also suggested worse prognosis in the high-risk group. For potential biological roles of JAKs, interaction analyses, functional enrichment analyses for biological process, cellular component, molecular function, and KEGG pathway analyses of JAKs and their neighbor genes in BC were conducted. Kinase targets, gene–miRNA interactions, and transcription factor–gene interactions of JAKs were also identified. Furthermore, JAKs were found to be significantly related to immune infiltrates as well as the expression levels of multiple immune markers in BC.

Conclusion

JAKs showed multilevel prognostic value and important biological roles in BC. They might serve as promising prognostic markers and possible targets in breast cancer.