The Pyroptosis-Related Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Gastric Cancer

Gastric cancer (GC) is one of the leading causes of cancer-related deaths and shows high levels of heterogeneity. The development of a specific prognostic model is important if we are to improve treatment strategies. Pyroptosis can arise in response to H. pylori, a primary carcinogen, and also in response to chemotherapy drugs. However, the prognostic evaluation of GC to pyroptosis is insufficient. Consensus clustering by pyroptosis-related regulators was used to classify 618 patients with GC from four GEO cohorts. Following Cox regression with differentially expressed genes, our prognosis model (PS-score) was built by LASSO-Cox analysis. The TCGA-STAD cohort was used as the validation set. ESTIMATE, CIBERSORTx, and EPIC were used to investigate the tumor microenvironment (TME). Immunotherapy cohorts by blocking PD1/PD-L1 were used to investigate the treatment response. The subtyping of GC based on pyroptosis-related regulators was able to classify patients according to different clinical traits and TME. The difference between the two subtypes identified in this study was used to develop a prognosis model which we named “PS-score.” The PS-score could predict the prognosis of patients with GC and his/her overall survival time. A low PS-score implies greater inflammatory cell infiltration and better response of immunotherapy by PD1/PD-L1 blockers. Our findings provide a foundation for future research targeting pyroptosis and its immune microenvironment to improve prognosis and responses to immunotherapy.

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Comprehensive Analysis and Prognosis Prediction of N6-methyladenosine-related Lncrnas in Immune Microenvironment Infiltration of Gastric Cancer

10.21203/rs.3.rs-1021150/v1 ◽

2021 ◽

Author(s):

Jianfeng Huang ◽

Wenzheng Chen ◽

Changyu Chen ◽

Tao Xiao ◽

Zhigang Jie

Keyword(s):

Gastric Cancer ◽

Risk Model ◽

Cox Regression ◽

Cytotoxic T Lymphocyte ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Rna Modification ◽

Consensus Clustering ◽

Immune Microenvironment ◽

Cox Regression Analysis

Abstract BackgroundN6-methyladenosine (m6A) RNA modification plays an important role in regulating tumor microenvironment (TME) infiltration. However, the relationship between the expression pattern of m6A-related long non-coding RNAs (lncRNAs) and the immune microenvironment of gastric cancer (GC) is unclear. MethodsIn this study, 23 m6A-related lncRNAs were identified by Pearson’s correlation analysis and univariate Cox regression analysis. According to the expression of these lncRNAs, we identified two distinct molecular clusters by consensus clustering and compared the differences of the TME and enriched pathways between the two clusters. We further constructed a prognostic risk signature and verified it using The Cancer Genome Atlas training and testing cohorts. ResultsThe results showed that cluster 1 was associated with tumor-related and immune activation-related pathways. In addition, cluster 1 was also associated with higher ImmuneScore, StromalScore, and ESTIMATEScore. The results of the stratified survival analysis and independent prognosis analysis indicated that the risk signature is an independent prognostic indicator for patients with GC. In addition, it can effectively predict survival status in patients with different clinical characteristics. Furthermore, our risk model showed that low risk scores were significantly correlated with high expression of programmed death-1 (PD-1) and cytotoxic T-lymphocyte associated protein 4 (CTLA4), as well as sensitivity to chemotherapeutic drugs (e.g., paclitaxel and oxaliplatin). ConclusionsThis evidence contributes to our understanding of the regulation of TME infiltration by m6A-related lncRNAs and my lead to more effective immunotherapy and chemotherapy for patients with GC.

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Identification of stem cell-related subtypes and risk scoring for gastric cancer based on stem genomic profiling

Stem Cell Research & Therapy ◽

10.1186/s13287-021-02633-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Renshen Xiang ◽

Wei Song ◽

Jun Ren ◽

Jing Wu ◽

Jincheng Fu ◽

...

