Effect of Age on Prognosis of Gastric Signet-Ring Cell Carcinoma: A SEER Database Analysis

A novel web-based dynamic prognostic nomogram for gastric signet ring cell carcinoma: a multicenter population-based study

Background

Gastric signet ring cell carcinoma (GSRCC) is a rare and highly malignant disease with a poor prognosis. To assess the overall survival (OS) and cancer-specific survival (CSS) of patients with GSRCC, prognostic nomograms were developed and validated using common clinical factors.

Methods

This retrospective cohort study included patients diagnosed with GSRCC between 2011 and 2018 from the National Cancer Center (n = 1453) and SEER databases (n = 2745). Prognostic nomograms were established by identifying independent prognostic factors using univariate and multivariate Cox regression analyses. The calibration curve and C-index were used to assess the predictions. The clinical usefulness of the survival prediction model was further evaluated using the DCA and ROC curves. The models were internally validated in the training cohort and externally validated in the validation cohort. Two web servers were created to make the nomogram easier to use.

Results

Patients with GSRCC were divided into training (n = 2938) and validation (n = 1260) cohorts. The nomograms incorporated six predictors: age, race, tumor site, tumor size, N stage, T stage, and AJCC stage. Excellent agreement was observed between the internal and exterior calibration plots for the GSRCC survival estimates. The C-index and area under the ROC curve were roughly greater than 0.7. Both nomograms had adequate clinical efficacy, as demonstrated by the DCA plots. Furthermore, we developed a dynamic web application utilizing the constructed nomograms available at https://jiangyujuan.shinyapps.io/OS-nomogram/ and https://jiangyujuan.shinyapps.io/DynNomapp-DFS/.

Conclusion

We developed web-based dynamic nomograms utilizing six independent prognostic variables that assist physicians in estimating the OS and CSS of patients with GSRCC.

Development and validation of a novel nomogram for postoperative overall survival of patients with primary gastric signet-ring cell carcinoma: a population study based on SEER database

BACKGROUND

Gastric signet ring cell carcinoma (GSRCC) is a highly malignant subtype of gastric cancer. We tried to establish and validate a nomogram using common clinical variables to achieve more personalized management.

METHODS

We analyzed patients with GSRCC in the Surveillance, Epidemiology, and End Results database between 2004 and 2017. The survival curve was calculated by the Kaplan-Meier method, and the difference in survival curve was tested by log-rank test. We used the cox proportional hazard model to evaluate independent factors of prognosis, and established a nomogram to predict 1-, 3- and 5- overall survival (OS). Harrell's consistency index and calibration curve were used to measure the discrimination and calibration of the nomogram. In addition, we used decision curve analysis (DCA) to compare the net clinical benefits of the nomogram and American Joint Committee on Cancer (AJCC) staging system.

RESULTS

The prognosis nomogram predicting 1-, 3- and 5-years OS for patients with GSRCC is established for the first time. The C-index and AUC of nomogram were higher than that of the American Joint Committee on Cancer (AJCC) staging system in the training set. Our model also shows better performance than the AJCC staging system in the validation set, and importantly, DCA shows that our model has a better net benefit than the AJCC stage.

CONCLUSIONS

We have developed and validated a new nomogram and risk classification system, which is better than the AJCC staging system. It will help clinicians manage postoperative patients with GSRCC more accurately.

Artificial intelligence annotated clinical-pathologic risk model to predict outcomes of advanced gastric cancer

Background

Gastric cancer with synchronous distant metastases indicates a dismal prognosis. The success in survival improvement mainly relies on our ability to predict the potential benefit of a therapy. Our objective is to develop an artificial intelligence annotated clinical-pathologic risk model to predict its outcomes.

Methods

In participants (n=47553) with gastric cancer of the surveillance, epidemiology, and end results program, we selected patients with distant metastases at first diagnosis, complete clinical-pathologic data and follow-up information. Patients were randomly divided into the training and test cohort at 7:3 ratio. 93 patients with advanced gastric cancer from six other cancer centers were collected as the external validation cohort. Multivariable analysis was used to identify the prognosis-related clinical-pathologic features. Then a survival prediction model was established and validated. Importantly, we provided explanations to the prediction with artificial intelligence SHAP (Shapley additive explanations) method. We also provide novel insights into treatment options.

