Multi‑institutional development and validation of a nomogram to predict prognosis of early-onset gastric cancer patients

Establishment and validation of an immune-related nomogram for the prognosis of pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PDAC) is a highly aggressive neoplasm characterized by limited therapeutic options, particularly in the realm of immunotherapy. This study aims to improve prognosis prediction to guide therapeutic decision-making, and to identify novel targets for immunotherapy of PDAC. We conducted Cox and LASSO regression analyses to develop immune-related gene signature and corresponding nomogram, and the robustness of these signatures was demonstrated using multiple approaches. Additionally, CIBERSORT, ESTIMATE, and xCell algorithms were utilized to assess immune cell infiltration, with experimental validation performed though qPCR. An immune-related gene signature consisting of 18 genes, and the prognostic nomogram was established with superior performance compared to the conventional staging system. Key parameters incorporated into the nomogram included the gene signature, tumor stage, and postoperative treatment. Patients identified as high-risk exhibited an anti-inflammatory tumor microenvironment, characterized by an increase in M2-like tumor-associated macrophages and heightened tumor purity. Notably, the expression of interleukin 6 receptor (IL6R) in PDAC was predominantly derived from macrophages and was significantly associated with patient survival outcomes. Furthermore, attenuated IL-6/IL-6R signaling was found to promote M2-like macrophage differentiation. This study successfully established an immune-related gene signature and a robust nomogram for predicting clinical outcomes in patients with PDAC. Furthermore, we identified IL6R as a promising target for future immunotherapeutic strategies.

A nomogram for predicting overall survival in oral squamous cell carcinoma: a SEER database and external validation study

Purpose

Oral squamous cell carcinoma (OSCC) often presents with unsatisfactory survival outcomes, especially in advanced stages. This study aimed to develop and validate a nomogram incorporating demographic, clinicopathologic, and treatment-related factors to improve the prediction of overall survival (OS) in OSCC patients.

Methods

Data from 15,204 OSCC patients in a US database were retrospectively utilized to construct a prognostic model and generate a nomogram. External validation was performed using an independent cohort of 359 patients from a specialized cancer center in China. Prognostic factors were identified using Cox regression analysis and incorporated into the nomogram. Model performance was evaluated by concordance index (C-index), time-dependent area under the receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis (DCA). A risk stratification system was developed to classify patients into high- and low-risk groups.

Results

Age, sex, primary tumor site, T and N staging, and treatment modalities (including surgery, chemotherapy, and radiotherapy) were found to be independent prognostic factors. The nomogram achieved a C-index of 0.727 in the training set and 0.6845 in the validation set, outperforming the conventional TNM staging system. The nomogram's superior predictive accuracy was confirmed by higher AUC values, better calibration, and improved clinical utility as demonstrated by DCA. Risk stratification, based on the nomogram, distinguished patients into distinct prognostic groups with significant OS differences.

Conclusions

This nomogram provides an effective, personalized tool for predicting OS in OSCC. It offers clinicians a valuable aid for treatment decision-making and improves patient management.

Development of a Prognostic Nomogram for Overall Survival in Gastric Cancer Patients Who Underwent Adjuvant Chemoradiotherapy

PURPOSE

The aim of this study was to identify prognostic factors influencing overall survival (OS) in patients with gastric cancer treated with adjuvant chemoradiotherapy (CRT) and to develop a predictive model.

METHODS

We retrospectively evaluated 245 non-metastatic gastric cancer patients who received adjuvant CRT or radiotherapy from 2010 to 2020. Survival analyses were performed using the Kaplan-Meier method. Prognostic factors were identified through univariate and multivariate Cox regression analyses. A nomogram was constructed based on significant predictive factors for OS, including lymph node ratio, T classification, tumor location, and local recurrence.

RESULTS

The median follow-up duration was 41.5 months (range, 6-144.8 months). The 2- and 5-year OS and progression-free survival were 77% and 53% and 64% and 49%, respectively. In multivariate analysis, tumor location (distal vs. proximal), pT classification (pT1-2 vs. pT3-4), lymph node ratio (< 0.18 vs. ≥ 0.18), and presence of local recurrence were independent prognostic factors for OS. The optimal cut-off value for the total nomogram score predicting OS was 116 points. Patients with < 116 points had 2- and 5-year OS rates of 87% and 73%, respectively, compared to 67% and 30% for those with ≥ 116 points.