Keyword(s):

Gastric Cancer ◽

Stem Cell ◽

Cell Activation ◽

Cox Regression ◽

Nk Cell ◽

Gene Set Enrichment Analysis ◽

Consensus Clustering ◽

Cox Regression Analysis ◽

Cell Enrichment ◽

Risk Scoring

Abstract Background Although numerous studies demonstrate the role of cancer stem cells in occurrence, recurrence, and distant metastases in gastric cancer (GC), little is known about the evolving genetic and epigenetic changes in the stem and progenitor cells. The purpose of this study was to identify the stem cell subtypes in GC and examine their clinical relevance. Methods Two publicly available datasets were used to identify GC stem cell subtypes, and consensus clustering was performed by unsupervised machine learning methods. The cancer stem cell (CSC) typing-related risk scoring (RS) model was established through multivariate Cox regression analysis. Results Cross-platform dataset-based two stable GC stem cell subtypes, namely low stem cell enrichment (SCE_L) and high stem cell enrichment (SCE_H), were prudently identified. Gene set enrichment analysis revealed that the classical oncogenic pathways, immune-related pathways, and regulation of stem cell division were active in SCE_H; ferroptosis, NK cell activation, and post-mutation repair pathways were active in SCE_L. GC stem cell subtypes could accurately predict clinical outcomes in patients, tumor microenvironment cell-infiltration characteristics, somatic mutation landscape, and potential responses to immunotherapy, targeted therapy, and chemotherapy. Additionally, a CSC typing-related RS model was established; it was strongly independent and could accurately predict the patient’s overall survival. Conclusions This study demonstrated the complex oncogenic mechanisms underlying GC. The findings provide a basis and reference for the diagnosis and treatment of GC.

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Construction of a Novel Signature and Prediction of the Immune Landscape in Soft Tissue Sarcomas Based on N6-Methylandenosine-Related LncRNAs

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.715764 ◽

2021 ◽

Vol 8 ◽

Author(s):

Li Zhang ◽

Xianzhe Tang ◽

Jia Wan ◽

Xianghong Zhang ◽

Tao Zheng ◽

...

Keyword(s):

High Risk ◽

Soft Tissue ◽

Cox Regression ◽

Soft Tissue Sarcomas ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Consensus Clustering ◽

Immune Microenvironment

Background: N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) are critically involved in cancer development. However, the roles and clinical significance of m6A-related lncRNAs in soft tissue sarcomas (STS) are inconclusive, thereby warranting further investigations.Methods: Transcriptome profiling data were extracted from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx). Consensus clustering was employed to divide patients into clusters and Kaplan–Meier analysis was used to explore the prognostic differences between the subgroups. Gene set enrichment analysis (GSEA) was conducted to identify the biological processes and signaling pathways associated with m6A-Related lncRNAs. Finally, patients were randomly divided into training and validation cohorts, and least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to establish the m6A-related lncRNA-based risk signature.Results: A total of 259 STS patients from TCGA-SARC dataset were enrolled in our study. Thirteen m6A-Related lncRNAs were identified to be closely related to the prognosis of STS patients. Patients were divided into two clusters, and patients in cluster 2 had a better overall survival (OS) than those in cluster 1. Patients in different clusters also showed differences in immune scores, infiltrating immune cells, and immune checkpoint expression. Patients were further classified into high-risk and low-risk subgroups according to risk scores, and high-risk patients were found to have a worse prognosis. The receiver operating characteristic (ROC) curve indicated that the risk signature displayed excellent performance at predicting the prognosis of patients with STS. Further, the risk signature was remarkably connected with the immune microenvironment and chemosensitivity in STS.Conclusion: Our study demonstrated that m6A-related lncRNAs were significantly associated with prognosis and tumor immune microenvironment and could function as independent prognosis-specific predictors in STS, thereby providing novel insights into the roles of m6A-related lncRNAs in STS.

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Established fibrous peritoneal metastasis in an immunocompetent mouse model similar to clinical immune microenvironment of gastric cancer

10.21203/rs.3.rs-41049/v3 ◽

2020 ◽

Author(s):

Daisuke Fujimori ◽

Jun Kinoshita ◽

Takahisa Yamaguchi ◽

Yusuke Nakamura ◽

Katsuya Gunjigake ◽

...

