M2 Macrophage-Based Prognostic Nomogram for Gastric Cancer After Surgical Resection.
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ABSTRACT:
SUBMITTER: Hu J
PROVIDER: S-EPMC8397443 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Gastric cancer is a common cancer afflicting people worldwide. Although incremental progress has been achieved in gastric cancer research, the molecular mechanisms underlying remain unclear. In this study, we conducted bioinformatics methods to identify prognostic marker genes associated with gastric cancer progression. Three hundred and twenty-seven overlapping DEGs were identified from three GEO microarray datasets. Functional enrichment analysis revealed that these DEGs are involved in extracellular matrix organization, tissue development, extracellular matrix-receptor interaction, ECM-receptor interaction, PI3K-Akt signaling pathway, focal adhesion, and protein digestion and absorption. A protein-protein interaction network (PPI) was constructed for the DEGs in which 25 hub genes were obtained. Furthermore, the turquoise module was identified to be significantly positively coexpressed with macrophage M2 infiltration by weighted gene coexpression network analysis (WGCNA). Hub genes of COL1A1, COL4A1, COL12A1, and PDGFRB were overlapped in both PPI hub gene list and the turquoise module with significant association with the prognosis in gastric cancer. Moreover, functional analysis demonstrated that these hub genes play pivotal roles in cancer cell proliferation and invasion. The investigation of the gene markers can help deepen our understanding of the molecular mechanisms of gastric cancer. In addition, these genes may serve as potential prognostic biomarkers for gastric cancer diagnosis.
| S-EPMC8802722 | biostudies-literature
Project description:PurposeSystemic inflammation and nutrition are vital for tumor progression. This study aimed to identify prognostic inflammation nutrition markers and develop a predictive nomogram for gallbladder cancer (GBC).MethodsA total of 123 patients with GBC who underwent surgical resection at the First Affiliated Hospital of Soochow University and Suzhou Kowloon Hospital were included in our study. The final prognostic variables were identified using univariate and multivariate analyses. A nomogram model was then established, and the consistency index (C-index), calibration curves, and Kaplan-Meier analysis were performed to evaluate the accuracy and discrimination of the nomogram. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) suggested that our nomogram had better predictive ability and clinical feasibility than a published model.ResultsThe cox regression analysis showed that carcinoembryonic antigen (CEA) > 4.580, albumin-bilirubin (ALBI) > -2.091, geriatric nutritional risk index (GNRI) < 90.83, T3-T4, and N2 are independent prognostic factors. A predictive nomogram was constructed with a C-index of 0.793. In the calibration curves, the nomogram-predicted 1-, 3-, and 5-year survival matched well with the actual survival. Kaplan-Meier analysis showed that the high-risk group had worse survival than the low-risk group (P < 0.001). Finally, our nomogram achieved better 1-, 3- and 5-year AUCs than an established model (0.871, 0.844, and 0.781 vs. 0.753, 0.750, and 0.693). DCA also confirmed that our model outperformed the established model.ConclusionsIn conclusion, our study revealed that CEA > 4.580, GNRI < 90.83, ALBI > -2.091, T3-T4 stage, and N2 were related to clinical outcomes of patients with GBC after surgical resection. The constructed nomogram has superior predictive ability and clinical practicality.