CONCLUSION

A nomogram was constructed incorporating lymph node ratio, T classification, tumor site, and local recurrence for gastric cancer patients receiving adjuvant CRT. Patients with a total score below 116 demonstrated higher survival rates. This nomogram may aid in defining optimal follow-up intervals.

Modified traditional TNM staging of pyriform sinus and hypopharyngeal and laryngeal cancer based on lymph node ratio and its clinical significance: a population-based study combined with external validation

BACKGROUND

To evaluate the application value of a new tumor-node-metastasis lymph node ratio-modified (TLNRM) staging prediction model based on lymph node ratio (LNR) in patients with pyriform sinus and hypopharyngeal and laryngeal cancer (PHLC).

MATERIALS AND METHODS

A total of 2257 patients with pathologically diagnosed PHLC from 2004 through 2019 were collected from the SEER database for analysis. The N staging of AJCC was replaced by LNR, and we compared the differences in patient prognosis and judgment ability between the new TLNRM staging and the 8th edition TNM staging. At the same time, data from 1094 people in our hospital were included for external verification and validation.

RESULTS

We selected four cutoff points based on LNR and reclassified N staging into five groups (LNR1-5). Compared to the traditional TNM staging (8th edition), the new TLNRM staging showed a statistically significant 5-year overall survival difference. The decision curve showed that the new TLNRM staging had a higher net benefit for different decision thresholds than the traditional TNM staging system's prediction line. The smaller Akaike information criterion (AIC) and Bayesian information criterion (BIC) suggested that the new staging system had a higher sensitivity to prognosis evaluation compared to the traditional staging system. TLNRM stage III patients can benefit from radiotherapy, while TLNRM IVA and IVB patients can benefit from chemoradiotherapy. The same conclusion has been drawn from external validation data from our center.

CONCLUSIONS

Compared with the traditional 8th edition AJCC staging system, the new TLNRM staging system has advantages in predicting the staging and prognosis of PHLC patients and can independently guide postoperative chemoradiotherapy in patients.

Establishment and validation of a nomogram model for early diagnosis of gastric cancer: a large-scale cohort study

Purpose

Identifying high-risk populations and diagnosing gastric cancer (GC) early remains challenging. This study aimed to establish and verify a nomogram model for the early diagnosis of GC based on conventional laboratory indicators.

Methods

We performed a retrospective analysis of the clinical data of 2,770 individuals with first diagnosis of GC and 1,513 patients with benign gastric disease from January 2018 to December 2022. The cases were divided into the training set and validation set randomly, with a ratio of 7:3. Variable screening was performed by least absolute shrinkage and selection operator (LASSO) and logistic regression analysis. A nomogram was constructed in the training set to assist in the early diagnosis of GC.

Results

There were 4283 patients included in the study, with 2998 patients assigned in the training set and 1285 patients in the validation set. Through LASSO regression and logistic regression analysis, independent variables associated with GC were identified, including CEA, CA199, LYM, HGB, MCH, MCHC, PLT, ALB, TG, HDL, and AFR. The nomogram model was constructed using the above 11 independent indicators. The AUC was 0.803 for the training set and 0.797 for the validation set, indicating that the model showed high clinical diagnostic efficacy. The calibration curves and decision curve analysis (DCA) of the nomogram presented good calibration and clinical application ability.

Conclusion

Based on the analysis of large sample size, we constructed a nomogram model with 11 routine laboratory indicators, which showed good discrimination ability and calibration.