Keyword(s):

Gastric Cancer ◽

Mouse Model ◽

Cell Line ◽

Peritoneal Metastasis ◽

Treatment Strategies ◽

M2 Macrophages ◽

Immunocompetent Mouse ◽

Immune Microenvironment ◽

Fibrous Stroma ◽

Immunocompetent Mice

Abstract Background Peritoneal metastasis (PM) in gastric cancer (GC) is characterized by diffusely infiltrating and proliferating cancer cells accompanied by extensive stromal fibrosis in the peritoneal space. The prognosis of GC with PM is still poor regardless of the various current treatments. In order to elucidate the cause of difficulties in PM treatment, we compared the tumor immune microenvironment (TME) in primary and PM lesions in GC. In addition, a PM model with fibrous stroma was constructed using immunocompetent mice to determine whether its TME was similar to that in patients. MethodsImmuno-histochemical analyses of infiltrating immune cells were performed in paired primary and PM lesions from 28 patients with GC. A C57BL/6J mouse model with PM was established using the mouse GC cell line YTN16 either with or without co-inoculation of mouse myofibroblast cell line LmcMF with a-SMA expression. The resected PM from each mouse model was analyzed the immunocompetent cells using immunohistochemistry.ResultsThe number of CD8+ cells was significantly lower in PM lesions than in primary lesions (P<0.01). Conversely, the number of CD163+ cells (M2 macrophages) was significantly higher in PM lesions than in primary lesions (P=0.016). Azan staining revealed that YTN16 and LmcMF co-inoculated tumors were more fibrous than tumor with YTN16 alone (P<0.05). Co-inoculated fibrous tumor also showed an invasive growth pattern and higher progression than tumor with YTN16 alone (P=0.045). Additionally, YTN16 and LmcMF co-inoculated tumors showed lower infiltration of CD8+ cells and higher infiltration of M2 macrophages than tumors with YTN16 alone (P<0.05, P<0.05). These results indicate that LmcMF plays as cancer-associated fibroblasts (CAFs) by crosstalk with YTN16 and CAFs contribute tumor progression, invasion, fibrosis, and immune suppression.ConclusionsThis model is the first immunocompetent mouse model similar to TME of human clinical PM with fibrosis. By using this model, new treatment strategies for PM, such as anti-CAFs therapies, may be developed.

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Development of a Prognostic Signature Based on Hypoxia-related Genes for Thyroid Cancer

10.21203/rs.3.rs-438962/v1 ◽

2021 ◽

Author(s):

HongYang Zhang ◽

Sijia Li ◽

Wei Li

Keyword(s):

Overall Survival ◽

Thyroid Cancer ◽

Prognostic Value ◽

Cox Regression ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Risk Scores ◽

Prognostic Signature ◽

Prognosis Model ◽

Cox Analysis

Abstract Background. We aimed to establish a model to predict the prognosis of patients with thyroid cancer based on differentially expressed hypoxia-related genes.Methods. By comparing the genes in TCGA database and hypoxiaDB database, we obtained differentially expressed genes (DEGs) related to hypoxia in thyroid cancer. Gene function enrichment analysis was performed, and a protein-protein interaction network was constructed using the STRING database. Univariate Cox regression were used to screen hypoxia-related genes with prognostic value. Subsequently, multivariate Cox analysis was used to determine prognostic markers based on thyroid cancer, a prognosis model based on these genes was established. The Kaplan-Meier analysis, Receiver operating characteristic (ROC) analysis and The Harrell’s concordance indexes in the training set and the validation set were used to evaluate the performance of the model. Finally, we conducted univariate analyses of the prognostic value of clinical data (including risk scores) of thyroid cancer patients.Results. 326 hypoxia-related thyroid cancer genes were found. Functional enrichment analysis demonstrated they were mainly involved in regulating biological functions. 23 genes have been proved to be associated with the prognosis of thyroid cancer with univariate Cox regression, among them, 11 marker genes were used to construct a new prognosis model by multivariate Cox analysis. Accordingly, the system of risk scores was constructed, patients with high-risk scores (P <0.005) had shorter overall survival than those with low-risk scores. The ROC curve indicated good performance of the eleven-gene signature at predicting overall survival. The Harrell’s concordance indexes in the internally validated for the 11-gene prognostic signature was 0.881. Moreover, univariate analysis showed that the risk score and age were significantly associated with patient overall survival. The model we created was significantly associated with patient overall survival.Conclusions. The model we established had excellent performance in the prognosis of thyroid cancer.