Results

Data from a total 2549 patients were included in model development and internal test (median age, 61 years [range, 53-69 years]; 1725 [67.7%] male). Data from an additional 93 patients were collected as the external validation cohort (median age, 59 years [range, 48-66 years]; 51 [54.8%] male). The clinical-pathologic model achieved a consistently high accuracy for predicting prognosis in the training (C-index: 0.705 [range, 0.690-0.720]), test (C-index: 0.737 [range, 0.717-0.757]), and external validation (C-index: 0.694 [range, 0.562-0.826]) cohorts. Shapley values indicated that undergoing surgery, chemotherapy, young, absence of lung metastases and well differentiated were the top 5 contributors to the high likelihood of survival. A combination of surgery and chemotherapy had the greatest benefit. However, aggressive treatment did not equate to a survival benefit. SHAP dependence plots demonstrated insightful nonlinear interactive associations among predictors in survival benefit prediction. For example, patients who were elderly, or poor differentiated, or presence of lung or bone metastases had a worse prognosis if they undergo surgery or chemotherapy, while patients with metastases to liver alone seemed to gain benefit from surgery and chemotherapy.

Conclusion

In this large multicenter cohort study, we developed an artificial intelligence annotated clinical-pathologic risk model to predict outcomes of advanced gastric cancer. It could be used to discuss treatment options.

A population-based predictive model to identify patients with signet ring cell carcinoma of the stomach who are most suitable for primary tumor resection

Background

Though the survival benefit of primary tumor operation for patients with signet ring cell carcinoma of the stomach is known, the specific characteristics of those patients who would profit from the operation are yet to be determined. To this end, a predictive model was developed to identify the conjecture that the survival profit from primary tumor operation would only be obtained by patients.

Method

The clinical data of the patients with signet ring cell carcinoma of the stomach were obtained from the Surveillance, Epidemiology, and End Results database, and then divided into operation and no-operation groups based on whether the patients underwent the primary tumor operation. To remove the confounding factors, propensity score matching was employed, and it was hypothesized that the patients who had been operated on and lived a longer life than the median cancer-specific survival time of those who hadn’t must have profited from the surgery. To discuss the independent factors of cancer-specific survival time in the beneficial group and the non-beneficial group, the Cox model was used, and based on the various vital predictive factors, a nomogram was drawn using logistic regression.

Result

The number of eligible patients was 12,484, with 43.9% (5483) of them having received surgery. After employing propensity score matching, the cancer-specific survival time of the operation group was found to be apparently longer (median: 21 vs. 5 months; p < 0.001) than the no-operation group. In the operation group, 4757 (86.7%) of the patients lived longer than five months (beneficial group). The six indexes (beneficial and non-beneficial group) included gender, age, Tumor Node Metastasis stage, histologic type, differentiation grade, and tumor position, and were used as predictors to draw the nomogram. The nomogram was used to divide the patients who had taken operations into two groups: the beneficial operation group and the non-beneficial operation group. The beneficial operation group, it was found, survived longer than the non-beneficial operation group (median cancer-specific survival time: 28 vs. 3 months, p < 0.001). Moreover, there was we could tell little difference in survival between the two groups (median cancer-specific survival time: 3 vs. 5 months).

Conclusions

The predictive model created to select suitable candidates for surgical treatment from patients with signet ring carcinoma of the stomach could be adopted to identify certain patients benefiting from the primary tumor operation.

Prognostic Nomogram for Predicting Overall Survival of Solitary Bone Plasmacytoma Patients: A Large Population-Based Study

Background

The aim of the study was to develop a nomogram to predict the overall survival (OS) of patients with solitary plasmacytoma of bone (SBP).

Materials and Methods

Patients diagnosed with SBP between 1993 and 2012 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. All eligible patients were randomly allocated to the training sets and the validation sets. The nomogram was developed with the training set and validated with the validation set using the concordance index (C-index), calibration plots, and decision curve analyses (DCA).

Results

Age, marital status, tumor grade, treatment were independent prognostic indicators for OS (P<0.05) and were integrated to construct the nomogram. C-indexes for OS prediction in the training and validation sets were 0.78 and 0.73, respectively. The calibration plots demonstrated good consistency between the predicted and actual survival. DCA demonstrated that the new model has great benefits. In the total cohort, the median OS of patients in the low- and high-risk groups were 12.17 (95% CI 11.92–12.42) and 3.92 (95% CI 2.83–5.01) years, respectively.

Conclusion

The nomogram showed excellent applicability and accuracy, which could be a reliable tool for predicting OS in SBP patients.