Depression screening model for middle-aged and elderly diabetic patients in China

Diabetes is a common global disease closely associated with an increased risk of depression. This study analyzed China Health and Retirement Longitudinal Study (CHARLS) data to examine depression in diabetic patients across China. using 29 variables including demographic, behavioral, health conditions, and mental health parameters. The dataset was randomly divided into a 70% training set and a 30% validation set. Predictive factors significantly associated with depression were identified using least absolute shrinkage and selection operator (LASSO) and logistic regression analysis. A nomogram model was constructed using these predictive factors. The model evaluation included the C-index, calibration curves, the Hosmer-Lemeshow test, and DCA. Depression prevalence was 39.1% among diabetic patients. Multifactorial logistic regression identified significant predictors including gender, permanent address, self-perceived health status, presence of lung disease, arthritis, memory disorders, life satisfaction, cognitive function score, ADL score, and social activity. The nomogram model showed high consistency and accuracy, with AUC values of 0.802 for the training set and 0.812 for the validation set. Both sets showed good model fit with Hosmer-Lemeshow P > 0.05. Calibration curves showed significant consistency between the nomogram model and actual observations. ROC and DCA indicated that the nomogram had a good predictive performance. The nomogram developed in this study effectively assesses depression risk in diabetic patients, helping clinicians identify high-risk individuals. This tool could potentially improve patient outcomes.

Analysis of factors influencing bronchiectasis patients with active pulmonary tuberculosis and development of a nomogram prediction model

Background

To identify the risk factors for bronchiectasis patients with active pulmonary tuberculosis (APTB) and to develop a predictive nomogram model for estimating the risk of APTB in bronchiectasis patients.

Methods

A retrospective cohort study was conducted on 16,750 bronchiectasis patients hospitalized at the Affiliated Hospital of Guangdong Medical University and the Second Affiliated Hospital of Guangdong Medical University between January 2019 and December 2023. The 390 patients with APTB were classified as the case group, while 818 patients were randomly sampled by computer at a 1:20 ratio from the 16,360 patients with other infections to serve as the control group. Relevant indicators potentially leading to APTB in bronchiectasis patients were collected. Patients were categorized into APTB and inactive pulmonary tuberculosis (IPTB) groups based on the presence of tuberculosis. The general characteristics of both groups were compared. Variables were screened using the least absolute shrinkage and selection operator (LASSO) analysis, followed by multivariate logistic regression analysis. A nomogram model was established based on the analysis results. The model's predictive performance was evaluated using calibration curves, C-index, and ROC curves, and internal validation was performed using the bootstrap method.

Results

LASSO analysis identified 28 potential risk factors. Multivariate analysis showed that age, gender, TC, ALB, MCV, FIB, PDW, LYM, hemoptysis, and hypertension are independent risk factors for bronchiectasis patients with APTB (p < 0.05). The nomogram demonstrated strong calibration and discrimination, with a C-index of 0.745 (95% CI: 0.715-0.775) and an AUC of 0.744 for the ROC curve. Internal validation using the bootstrap method produced a C-index of 0.738, further confirming the model's robustness.

Conclusion

The nomogram model, developed using common clinical serological characteristics, holds significant clinical value for assessing the risk of APTB in bronchiectasis patients.

Serum CHI3L1 Levels Predict Overall Survival of Hepatocellular Carcinoma Patients after Hepatectomy

Objective: The Chitinase 3-like protein 1 (CHI3L1) is currently used as a biomarker for the diagnosis of liver fibrosis. However, its prognostic value for hepatocellular carcinoma (HCC) patients remains controversial. In this study, we aimed to investigate the prognostic value of the CHI3L1 in HCC patients after hepatectomy. Methods: In total, 753 HCC patients who underwent curative hepatectomy between January 2017 to August 2021 were retrospectively recruited. The probability of overall survival (OS) was evaluated by the Kaplan-Meier method and compared between groups using the log-rank test. Cox proportional hazard regression analysis was used to determine the independent prognostic factors. A prognostic nomogram was constructed for further examine the clinical utility of CHI3L1 in HCC. Results: Kaplan-Meier analysis revealed that elevated serum CHI3L1 levels were associated with worse overall survival of HCC patients. Multivariate Cox regression analysis showed that the high-CHI3L1 group (≥198.94 ng/ml) was associated with a shorter survival time compared with that in the low-CHI3L1 group (< 198.94 ng/ml) after adjustment for potential confounding factors (HR =1.43, 95% CI = 1.05-1.94, P = 0.024). Additionally, the nomogram had sufficient calibration and discriminatory power in the training cohort, with C-indexes of 0.723 (95% CI: 0.673-0.772). The validation cohort showed similar results. Finally, we demonstrated that the AUC of the nomogram was 0.752 (95% CI: 0.683-0.821), which had better predictive ability than AFP (AUC: 0.644, 95% CI: 0.577-0.711). Conclusion: Our results confirmed that the CHI3L1 could serve as an independent predictor for OS in HCC patients after hepatectomy. The nomogram showed a good performance in prognosis prediction of HCC.