| S-EPMC11350988 | biostudies-literature
Project description:Objective: We sought to investigate the prognostic significance of body composition and weight change during the first 6 months of adjuvant chemotherapy after R0 resection and develop novel nomograms to accurately predict relapse-free survival (RFS) and overall survival (OS). Methods: This retrospective study included 190 patients who underwent curative radical gastrectomy for gastric cancer and received adjuvant chemotherapy. The changes in weight and body composition including skeletal muscle index (SMI), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) were analyzed for 6 months. LASSO Cox regression and multivariate Cox regression were conducted to evaluate other clinical characteristics, which were used to construct a nomogram for the prediction of 3- and 5-year RFS and OS. The constructed nomogram was subjected to 1,000 resamples bootstrap for internal validation. The Concordance index (C-index) and time-dependent receiver operating characteristic (t-ROC) curves were used to evaluate and compare the discriminative abilities of the new nomograms, non-nutritional nomograms, and pTNM stage. Results: The median follow-up duration was 42.0 (25.2-55.1) months. Factors included in the newly-built nomogram for RFS were pT stage, pN stage, tumor site, tumor size, nerve invasion or not, surgery type, and change of L3SMI, while factors included in the nomogram for OS were pT stage, pN stage, tumor size, nerve invasion or not, surgery type, and change of L3SMI. The C-index and t-ROC indicated that our newly-built nomograms had greater potential to accurately predict prognosis than the non-nutritional nomograms and pTNM stage system. Besides, oral nutritional supplements can reduce the degree of weight and L3SMI loss. Conclusion: Change in skeletal muscle mass during adjuvant chemotherapy can be incorporated into predictive prognostic nomograms for RFS and OS in GC patients after radical resection. Dynamic changes in body composition and weight during adjuvant chemotherapy contribute to the early detection of poor outcomes.
| S-EPMC8572887 | biostudies-literature
Project description:BackgroundThe purpose of this study was to develop a large population-based nomogram incorporating the log odds of positive nodes (LODDS) for predicting the overall survival (OS) of stage II/III rectal cancer patients treated with neoadjuvant chemoradiotherapy (NCRT) followed by surgical resection.MethodsThe Surveillance, Epidemiology, and End Results database was used to collect information on patients diagnosed with stage II/III rectal cancer between 2010 and 2015 and treated with NCRT followed by surgical resection. The Cox regression analyses were performed to determine the independent prognostic factors. In this study, LODDS was employed instead of American Joint Committee on Cancer (AJCC) 7th N stage to determine lymph node status. Then a nomogram integrating independent prognostic factors was developed to predict the 24-, 36-, and 60-month overall survival. The receiver operating characteristic (ROC) curves and calibration curves were used to validate the nomogram. Furthermore, patients were stratified into three risk groups (high-, middle-, and low-risk) based on the total points obtained from the nomogram. And Kaplan-Meier curves were plotted to compare the OS of the three groups.ResultsA total of 3829 patients were included in the study. Race, sex, age, marital status, T stage, tumor grade, tumor size, LODDS, CEA level, and postoperative chemotherapy were identified as independent prognostic factors, based on which the prognostic nomogram was developed. The area under curve values of the nomogram for the 24-, 36-, and 60-month OS in the training cohort were 0.736, 0.720, and 0.688, respectively; and 0.691, 0.696, and 0.694 in the validation cohort, respectively. In both the validation and training cohorts, the calibration curves showed a high degree of consistency between actual and nomogram-predicted survival rates. The Kaplan-Meier curves showed that the three risk groups had significant differences in overall survival (P < 0.001).ConclusionA large population-based nomogram incorporating LODDS was developed to assist in evaluating the prognosis of stage II/III rectal cancer patients treated with NCRT followed by surgical resection. The nomogram showed a satisfactorily discriminative and stable ability to predict the OS for those patients.
| S-EPMC9254567 | biostudies-literature
Project description:High levels of M2 macrophage infiltration invariably contribute to poor cancer prognosis and can be manipulated by metabolic reprogramming in the tumor microenvironment. However, the metabolism-related genes (MRGs) affecting M2 macrophage infiltration and their clinical implications are not fully understood. In this study, we identified 173 MRGs associated with M2 macrophage infiltration in cases of gastric cancer (GC) using the TCGA and GEO databases. Twelve MRGs were eventually adopted as the prognostic signature to develop a risk model. In the high-risk group, the patients showed poorer survival outcomes than patients in the low-risk group. Additionally, the patients in the high-risk group were less sensitive to certain drugs, such as 5-Fluorouracil, Oxaliplatin, and Cisplatin. Risk scores were positively correlated with the infiltration of multiple immune cells, including CD8+ T cells and M2 macrophages. Furthermore, a difference was observed in the expression and distribution between the 12 signature genes in the tumor microenvironment through single-cell sequencing analysis. In vitro experiments proved that the M2 polarization of macrophages was suppressed by Sorcin-knockdown GC cells, thereby hindering the proliferation and migration of GC cells. These findings provide a valuable prognostic signature for evaluating clinical outcomes and corresponding treatment options and identifying potential targets for GC treatment.