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Angiogenesis-Related Gene Expression Signatures Predicting Prognosis in Gastric Cancer Patients

Cancers ◽

10.3390/cancers12123685 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3685

Author(s):

Haoyu Ren ◽

Jiang Zhu ◽

Haochen Yu ◽

Alexandr Bazhin ◽

Christoph Westphalen ◽

...

Keyword(s):

Gastric Cancer ◽

Risk Score ◽

Cox Regression ◽

Clinical Stage ◽

External Validation ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Related Gene ◽

Angiogenesis Related Gene

Increasing evidence indicates that angiogenesis is crucial in the development and progression of gastric cancer (GC). This study aimed to develop a prognostic relevant angiogenesis-related gene (ARG) signature and a nomogram. The expression profile of the 36 ARGs and clinical information of 372 GC patients were extracted from The Cancer Genome Atlas (TCGA). Consensus clustering was applied to divide patients into clusters 1 and 2. Least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to identify the survival related ARGs and establish prognostic gene signatures, respectively. The Asian Cancer Research Group (ACRG) (n = 300) was used for external validation. Risk score of ARG signatures was calculated, and a prognostic nomogram was developed. Gene set enrichment analysis of the ARG model risk score was performed. Cluster 2 patients had more advanced clinical stage and shorter survival rates. ARG signatures carried prognostic relevance in both cohorts. Moreover, ARG-risk score was proved as an independent prognostic factor. The predictive value of the nomogram incorporating the risk score and clinicopathological features was superior to tumor, lymph node, metastasis (TNM) staging. The high-risk score group was associated with several cancer and metastasis-related pathways. The present study suggests that ARG-based nomogram could serve as effective prognostic biomarkers and allow a more precise risk stratification.

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Impact of homozygote variant of bleomycin hydrolase (BLMH) gene on survival after chemotherapy for testicular cancer (TC)

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.5022 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 5022-5022

Author(s):

E. C. de Haas ◽

N. Zwart ◽

C. Meijer ◽

H. M. Boezen ◽

G. van der Steege ◽

...

Keyword(s):