Characteristics of regional lymph node metastasis in breast cancer and construction of a nomogram model based on ultrasonographic analysis: a retrospective study

OBJECTIVE

The ultrasonographic characteristics of lymph node metastasis in breast cancer patients were retrospectively analyzed, and a predictive nomogram model was constructed to provide an imaging basis for better clinical evaluation.

METHODS

B-mode ultrasound was used to retrospectively analyze the imaging characteristics of regional lymph nodes and tumors. Pathological examination confirmed the presence of lymph node metastasis in breast cancer patients. Univariable and multivariable logistic regression analyses were performed to analyze the risk factors for lymph node metastasis. LASSO regression analysis was performed to screen noninvasive indicators, and a nomogram prediction model was constructed for breast cancer patients with lymph node metastasis.

RESULTS

A total of 187 breast cancer patients were enrolled, including 74 patients with lymph node metastasis in the positive group and 113 patients without lymph node metastasis in the negative group. Multivariate analysis revealed that pathological type (OR = 4.58, 95% CI: 1.44-14.6, p = 0.01), tumor diameter (OR = 1.37, 95% CI: 1.07-1.74, p = 0.012), spiculated margins (OR = 7.92, 95% CI: 3.03-20.67, p < 0.001), mixed echo of the breast tumor (OR = 37.09, 95% CI: 3.49-394.1, p = 0.003), and unclear lymphatic hilum structure (OR = 16.07, 95% CI: 2.41-107.02, p = 0.004) were independent risk factors for lymph node metastasis. A nomogram model was constructed for predicting breast cancer with lymph node metastasis, incorporating three significantly correlated indicators identified through LASSO regression analysis, namely, tumor spiculated margins, cortical thickness of lymph nodes, and unclear lymphatic hilum structure. The receiver operating characteristic (ROC) curve revealed that the area under the curve (AUC) was 0.717 (95% CI, 0.614-0.820) for the training set and 0.817 (95% CI, 0.738-0.890) for the validation set. The Hosmer-Lemeshow test results for the training set and the validation set were p = 0.9148 and p = 0.1648, respectively. The prediction nomogram has good diagnostic performance.

CONCLUSIONS

B-mode ultrasound is helpful in the preoperative assessment of breast cancer patients with lymph node metastasis. The predictive nomogram model, which is based on logistic regression and LASSO regression analysis, is clinically safe, reliable, and highly practical.

Emerging trends in early-onset gastric cancer

ABSTRACT

The incidence of early-onset gastric cancer (EOGC) is consistently increasing, and its etiology is notably complex. This increase may be attributed to distinctive factors that differ from those associated with late-onset gastric cancer (LOGC), including genetic predispositions, dietary factors, gastric microbiota dysbiosis, and screening of high-risk cases. These factors collectively contribute to the onset of cancer. EOGC significantly differs from LOGC in terms of clinicopathological and molecular characteristics. Moreover, multiple differences in prognosis and clinical management also exist. This study aimed to systematically review the latest research advancements in the epidemiological characteristics, etiological factors, clinicopathological and molecular features, prognosis, and treatment modalities of EOGC.

Risk assessment and prediction of occult uterine sarcoma in patients with presumed uterine fibroids before high-intensity focused ultrasound treatment

OBJECTIVE

To develop a diagnostic model for predicting occult uterine sarcoma in patients with presumed uterine fibroids.

MATERIALS AND METHODS

We retrospectively reviewed 41631 patients with presumed uterine fibroids who presented for HIFU treatment in 13 hospitals between November 2008 and October 2023. Of these patients, 27 with occult uterine sarcoma and 54 with uterine fibroids were enrolled. Univariate analysis and multivariate logistics regression analysis were used to determine the independent risk factors for the diagnosis of occult uterine sarcoma. A prediction model was constructed based on the coefficients of the risk factors.