| S-EPMC10342140 | biostudies-literature
Project description:ObjectiveAccurate prediction of postoperative recurrence risk of gastric cancer (GC) is critical for individualized precision therapy. We aimed to investigate whether a computed tomography (CT)-based radiomics nomogram can be used as a tool for predicting the local recurrence (LR) of GC after radical resection.Materials and methods342 patients (194 in the training cohort, 78 in the internal validation cohort, and 70 in the external validation cohort) with pathologically proven GC from two centers were included. Radiomics features were extracted from the preoperative CT imaging. The clinical model, radiomics signature, and radiomics nomogram, which incorporated the radiomics signature and independent clinical risk factors, were developed and verified. Furthermore, the performance of these three models was assessed by using the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).ResultsThe radiomics signature, which was comprised of two selected radiomics features, namely, contrast_GLCM and dissimilarity_GLCM, showed better performance than the clinical model in predicting the LR of GC, with AUC values of 0.83 in the training cohort, 0.84 in the internal validation cohort, and 0.73 in the external cohort, respectively. By integrating the independent clinical risk factors (N stage, bile acid duodenogastric reflux and nodular or irregular outer layer of the gastric wall) into the radiomics signature, the radiomics nomogram achieved the highest accuracy in predicting LR, with AUC values of 0.89, 0.89 and 0.80 in the three cohorts, respectively. DCA in the validation cohort showed that radiomics nomogram added more net benefit than the clinical model within the range of 0.01-0.98.ConclusionThe CT-based radiomics nomogram has the potential to predict the LR of GC after radical resection.
| S-EPMC8445075 | biostudies-literature
Project description:Aberrant chemokine stromal cell-derived factor 1 (SDF-1) expression has been shown to be involved in the development and progression of various malignancies. Our present study aims to investigate the clinical and prognostic value of SDF-1 expression and improve risk stratification in patients with gastric cancer. Peritumoral and intratumoral SDF-1 levels were assessed in 220 retrospectively enrolled gastric cancer patients, and their relations with clinicopathological features and clinical outcomes were evaluated. A predictive nomogram was created to refine risk stratification for overall survival of gastric cancer patients. Compared with peritumor tissues, tumor tissues showed decreased SDF-1 expression levels according to TNM stage progression in gastric cancer specimens. Peritumoral SDF-1 expression correlated positively with tumor invasion depth and lymph node metastasis, whereas intratumoral SDF-1 expression associated negatively with tumor size, tumor differentiation, tumor invasion depth, lymph node metastasis, and clinical TNM stage. Moreover, both low peritumoral SDF-1 expression and high intratumoral SDF-1 expression indicated favorable overall survival, and SDF-1 risk derived from the peritumoral/intratumoral SDF-1 expression signature could stratify prognosis of patients with gastric cancer. After backward elimination, SDF-1 risk was identified as an independent prognostic factor for survival. Finally, a predictive nomogram was generated with identified independent prognosticators to assess patient survival at 3 and 5 years following surgery. Conclusively, SDF-1 risk, an identified independent prognostic factor, could be developed into a nomogram with tumor invasion depth, lymph node involvement, and distant metastasis to refine predictive accuracy for survival in patients with gastric cancer after surgical resection.