Overall Survival ◽

Cox Regression ◽

Patient Characteristics ◽

Cytotoxic Agents ◽

Rank Test ◽

Future Research ◽

Polymorphism Analysis ◽

Kaplan Meier ◽

Response To Chemotherapy ◽

Increased Risk

5022 Background: With bleomycin, etoposide and cisplatin, cure of disseminated TC exceeds 80%. Next to tumor characteristics, response to chemotherapy may be determined by polymorphisms of genes involved in metabolism or target pathways of cytotoxic drugs. We investigated whether the A1450G polymorphic site in the gene for BLMH, an enzyme that inactivates bleomycin, is associated with differences in survival. Methods: Data were collected on survival of non-seminomatous TC patients treated with bleomycin and platinum from 1977–2003. BLMH genotype was determined from genomic DNA by PCR + restriction fragment length polymorphism analysis. The 3 genotypes [AA (wild-type), AG (heterozygote variant) and GG (homozygote variant)] were compared for patient characteristics, prognostic factors and received chemotherapy (Mann-Whitney U or χ2 test) and survival (Kaplan-Meier + log-rank test and Cox regression). Results: Data on BLMH genotype and survival were available for 304/372 patients (82%) with median follow-up of 10 yrs (range 0–27). The 3 genotypes AA (n=140), AG (n=133) and GG (n=31) did not differ significantly with respect to age, IGCCC prognosis, creatinine clearance and received dose of bleomycin and platinum. Overall survival of the GG genotype (61%) was worse than the overall survival of AA and AG combined (83%) (p=0.004), due to worse TC related survival of GG (71%) compared to AA + AG (90%) (p=0.001). Homozygote variants (GG) had a significantly increased risk for TC related death (odds ratio (OR) = 4.97) compared to wildtypes (AA) ( table ). Conclusion: Germline presence of the homozygote variant (GG) of the BLMH gene appears to be an unfavorable prognostic factor for TC related death after chemotherapy, in addition to the commonly used IGCCC prognosis. It is unclear whether this is due to alterations in metabolism or target pathways of bleomycin or other cytotoxic agents, or linkage disequilibrium to a yet unknown involved gene. This needs to be unraveled in future research. [Table: see text] No significant financial relationships to disclose.

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Use of circulating tumor cells to predict response to chemotherapy in patients with advanced gastric cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2011.29.4_suppl.43 ◽

2011 ◽

Vol 29 (4_suppl) ◽

pp. 43-43

Author(s):

S. Matsusaka ◽

K. Chin ◽

N. Mizunuma ◽

M. Ogura ◽

M. Suenaga ◽

...

Keyword(s):

Gastric Cancer ◽

Advanced Gastric Cancer ◽

Tumor Cells ◽

Circulating Tumor Cells ◽

Cox Regression ◽

Performance Status ◽

Univariate Analysis ◽

Eastern Cooperative Oncology Group ◽

Cox Regression Analysis ◽

Response To Chemotherapy

43 Background: The purpose of this study was to quantify circulating tumor cells (CTCs) in advanced gastric cancer (AGC) patients, and to demonstrate the role of CTCs in cancer therapy. The purpose of this study was to identify CTC threshold proposal for determining response to chemotherapy in AGC. Methods: From November 2007 to June 2009, fifty-two patients with AGC were enrolled into a prospective study. All patients were enrolled using institutional review board-approved protocols at the Cancer Institute Hospital and provided informed consent. The study population consisted of patients of aged 18 years or older with histologically proven AGC. Other inclusion criteria were Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 2; adequate organ function. The subjects were five patients treated with S-1 (40 mg/m2, twice daily, days 1-28, repeated every 6 weeks), twenty-six patients treated with S-1 plus CDDP (S-1 40 mg/m2, twice daily, days 1-21, CDDP 60 mg/m2, day 8, repeated every 5 weeks), and twenty-one patients treated with paclitaxel (80 mg/m2, weekly). CTCs of whole blood at baseline, two weeks and four weeks after initiation of chemotherapy, were isolated and enumerated using immunomagnetics. Results: Patients with ≥4 CTCs at two-week points and four-week points had a shorter median PFS (1.4, 1.4 months, respectively), than those with the median PFS of <4 CTCs (4.9, 5.0 months, respectively) (p<0.001, p<0.001, respectively). Patients with ≥4 CTCs at two-week points and four-week points had shorter median OS (3.5, 4.0 months, respectively) than those with the median PFS of <4 CTCs (11.7, 11.4 months, respectively) (p<0.001, p=0.001, respectively). In univariate analysis, PS, treatment regimen, Line of chemotherapy, and CTC levels at 2 weeks and 4 weeks predicted PFS and OS. In order to evaluate the independent predictive effect of chemotherapy, multivariate Cox regression analysis was carried out. CTC levels at 2 weeks and 4 weeks were the strongest predictors. Conclusions: A threshold of lower than 4 CTC/7.5 ml at 2 weeks and 4 weeks was a significant predictor of the outcome for AGC patients treated with S-1 based regimen or paclitaxel regimen. No significant financial relationships to disclose.