RESULTS

The multivariate analysis revealed abnormal vaginal bleeding, ill-defined boundary of tumor, hyperintensity on T2WI, and central unenhanced areas as independent risk factors. A scoring system was created to assess for occult uterine sarcoma risk. The score for abnormal vaginal bleeding was 56. The score for ill-defined lesion boundary was 90. The scores for lesions with hypointensity, isointensity signal/heterogeneous signal intensity, and hyperintensity on T2WI were 0, 42, and 93, respectively. The scores for lesions without enhancement on the mass margin, uniform enhancement of tumor, and no enhancement in the center of tumor were 0, 20, and 100, respectively. Patients with a higher total score implied a higher likelihood of a diagnosis of occult uterine sarcoma than that of patients with a lower score. The established model showed good predictive efficacy.

CONCLUSIONS

Our results demonstrated that the diagnostic prediction model can be used to evaluate the risk of uterine sarcoma in patients with presumed uterine fibroids.

Screening high-risk individuals for primary gastric carcinoma: evaluating overall survival probability score in the presence and absence of lymphatic metastasis post-gastrectomy

OBJECTIVE

The aim of this study was to develop and validate prognostic models for predicting overall survival in individuals with gastric carcinoma, specifically focusing on both negative and positive lymphatic metastasis.

METHODS

A total of 1650 patients who underwent radical gastric surgery at Shanxi Cancer Hospital between May 2002 and December 2020 were included in the analysis. Multiple Cox Proportional Hazards analysis was performed to identify key variables associated with overall survival in both negative and positive lymphatic metastasis cases. Internal validation was conducted using bootstrapping to assess the prediction accuracy of the models. Calibration curves were used to demonstrate the accuracy and consistency of the predictions. The discriminative abilities of the prognostic models were evaluated and compared with the 8th edition of AJCC-TNM staging using Harrell's Concordance index, decision curve analysis, and time-dependent receiver operating characteristic curves.

RESULTS

The nomogram for node-negative lymphatic metastasis included variables such as age, pT stage, and maximum tumor diameter. The C-index for this model in internal validation was 0.719, indicating better performance compared to the AJCC 8th edition TNM staging. The nomogram for node-positive lymphatic metastasis included variables such as gender, age, maximum tumor diameter, neural invasion, Lauren classification, and expression of Her-2, CK7, and CD56. The C-index for this model was 0.674, also outperforming the AJCC 8th edition TNM staging. Calibration curves, time-dependent receiver operating characteristic curves, and decision curve analysis for both nomograms demonstrated excellent prediction ability. Furthermore, significant differences in prognosis between low- and high-risk groups supported the models' strong risk stratification performance.

CONCLUSION

This study provides valuable risk stratification models for lymphatic metastasis in gastric carcinoma, encompassing both node-positive and negative cases. These models can help identify low-risk individuals who may not require further intervention, while high-risk individuals can benefit from targeted therapies aimed at addressing lymphatic metastasis.

Identification of a novel matrix metalloproteinases-related prognostic signature in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide. Cancer cells' local infiltration, proliferation, and spread are mainly influenced by the protein hydrolyzing function of different matrix metalloproteinases (MMPs). However, no study has determined the relationship between MMPs and prognostic prediction in HCC.

METHODS

Expression profiles of mRNA and MMPs-related genes were obtained from publicly available databases. Cox regression and LASSO Cox regression analysis were used to identify and predict MMPs-related prognostic signature and construct predictive models for overall survival (OS). A nomogram was used to validate the accuracy of the prediction model. Drug prediction was performed using the Genomics of Drug Sensitivity in Cancer (GDSC) dataset, and single-cell clustering analysis was performed to further understand the significance of the MMPs-related signature.

RESULTS

A MMPs-related prognostic signature (including RNPEPL1, ADAM15, ADAM18, ADAMTS5, CAD, YME1L1, AMZ2, PSMD14, and COPS6) was identified. Using the median value, HCC patients in the high-risk group showed worse OS than those in the low-risk group. Immune microenvironment analysis showed that patients in the high-risk group had higher levels of M0 and M2 macrophages. Drug sensitivity analysis revealed that the IC50 values of sorafenib, cisplatin, and cytarabine were higher in the high-risk group. Finally, the single-cell cluster analysis results showed that YME1L1 and COPS6 were the major genes expressed in the monocyte cluster.