| S-EPMC4462371 | biostudies-literature
Project description:BackgroundThe biological behavior of early recurrence is more invasive and the prognosis is worse in gastric cancer (GC). The risk of early recurrence (ER) for GC in stage II/III has not been reported of which the majority of GC patients are in China. Therefore, it is necessary to analyze the ER of gastric cancer in stage II/III.MethodsThe medical records of 1511 consecutive stage II/III GC patients who received resections were retrospectively reviewed. They were randomly classified into either a development or validation group at a ratio of 7:3. The nomogram was constructed based on prognostic factors using logistic regression analysis and was validated by bootstrap resampling and validation dataset, respectively. Concordance index (C-index) values and calibration curves were used to evaluate the predictive accuracy and discriminatory capability.ResultsThree hundred eleven patients experienced ER, accounting for 20.58% of the GC patients investigated. Multivariate logistic regression analysis identified tumors located at upper, middle third, or mixed, a positive lymph node ratio ≥ 0.335, pTNM stage III, lymphocyte count < 1.5 × 109/L, postoperative infection complications and adjuvant chemotherapy < 6 cycles were all independent predictors for ER after curative resection of stage II/III GC. The C-index value obtained for the model was 0.780 (95% CI, 0.747-0.813), and the calibration curves of validation group yielded a C-index value of 0.739 (95% CI, 0.684-0.794), suggesting the practicability of the model.ConclusionsThe nomogram which was developed for predicting ER of stage II/III GC after surgery had good accuracy and was verified through both internal and external validation. The nomogram established can assist clinicians in determining the optimal therapy strategies in counseling, adjuvant treatments, and subsequent follow-up planning.
| S-EPMC6923869 | biostudies-literature
Project description:PurposeThe exploration and interpretation of DNA methylation-driven genes might contribute to molecular classification, prognostic prediction and therapeutic choice. In this study, we built a prognostic risk model via integrating analysis of the transcriptome and methylation profile for patients with gastric cancer (GC).MethodsThe mRNA expression profiles, DNA methylation profiles and corresponding clinicopathological information of 415 GC patients were downloaded from The Cancer Genome Atlas (TCGA). Differential expression and correlation analysis were performed to identify DNA methylation-driven genes. The candidate genes were selected by univariate Cox regression analyses followed by the least absolute shrinkage and selection operator (LASSO) regression. A prognostic risk nomogram model was then built together with clinicopathological parameters.Results5 DNA methylation-driven genes (CXCL3, F5, GNAI1, GAMT and GHR) were identified by integrated analyses and selected to construct the prognostic risk model with clinicopathological parameters. High expression and low DNA hypermethylation of F5, GNAI1, GAMT and GHR, as well as low expression and high DNA hypomethylation of CXCL3 were significantly associated with poor prognosis rates, respectively. The high-risk group showed a significantly shorter prognosis than the low-risk group in the TCGA dataset (HR = 0.212, 95% CI = 0.139-0.322, P = 2e-15). The final nomogram model showed high predictive efficiency and consistency in the training and validation group.ConclusionWe construct and validate a prognostic nomogram model for GC based on five DNA methylation-driven genes with high performance and stability. This nomogram model might be a powerful tool for prognosis evaluation in the clinic and also provided novel insights into the epigenetics in GC.
| S-EPMC7732649 | biostudies-literature
Project description:Resident macrophages in the tumor microenvironment exert a dual role in tumor progression. So far, the mechanism of intratumoral macrophage generation is still largely unknown. In the present study, the importance of macrophages in the pro-tumor role of gastric cancer-derived mesenchymal stromal cells (GC-MSCs) was observed in a mouse xenograft model with macrophage depletion. In gastric cancer tissues, high expression levels of Ym-1, Fizz-1, arginase-1, and CCR-2, as well as a low expression level of iNOS, were verified, and co-localization of GC-MSCs and tumor-associated macrophages (TAMs) was observed by dual immunofluorescence histochemistry. TAMs isolated from gastric cancer tissues predominantly displayed an M2 phenotype. In a co-culture system, the contribution of GC-MSCs to M2 polarization of macrophages was confirmed by the M2-related protein expression, M2-like immunophenotype and cytokine profile of GC-MSC-primed macrophages in vitro. Blockade of IL-6/IL-8 by neutralizing antibodies significantly attenuated the promoting effect of GC-MSCs on M2-like macrophage polarization via the JAK2/STAT3 signaling pathway. In addition, GC-MSC-primed macrophages promoted the migration and invasion of gastric cancer cells, and the process of EMT in gastric cancer cells was significantly enhanced by GC-MSC-primed macrophage treatment. Our study showed that tumor-promoting GC-MSCs contribute to M2 macrophage polarization within the gastric cancer niche through considerable secretion of IL-6 and IL-8. These GC-MSC-primed macrophages can subsequently prompt gastric cancer metastasis via EMT promotion in gastric cancer cells.
| S-EPMC6892854 | biostudies-literature