Development and validation of a novel nomogram to predict overall survival in gastric cancer with lymph node metastasis

A web-based prediction model for long-term cancer-specific survival of middle-aged patients with early-stage gastric cancer: a multi-institutional retrospective study

BACKGROUND

This study constructed and validated a prognostic model to evaluate long-term cancer-specific survival (CSS) in middle-aged patients with early gastric cancer (EGC).

METHODS

We extracted clinicopathological data from relevant patients between 2004 and 2015 from Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided the patients into a training group (N = 688) and a validation group (N = 292). In addition, 102 Chinese patients were enrolled for external validation. Univariate and multivariate Cox regression models were used to screen for independent prognostic factors, and a nomogram was constructed to predict CSS. We used the concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the predictive performance of the model.

RESULTS

Univariate and multivariate COX regression analyses showed that tumor location, differentiation grade, N stage, chemotherapy, and number of regional nodes examined were independent risk factors for prognosis, and these factors were used to construct the nomogram. The C-index of the model in the training cohort, internal validation cohort, and external validation cohort was 0.749 (95% CI 0.699-0.798), 0.744 (95% CI 0.671-0.818), and 0.807 (95% CI 0.721-0.893), respectively. The calibration curve showed that the model had an excellent fit. The DCA curve showed that the model had good predictive performance and practical clinical value.

CONCLUSION

This study developed and validated a new nomogram to predict CSS in middle-aged patients with EGC. The prediction model has unique and practical value and can help doctors carry out individualized treatment and judge prognosis.

miR-21 Targets ASPP2 to Inhibit Apoptosis via CHOP-Mediated Signaling in Helicobacter pylori-Infected Gastric Cancer Cells

Helicobacter pylori (H. pylori) infection affects cell survival pathways, including apoptosis and proliferation in host cells, and disruption of this balance is the key event in the development of H. pylori-induced gastric cancer (HPGC). H. pylori infection induces alterations in microRNAs expression that may be involved in GC development. Bioinformatic analysis showed that microRNA-21 (miR-21) is significantly upregulated in HPGC. Furthermore, quantitative proteomics and in silico prediction were employed to identify potential targets of miR-21. Following functional enrichment and clustered interaction network analyses, five candidates of miR-21 targets, PDCD4, ASPP2, DAXX, PIK3R1, and MAP3K1, were found across three functional clusters in association with cell death and survival, cellular movement, and cellular growth and proliferation. ASPP2 is inhibited by H. pylori-induced miR-21 overexpression. Moreover, ASPP2 levels are inversely correlated with miR-21 levels in HPGC tumor tissues. Thus, ASPP2 was identified as a miR-21 target in HPGC. Here, we observed that H. pylori-induced ASPP2 suppression enhances resistance to apoptosis in GC cells using apoptosis assays. Using protein interaction network and coimmunoprecipitation assay, we identified CHOP as a direct mediator of the ASPP2 proapoptotic activity in H. pylori-infected GC cells. Mechanistically, ASPP2 suppression promotes p300-mediated CHOP degradation, in turn inhibiting CHOP-mediated transcription of Noxa, Bak, and suppression of Bcl-2 to enact antiapoptosis in the GC cells after H. pylori infection. Clinicopathological analysis revealed correlations between decreased ASPP2 expression and higher HPGC risk and poor prognosis. In summary, the discovery of H. pylori-induced antiapoptosis via miR-21-mediated suppression of ASPP2/CHOP-mediated signaling provides a novel perspective for developing HPGC management and treatment.

A novel prognostic model for patients with colon adenocarcinoma

BACKGROUND

Colon adenocarcinoma (COAD) is a highly heterogeneous disease, which makes its prognostic prediction challenging. The purpose of this study was to investigate the clinical epidemiological characteristics, prognostic factors, and survival outcomes of patients with COAD in order to establish and validate a predictive clinical model (nomogram) for these patients.

METHODS

Using the SEER (Surveillance, Epidemiology, and End Results) database, we identified patients diagnosed with COAD between 1983 and 2015. Disease-specific survival (DSS) and overall survival (OS) were assessed using the log-rank test and Kaplan-Meier approach. Univariate and multivariate analyses were performed using Cox regression, which identified the independent prognostic factors for OS and DSS. The nomograms constructed to predict OS were based on these independent prognostic factors. The predictive ability of the nomograms was assessed using receiver operating characteristic (ROC) curves and calibration plots, while accuracy was assessed using decision curve analysis (DCA). Clinical utility was evaluated with a clinical impact curve (CIC).