CONCLUSIONS

A novel MMPs-related signature can be used to predict the prognosis of HCC. The findings of this research could potentially impact the predictability of the prognosis and treatment of HCC.

Development and validation of a nomogram to predict cancer-specific survival of elderly patients with unresected gastric cancer who received chemotherapy

This investigation aimed to explore the prognostic factors in elderly patients with unresected gastric cancer (GC) who have received chemotherapy and to develop a nomogram for predicting their cancer-specific survival (CSS). Elderly gastric cancer patients who have received chemotherapy but no surgery in the Surveillance, Epidemiology, and End Results Database between 2004 and 2015 were included in this study. Cox analyses were conducted to identify prognostic factors, leading to the formulation of a nomogram. The nomogram was validated using receiver operating characteristic (ROC) and calibration curves. The findings elucidated six prognostic factors encompassing grade, histology, M stage, radiotherapy, tumor size, and T stage, culminating in the development of a nomogram. The ROC curve indicated that the area under curve of the nomogram used to predict CSS for 3, 4, and 5 years in the training queue as 0.689, 0.708, and 0.731, and in the validation queue, as 0.666, 0.693, and 0.708. The calibration curve indicated a high degree of consistency between actual and predicted CSS for 3, 4, and 5 years. This nomogram created to predict the CSS of elderly patients with unresected GC who have received chemotherapy could significantly enhance treatment accuracy.

Early-Onset Gastrointestinal Malignancies: An Investigation into a Rising Concern

There is growing recognition of early-onset gastrointestinal (GI) malignancies in young adults < 50 years of age. While much of the literature has emphasized colorectal cancer, these also include esophageal, gastric, liver, pancreatic, and biliary tract malignancies. Various factors, including lifestyle, hereditary, and environmental elements, have been proposed to explain the rising incidence of GI malignancies in the younger population. This review aims to provide an overview of the recent literature, including global trends and information regarding genetic and environmental risk factors.

Nomogram models for predicting overall and cancer-specific survival in early-onset gastric cancer patients: a population-based cohort study

To develop nomogram models for predicting the overall survival (OS) and cancer-specific survival (CSS) of early-onset gastric cancer (EOGC) patients. A total of 1077 EOGC patients from the Surveillance, Epidemiology, and End Results (SEER) database were included, and an additional 512 EOGC patients were recruited from the Fourth Hospital of Hebei Medical University, serving as an external test set. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors. Based on these factors, two nomogram models were established, and web-based calculators were developed. These models were validated using receiver operating characteristics (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Multivariate analysis identified gender, histological type, stage, N stage, tumor size, surgery, primary site, and lung metastasis as independent prognostic factors for OS and CSS in EOGC patients. Calibration curves and DCA curves demonstrated that the two constructed nomogram models exhibited good performance. These nomogram models demonstrated superior performance compared to the 7th edition of the AJCC tumor-node-metastasis (TNM) classification (internal validation set: 1-year OS: 0.831 vs 0.793, P = 0.072; 1-year CSS: 0.842 vs 0.816, P = 0.190; 3-year OS: 0.892 vs 0.857, P = 0.039; 3-year CSS: 0.887 vs 0.848, P = 0.018; 5-year OS: 0.906 vs 0.880, P = 0.133; 5-year CSS: 0.900 vs 0.876, P = 0.109). In conclusion, this study developed two nomogram models: one for predicting OS and the other for CSS of EOGC patients, offering valuable assistance to clinicians.

Deep learning model for predicting postoperative survival of patients with gastric cancer

Background

Prognostic prediction for surgical treatment of gastric cancer remains valuable in clinical practice. This study aimed to develop survival models for postoperative gastric cancer patients.

Methods

Eleven thousand seventy-five patients from the Surveillance, Epidemiology, and End Results (SEER) database were included, and 122 patients from the Chinese database were used for external validation. The training cohort was created to create three separate models, including Cox regression, RSF, and DeepSurv, using data from the SEER database split into training and test cohorts with a 7:3 ratio. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The new risk stratification based on the best model will be compared with the AJCC stage on the test and Chinese cohorts using decision curve analysis (DCA), the net reclassification index (NRI), and integrated discrimination improvement (IDI).