Nomograms to Predict Overall and Cancer-Specific Survival in Gastric Signet-Ring Cell Carcinoma

BACKGROUND

The objective of our study was to develop and validate nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with signet-ring cell carcinoma (SRCC) of the stomach.

METHODS

Data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. A total of 1781 patients were randomly allocated to a training set (n = 1335) and a validation set (n = 446). Univariate and multivariate analyses were used to determine the prognostic effect of variables. Nomograms were developed to estimate OS and CSS and assessed using the concordance index (C-index), calibration curves, receiver operating characteristic (ROC), and decision curve analyses (DCA). DCA was utilized to compare the nomograms and the Tumor-Node-Metastasis (TNM) staging system.

RESULTS

Age, race, tumor size, T, N, M stage, and use of surgery and/or radiotherapy were included in the nomograms. C-indexes for OS and CSS were 0.74 and 0.75 in the training set, respectively. C-indexes for OS and CSS were 0.76 and 0.76 in the validation set. Calibration plots and receiver operating characteristic (ROC) curves showed good predictive accuracy. According to the decision curve analyses (DCA), the new model was more useful than the TNM staging system.

CONCLUSIONS

We developed nomograms to predict OS and CSS in patients with SRCC of the stomach. Nomograms may be a valuable clinical supplement of the conventional TNM staging system.

Is there still a place for conventional histopathology in the age of molecular medicine? Laurén classification, inflammatory infiltration and other current topics in gastric cancer diagnosis and prognosis

Gastric cancer (GC) is the fifth most common cancer and the third cause of cancer-related deaths worldwide. In western countries, more than half of GC patients are diagnosed at advanced stages and 5-year survival rates range between 20-30%. The only curative treatment is surgery, and despite recent advances in oncological therapies, GC prognosis is still poor. The main prognostic tool for patient categorization and treatment selection is the TNM classification, but its limitations are being increasingly recognized. Early recurrences may occur in early-stage disease, and patients at the same stage show heterogeneous outcomes. Thus, there is a need to improve GC stratification and to identify new prognostic factors, which may allow us to select drug-susceptible populations, refine patient grouping for clinical trials and discover new therapeutic targets. Molecular classifications have been developed, but they have not been translated to the clinical practice. On the other hand, histological assessment is cheap and widely available, and it is still a mainstay in the era of molecular medicine. Furthermore, histological features are acquiring new roles as reflectors of the genotype-phenotype correlation, and their potential impact on patient management is currently being analyzed. The aim of this literature review is to provide a modern overview of the histological assessment of GC. In this study, we discuss recent topics on the histological diagnosis of GC, focusing on the current role of Laurén classification and the potential value of new histological features in GC, such as inflammatory infiltration and tumor budding.

The Clinicopathological Characteristics And Genetic Alterations of Signet-ring Cell Carcinoma in Gastric Cancer

Signet-ring cell carcinoma (SRC) in advanced gastric cancer (GC) is often associated with more invasiveness and a worse prognosis than other cell types. The genetic alterations associated with gastric carcinogenesis in SRC are still unclear. In this study, 441 GC patients receiving curative surgery for GC between 2005 and 2013 were enrolled. The clinicopathological characteristics and genetic alterations of GC patients with and without SRC were compared. Among the 441 GC patients, 181 had SRC. For early GC, patients with SRC had more tumors located in the middle and lower stomach, more infiltrating tumors and better overall survival (OS) rates than those without SRC. For advanced GC, patients with SRC had more scirrhous type tumors, more PIK3CA amplifications, fewer microsatellite instability-high (MSI-H) tumors, more peritoneal recurrences and worse 5-year OS rates than those without SRC. For advanced GC with SRC, patients with peritoneal recurrence tended to have PD-L1 expression. For advanced GC without SRC, patients with liver metastasis tended to have PD-L1 expression, PI3K/AKT pathway mutations, TP53 mutations and MSI-H tumors. For advanced GC, PD-L1 expression was associated with peritoneal recurrence in SRC tumors, while non-SRC tumors with liver metastasis were likely to have PI3K/AKT pathway mutations, TP53 mutations and PD-L1 expression; immunotherapy and targeted therapy may be beneficial for these patients.

Identification of key genes and pathways in gastric signet ring cell carcinoma based on transcriptome analysis

BACKGROUND

Gastric signet ring cell carcinoma (GSRCC) is one of the most malignant tumors. It has the features of high invasiveness, rapid progression, and resistance to chemotherapy. However, systematic analyses of mRNAs have not yet been performed for GSRCC.