RESULTS

A total of 104,933 patients were identified to have COAD, including 31,479 women and 73,454 men. The follow-up study duration ranged from 22 to 88 months, with an average of 46 months. Multivariate Cox regression analysis revealed that age, gender, race, site_recode_ICD, grade, CS_tumor_size, CS_extension, and metastasis were independent prognostic factors. Nomograms were constructed to predict the probability of 1-, 3-, and 5-year OS and DSS. The concordance index (C-index) and calibration plots showed that the established nomograms had robust predictive ability. The clinical decision chart (from the DCA) and the clinical impact chart (from the CIC) showed good predictive accuracy and clinical utility.

CONCLUSION

In this study, a nomogram model for predicting the individualized survival probability of patients with COAD was constructed and validated. The nomograms of patients with COAD were accurate for predicting the 1-, 3-, and 5-year DSS. This study has great significance for clinical treatments. It also provides guidance for further prospective follow-up studies.

A novel prognostic model to predict OS and DFS of stage II/III gastric adenocarcinoma patients in China

Background

The prognosis of advanced gastric adenocarcinoma (GAC) after radical gastrectomy varies greatly. We aimed to build and validate a novel individualized nomogram based on inflammation index and tumor markers for patients with stage II/III GAC.

Methods

A total of 755 individuals with stage II/III GAC who had undergone radical gastrectomy at the First Affiliated Hospital of Zhengzhou University between 2012 and 2017 were included in this retrospective study. The patients were randomly divided into a training cohort (n ​= ​503) and a validation cohort (n ​= ​252). Univariate and multivariate analyses were used to determine independent prognostic factors of overall survival (OS) and disease-free survival (DFS). A nomogram was developed based on these independent factors. The concordance index (C-index) and calibration curves were used to evaluate the predictive accuracy of the nomogram.

Results

Univariate and multivariate analyses demonstrated that older age, poor differentiation, advanced stage, elevated neutrophil-to-lymphocyte ratio (NLR), lower hemoglobin, and high carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) levels were significantly associated with lower OS and DFS and were independent prognostic factors in stage II/III GAC. The nomogram developed based on these factors in the training cohort showed excellent calibration and discrimination (OS: C-index ​= ​0.739, 95% CI ​= ​0.706-0.772; DFS: C-index ​= ​0.735, 95% CI ​= ​0.702-0.769). In the internal validation cohort, the nomogram was also well-calibrated for the prediction of OS and DFS; it was superior to the 8th edition UICC/AJCC TNM staging system (for OS: C-index ​= ​0.746 vs. 0.679, respectively; for DFS: C-index ​= ​0.736 vs. 0.675, respectively; P ​< ​0.001).

Conclusion

The nomogram model could reliably predict OS and DFS in stage II/III gastric cancer patients with radical gastrectomy. It may help physicians make better treatment decisions.

Efficacy of indocyanine green fluorescence imaging-guided lymphadenectomy in radical gastrectomy for gastric cancer: A systematic review and meta-analysis

Background

Indocyanine green (ICG) imaging-guided lymphadenectomy has been introduced in gastric cancer (GC) surgery and its clinical value remains controversial. The aim of this study is to evaluate the efficacy of ICG fluorescence imaging-guided lymphadenectomy in radical gastrectomy for GC.

Methods

Studies comparing lymphadenectomy in radical gastrectomy between use and non-use of ICG fluorescence imaging up to July 2022 were systematically searched from PubMed, Web of Science, Embase and Cochrane Library. A pooled analysis was performed for the available data regarding the baseline features, the number of retrieved lymph nodes (LNs), the number of metastatic LNs and surgical outcomes as well as oncological outcomes. RevMan 5.3 software was used to perform the statistical analysis. Quality evaluation and publication bias were also conducted.