Results

It was discovered that the DeepSurv model predicted postoperative gastric cancer patients' overall survival (OS) with a c-index of 0.787; the area under the curve reached 0.781, 0.798, 0.868 at 1-, 3- and 5- years, respectively; the Brier score was below 0.25 at different time points; showing an advantage over the Cox and RSF models. The results are also validated in the China cohort. The calibration plots demonstrated good agreement between the DeepSurv model's forecast and actual results. The NRI values (test cohort: 0.399, 0.288, 0.267 for 1-, 3- and 5-year OS prediction; China cohort:0.399, 0.288 for 1- and 3-year OS prediction) and IDI (test cohort: 0.188, 0.169, 0.157 for 1-, 3- and 5-year OS prediction; China cohort: 0.189, 0.169 for 1- and 3-year OS prediction) indicated that the risk score stratification performed significantly better than the AJCC staging alone (P < 0.05). DCA showed that the risk score stratification was clinically useful and had better discriminative ability than the AJCC staging. Finally, an interactive native web-based prediction tool was constructed for the survival prediction of patients with postoperative gastric cancer.

Conclusion

In this study, a high-performance prediction model for the postoperative prognosis of gastric cancer was developed using DeepSurv, which offers essential benefits for risk stratification and prognosis prediction for each patient.

Primary resistance to immunotherapy in patients with a dMMR/MSI metastatic gastrointestinal cancer: who is at risk? An AGEO real-world study

BACKGROUND

The outstanding efficacy of immunotherapy in metastatic dMMR/MSI gastro-intestinal (GI) cancers has led to a rapid increase in the number of patients treated. However, 20-30% of patients experience primary resistance to immune checkpoint inhibitors (ICIPR) and need better characterization.

METHODS

This AGEO real-world study retrospectively analyzed the efficacy and safety of ICIs and identified clinical variables associated with ICIPR in patients with metastatic dMMR/MSI GI cancers treated with immunotherapy between 2015 and 2022.

RESULTS

399 patients were included, 284 with colorectal cancer (CRC) and 115 with non-CRC, mostly treated by an anti-PD(L)1 (88.0%). PFS at 24 months was 55.8% (95CI [50.8-61.2]) and OS at 48 months was 59.1% (95CI [53.0-65.9]). ORR was 51.0%, and 25.1% of patients were ICIPR. There was no statistical difference in ORR, DCR, PFS, or OS between CRC and non-CRC groups. In multivariable analysis, ICIPR was associated with ECOG-PS ≥ 2 (OR = 3.36), liver metastases (OR = 2.19), peritoneal metastases (OR = 2.00), ≥1 previous line of treatment (OR = 1.83), and age≤50 years old (OR = 1.76).

CONCLUSION

These five clinical factors associated with primary resistance to ICIs should be considered by physicians to guide treatment choice in GI dMMR/MSI metastatic cancer patients.

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.

Construction of a Nomogram to Predict Overall Survival in Patients with Early-Onset Hepatocellular Carcinoma: A Retrospective Cohort Study

Hepatocellular carcinoma (HCC) is a widespread and impactful cancer which has pertinent implications worldwide. Although most cases of HCC are typically diagnosed in individuals aged ≥60 years, there has been a notable rise in the occurrence of HCC among younger patients. However, there is a scarcity of precise prognostic models available for predicting outcomes in these younger patients. A retrospective analysis was conducted to investigate early-onset hepatocellular carcinoma (EO-LIHC) using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2018. The analysis included 1392 patients from the SEER database and our hospital. Among them, 1287 patients from the SEER database were assigned to the training cohort (n = 899) and validation cohort 1 (n = 388), while 105 patients from our hospital were assigned to validation cohort 2. A Cox regression analysis showed that age, sex, AFP, grade, stage, tumor size, surgery, and chemotherapy were independent risk factors. The nomogram developed in this study demonstrated its discriminatory ability to predict the 1-, 3-, and 5-year overall survival (OS) rates in EO-LIHC patients based on individual characteristics. Additionally, a web-based OS prediction model specifically tailored for EO-LIHC patients was created and validated. Overall, these advancements contribute to improved decision-making and personalized care for individuals with EO-LIHC.