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SOURCE: Prediction models for overall survival in patients with metastatic and potentially curable esophageal and gastric cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.4_suppl.301 ◽

2020 ◽

Vol 38 (4_suppl) ◽

pp. 301-301

Author(s):

Héctor G. van den Boorn ◽

Ameen Abu-Hanna ◽

Nadia Haj Mohammad ◽

Maarten C.C.M. Hulshof ◽

Suzanne S. Gisbertz ◽

...

Keyword(s):

Gastric Cancer ◽

Decision Making ◽

Overall Survival ◽

Shared Decision Making ◽

Prediction Models ◽

Cox Regression ◽

Future Research ◽

Shared Decision ◽

Netherlands Cancer Registry ◽

Treatment Type

301 Background: Prediction models in cancer care can provide personalized prediction outcomes and can aid in shared decision making. Existing prediction models for esophageal and gastric cancer (EGC), however, are mostly aimed at predicting survival after a curative treatment has already been completed. The aim of this study is to develop prediction models, called SOURCE, to predict overall survival at diagnosis in potentially curable and metastatic EGC patients. Methods: The data from 12,756 EGC patients diagnosed between 2014-2017 were retrieved from the prospective Netherlands Cancer Registry. Four Cox regression models were created for potentially curable and metastatic cancers of the esophagus and stomach. Predictors, including treatment type, were selected using the Akaike Information Criterion. The models were validated with temporal cross-validation on their concordance-index (c-index) and calibration. Results: The validated model’s c-index is 0.76 for potentially curable cancer. For the metastatic models, the c-indices are 0.71 and 0.70 for esophageal and gastric cancer, respectively. The calibration intercepts and slopes lie in the 95% confidence interval of 0 and 1, respectively. The included model variables are given in Table. Conclusions: The SOURCE prediction models show fair c-indices and an overall good calibration. The models are the first in EGC to include treatment as a predictor. The models predict survival at diagnosis for a variety of treatments and therefore could have a high clinical applicability. Future research is needed to demonstrate the effect on shared decision making in clinical practice. [Table: see text]

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Identification of Iron Metabolism-Related Genes Signature and Novel Role of FLVCR1 in Hepatocellular Carcinoma

10.21203/rs.3.rs-45663/v1 ◽

2020 ◽

Author(s):

Jianhui Chen ◽

Chuan HU ◽

Reguang Pan ◽

Xuedan Du ◽

Haotian Fu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Iron Metabolism ◽

Clustering Analysis ◽

Cox Regression ◽

Treatment Strategies ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Lasso Regression ◽

Prognostic Signature

Abstract Background: Hepatocellular carcinoma (HCC) is the main and highly malignant histological subtype of liver cancer. We tried to construct a novel signature with iron metabolism-related genes to provide new therapeutic targets and improve the prognosis for HCC patients.Methods: The gene expression data of 70 iron metabolism-related genes and its relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Consensus clustering analysis was performed to determine clusters of HCC patients with different OS. Cox regression and LASSO regression analyses were used to establish a prognostic signature. Receiver operating characteristic (ROC) and Kaplan–Meier analyses were carried out to examine the predicated performance of the signature.Results: Consensus clustering analysis determined two clusters of HCC patients with different OS(p<0.01), TNM stage(p<0.05) and pathological grade(p<0.05). A nine-gene prognostic signature established with iron metabolism-related genes can independently predicate the prognostic of HCC patients. The ROC curves showed a great performance of the signature. In addition, FLVCR1, a hub gene with the highest mutation frequency in our signature, showed the significantly prognostic value in HCC patients. High FLVCR1 expression was significantly associated with poor prognosis and aggressive progression in HCC patients. The promoter methylation level of FLVCR1 was lower in HCC samples with aggressive progression status. The FLVCR1 expression was positively correlated with the infiltration level of B cell, CD4+ T cell, macrophage, neutrophil and dendritic cell. Conclusion: Our study first established a signature related to iron metabolism and identified FLVCR1 as a potential therapeutic target. These findings provided more treatment strategies for HCC patients.

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