Results

17 studies with a total of 2274 patients (1186 in the ICG group and 1088 in the control group) undergoing radical gastrectomy and lymphadenectomy were included. In the pooled analysis, the baseline features were basically comparable. However, the number of retrieved LNs in the ICG group was significantly more than that in the control group (MD = 7.41, 95% CI = 5.44 to 9.37, P < 0.00001). No significant difference was found between the ICG and control groups in terms of metastatic LNs (MD = -0.05, 95% CI = -0.25 to 0.16, P = 0.65). In addition, the use of ICG could reduce intraoperative blood loss (MD = -17.96, 95% CI = -27.89 to -8.04, P = 0.0004) without increasing operative time (P = 0.14) and overall complications (P = 0.10). In terms of oncological outcomes, the use of ICG could reduce the overall recurrence rate (OR = 0.50; 95% CI 0.28-0.89; P = 0.02) but could not increase the 2-year overall survival rate (OR = 1.25; 95% CI 0.72-2.18; P = 0.43).

Conclusions

ICG imaging-guided lymphadenectomy is valuable for complete LNs dissection in radical gastrectomy for GC. However, more high-quality randomized controlled trials are needed to confirm this benefit.

Model based on preoperative clinical characteristics to predict lymph node metastasis in patients with gastric cancer

Background

The risk factors of lymph node metastasis (LNM) in gastric cancer (GC) remain controversial. We aimed to identify risk factors of LNM in GC and construct a predictive model.

Methods

A total of 1,337 resectable GC patients who underwent radical D2 lymphadenectomy at the first affiliated Hospital of Anhui Medical University from January 2011 to January 2014 were retrospectively analyzed and randomly divided into training and validation cohorts (n = 1,003 and n = 334, respectively) in a 3:1 ratio. Collecting indicators include age, gender, body mass index (BMI), tumor location, pathology, histological grade, tumor size, preoperative neutrophils to lymphocytes ratio (NLR), platelets to lymphocytes ratio (PLR), fibrinogen to albumin ratio (FAR), carcinoembryonic antigen (CEA), cancer antigen19-9 (CA19-9) and lymph nodes status. Significant risk factors were identified through univariate and multivariate logistic regression analysis, which were then included and presented as a nomogram. The performance of the model was assessed with receiver operating characteristic curves (ROC curves), calibration plots, and Decision curve analysis (DCA), and the risk groups were divided into low-and high-risk groups according to the cutoff value which was determined by the ROC curve.

Results

BMI, histological grade, tumor size, CEA, and CA19-9 were enrolled in the model as independent risk factors of LNM. The model showed good resolution, with a C-index of 0.716 and 0.727 in the training and validation cohort, respectively, and good calibration. The cutoff value for predicted probability is 0.594, the proportion of patients with LNM in the high-risk group was significantly higher than that in the low-risk group. Decision curve analysis also indicated that the model had a good positive net gain.

Conclusions

The nomogram-based prediction model developed in this study is stable with good resolution, reliability, and net gain. It can be used by clinicians to assess preoperative lymph node metastasis and risk stratification to develop individualized treatment plans.

Controlling nutritional status score as a prognostic marker to predict overall survival in resected biliary tract cancers

Background

The aim of our study was to explore the prognostic significance of the preoperative controlling nutritional status (CONUT) score and establish a nomogram to predict overall survival (OS) and to achieve a more accurate prognostic risk stratification.

Methods

Clinicopathological records of 371 patients who underwent surgical resection for biliary tract cancers (BTC) from December 2002 to December 2017 were reviewed retrospectively. The associations of the CONUT score with clinicopathological factors and OS were evaluated. Univariate and multivariable Cox regression analysis were used to screen out independent predictors. A nomogram was developed and validated to estimate OS.

Results

The CONUT score was an independent predictor of OS [hazard ratio 1.478, 95% confidence interval (CI), 1.078-2.025, P=0.015]. And patients with a high CONUT score tended to have a poor prognosis with poor differentiation (P=0.011) of tumor cells and longer hospital stays (P=0.046). Besides the CONUT score, carbohydrate antigen 19-9, surgical method, and the American Joint Committee on Cancer (AJCC; 7th edition) TNM stage were contained in the final prognostic model. An OS nomogram was generated to visually predict 1-, 3-, and 5-year OS. The C-index was 0.714 (95% CI, 0.673-0.755) and 0.679 (95% CI, 0.616-0.742) in the development and validation cohort respectively. The nomogram provided superior discriminative power than the AJCC TNM staging system. The nomogram also demonstrated good risk stratification power in the entire cohort of BTC patients as well as for both BTC and surgical method subgroups.

Conclusions

The nomogram based on the CONUT score can predict OS in patients with BTCs, and it performed better than the AJCC TNM staging system.