AIM

To identify key mRNAs and signaling pathways in GSRCC.

METHODS

A transcriptome analysis of two GSRCC and two non-GSRCC samples was performed in this study. Differentially expressed mRNAs and pathways were identified based on the KEGG and PANTHER pathway annotations. The interactive relationships among the differential genes were mapped with the STRING database. Quantitative real-time polymerase chain reaction was used to validate the key gene expression in GSRCC.

RESULTS

About 1162 differential genes (using a 2-fold cutoff, P < 0.05) were identified in GSRCC compared with non-GSRCC. The enriched KEGG and PANTHER pathways for the differential genes included immune response pathways, metabolic pathways, and metastasis-associated pathways. Ten genes (MAGEA2, MAGEA2B, MAGEA3, MAGEA4, MAGEA6, MUC13, GUCA2A, FFAR4, REG1A, and REG1B) were identified as hub genes in the protein-protein interaction network. The expression levels of five genes (MAGEA2, MAGEA3, MAGEA4, MAGEA6, and REG1B) showed potential clinical value.

CONCLUSION

We have identified the potential key genes and pathways in GSRCC, and these hub genes and pathways could be diagnostic markers and therapeutic targets for GSRCC.

Development and validation of prognostic nomogram based on log odds of positive lymph nodes for patients with gastric signet ring cell carcinoma

Objective

Our aims were to establish novel nomogram models, which directly targeted patients with signet ring cell carcinoma (SRC), for individualized prediction of overall survival (OS) rate and cancer-specific survival (CSS).

Methods

We selected 1,365 SRC patients diagnosed from 2010 to 2015 from Surveillance, Epidemiology and End Results (SEER) database, and then randomly partitioned them into a training cohort and a validation cohort. Independent predicted indicators, which were identified by using univariate testing and multivariate analyses, were used to construct our prognostic nomogram models. Three methods, Harrell concordance index (C-index), receiver operating characteristics (ROC) curve and calibration curve, were used to assess the ability of discrimination and predictive accuracy. Integrated discrimination improvement (IDI), net reclassification improvement (NRI) and decision curve analysis (DCA) were used to assess clinical utility of our nomogram models.

Results

Six independent predicted indicators, age, race, log odds of positive lymph nodes (LODDS), T stage, M stage and tumor size, were associated with OS rate. Nevertheless, only five independent predicted indicators were associated with CSS except race. The developed nomograms based on those independent predicted factors showed reliable discrimination. C-index of our nomogram for OS and CSS was 0.760 and 0.763, which were higher than American Joint Committee on Cancer (AJCC) 8th edition tumor-node-metastasis (TNM) staging system (0.734 and 0.741, respectively). C-index of validation cohort for OS was 0.757 and for CSS was 0.773. The calibration curves also performed good consistency. IDI, NRI and DCA showed the nomograms for both OS and CSS had a comparable clinical utility than the TNM staging system.

Conclusions

The novel nomogram models based on LODDS provided satisfying predictive ability of SRC both in OS and CSS than AJCC 8th edition TNM staging system alone.

Postoperative Radiotherapy Improves Survival in Gastric Signet-Ring Cell Carcinoma: a SEER Database Analysis

Purpose

To identify the potential therapeutic role of postoperative radiotherapy (RT) in patients with locally advanced (stage II and stage III) gastric signet ring cell carcinoma (SRC).

Materials and methods

Patients with locally advanced gastric SRC from the Surveillance, Epidemiology, and End Results program database between 2004 and 2012 were included in our study. Univariate and multivariate Cox proportional models were performed, and survival curves were generated to evaluate the prognostic effect of postoperative RT and surgery alone on SRC patients. Propensity score matching (PSM) was used to avoid selection bias among the study cohorts.

Results

We found that patients with postoperative RT had better probability of survival compared with those who did not receive RT (overall survival [OS], P<0.001; cancer-specific survival [CSS], P<0.001). After PSM, analysis of both overall and CSS showed that patients who underwent postoperative RT had better prognosis than those receiving surgery alone in the matched cohort (OS, P=0.00079; CSS, P=0.0036). Multivariate Cox proportional model indicated that postoperative RT had better effect on prognosis compared with surgery alone with respect to both overall (hazard ratio [HR], 0.716; 95% confidence interval [95% CI], 0.590–0.87; P=0.001) and CSS (HR, 0.713; 95% CI, 0.570–0.890; P=0.003).

Conclusions

Postoperative RT had better prognosis compared with surgery alone for both overall and CSS for patients with locally advanced gastric SRC.

Identification of Key Genes and Circular RNAs in Human Gastric Cancer

Background

Globally, gastric cancer (GC) is the third most common source of cancer-associated mortality. The aim of this study was to identify key genes and circular RNAs (circRNAs) in GC diagnosis, prognosis, and therapy and to further explore the potential molecular mechanisms of GC.

Material/Methods

Differentially expressed genes (DEGs) and circRNAs (DE circRNAs) between GC tissues and adjacent non-tumor tissues were identified from 3 mRNA and 3 circRNA expression profiles. Functional analyses were performed, and protein–protein interaction (PPI) networks were constructed. The significant modules and key genes in the PPI networks were identified. Kaplan-Meier analysis was performed to evaluate the prognostic value of these key genes. Potential miRNA-binding sites of the DE circRNAs and target genes of these miRNAs were predicted and used to construct DE circRNA–miRNA–mRNA networks.

Results

A total of 196 upregulated and 311 downregulated genes were identified in GC. The results of functional analysis showed that these DEGs were significantly enriched in a variety of functions and pathways, including extracellular matrix-related pathways. Ten hub genes (COL1A1, COL3A1, COL1A2, COL5A2, FN1, THBS1, COL5A1, SPARC, COL18A1, and COL11A1) were identified via PPI network analysis. Kaplan-Meier analysis revealed that 7 of these were associated with a poor overall survival in GC patients. Furthermore, we identified 2 DE circRNAs, hsa_circ_0000332 and hsa_circ_0021087. To reveal the potential molecular mechanisms of circRNAs in GC, DE circRNA–microRNA–mRNA networks were constructed.

Conclusions

Key candidate genes and circRNAs were identified, and novel PPI and circRNA–microRNA–mRNA networks in GC were constructed. These may provide useful information for the exploration of potential biomarkers and targets for the diagnosis, prognosis, and therapy of GC.

Survival Analysis of Cancer Patients in North Eastern Nigeria from 2004 - 2017 - A Kaplan - Meier Method

BACKGROUND:

Cancer is a deadly malignant disease and is prevalent in Sub Saharan Africa. The North East part of Nigeria in particular and the country, in general, are struggling to cope with the increasing burden of cancer and other communicable and non-communicable diseases. The situation is worsened by the ongoing insurgency and terrorist activities in the area.

AIM:

The aim of this paper is to present the research findings from a cohort study aimed at the analysis of the estimation of the survivorship time of the real data of cancer patients in the North-eastern part of Nigeria and to establish if the insurgency in the region has contributed negatively to the life expectancy of its inhabitants.

MATERIAL AND METHODS:

The record of 1,090 patients from medical records departments of the University of Maiduguri Teaching Hospital (UMTH), located in Maiduguri, the capital city of Borno State in northeast Nigeria was obtained. The record showed patients that were diagnosed and died of one type of cancer or the other from 2004 to 2017. All the cancer cases included in the present study were grouped into sex, age, marital status, occupation, date admitted and date of death/discharge. Descriptive statistics and Kaplan-Meier method were used to analyse the data using SPSS version 23 while Microsoft EXCEL and Minitab 16.0 were used for data cleansing and organisation.

RESULTS:

Of the 1,090 patients analysed, 920 (84.40%) experienced the event, i.e. death, while 170 (15.60%) patients were censored. The data were analysed based on the ages and sex of the patients. 50.20% of the patients were of ages 21-50 years. The proportions of patients in this age bracket surviving past 7 days are 75%, while those between ages 80 years and above is 12 days. Others are of survival time of 5 days (ages 0-20 years) and 7 days (51-79 years). Using sex, 75% of the patients’ survival time is 7 days in the case of male and 6 days for females. It is safe to say that the survival time for cancer patients of the university the Maiduguri is 6 days and the result reflects the Northeastern part of Nigeria. This is because the hospital is one of few tertiary healthcare facilities in that area and consequently, cancer cases are often referred there.

CONCLUSION:

Cancer incidence is high, and the probability of survival reduces as the survival time increases. This is a dire situation in need of urgent intervention from the government, groups and individuals to tackle the scourge of cancer, thereby improving on the life expectancy battered by the ongoing Boko Haram insurgency in that region.