Biological and clinical implications of early-onset cancers: A unique subtype

In recent years, the incidence of cancers is continuously increasing in young adults. Early-onset cancer (EOC) is usually defined as patients with cancers under the age of 50, and may represent a unique subgroup due to its special disease features. Overall, EOCs often initiate at a young age, present as a better physical performance but high degree of malignancy. EOCs also share common epidemiological and hereditary risk factors. In this review, we discuss several representative EOCs which were well studied previously. By revealing their clinical and molecular similarities and differences, we consider the group of EOCs as a unique subtype compared to ordinary cancers. In consideration of EOC as a rising threat to human health, more researches on molecular mechanisms, and large-scale, prospective clinical trials should be carried out to further translate into improved outcomes.

Prediction model of liver metastasis risk in patients with gastric cancer: A population-based study

Liver was the most common site of distant metastasis in patients with gastric cancer (GC). The prediction model of the risk of liver metastasis was rarely proposed. Therefore, we aimed to establish a prediction model for liver metastasis in patients with GC. In this retrospective cohort study, we extracted demographic and clinical data of all the GC patients from the Surveillance, Epidemiology, and End Results registration database from 2010 to 2015. Patients were divided into training set (n = 1691) for model development and testing set (n = 3943) for validation. Univariable and multivariable logistic regression analyses were carried out on the training set to screen potential predictors of liver metastasis and constructed a prediction model. The receiver operator characteristics curves with the area under curve values were used to assess the predictive performance of the liver metastasis prediction model. And a nomogram of the prediction model was also constructed. Of the total 5634 GC patients, 444 (7.88%) had liver metastasis. Variables including age, gender, N stage, T stage, Lauren classification, tumor size, histological type, and surgery were included in the liver metastasis prediction model. The study results indicated that the model had excellent discriminative ability with an area under curve of 0.851 (95% confidence interval: 0.829-0.873) in the training set, and that of 0.849 (95% confidence interval: 0.813-0.885) in the testing set. We have developed an effective prediction model with 8 easily acquired predictors of liver metastasis. The prediction model could predict the risk of liver metastasis in GC patients and performed well, which would assist clinicians to make individualized prediction of liver metastasis in GC patients and adjust treatment strategies in time to improve the prognosis.

Development and validation of nomogram models to predict radiotherapy or chemotherapy benefit in stage III/IV gastric adenocarcinoma with surgery

Objectives

The advanced gastric adenocarcinoma (GAC) patients (stage III/IV) with surgery may have inconsistent prognoses due to different demographic and clinicopathological factors. In this retrospective study, we developed clinical prediction models for estimating the overall survival (OS) and cancer-specific survival (CSS) in advanced GAC patients with surgery.

Methods

A retrospective analysis was conducted using the Surveillance, Epidemiology, and End Results (SEER) database. The total population from 2004 to 2015 was divided into four levels according to age, of which 179 were younger than 45 years old, 695 were 45-59 years old, 1064 were 60-74 years old, and 708 were older than 75 years old. There were 1,712 men and 934 women. Univariate and multivariate Cox regression analyses were performed to identify prognostic factors for OS and CSS. Nomograms were constructed to predict the 1-, 3-, and 5-year OS and CSS. The models' calibration and discrimination efficiency were validated. Discrimination and accuracy were evaluated using the consistency index, area under the receiver operating characteristic curve, and calibration plots; and clinical usefulness was assessed using decision curve analysis. Cross-validation was also conducted to evaluate the accuracy and stability of the models. Prognostic factors identified by Cox regression were analyzed using Kaplan-Meier survival analysis.

Results

A total of 2,646 patients were included in our OS study. Age, primary site, differentiation grade, AJCC 6th_TNM stage, chemotherapy, radiotherapy, and number of regional nodes examined were identified as prognostic factors for OS in advanced GAC patients with surgery (P < 0.05). A total of 2,369 patients were included in our CSS study. Age, primary site, differentiation grade, AJCC 6th_TNM stage, chemotherapy, radiotherapy, and number of regional nodes examined were identified as risk factors for CSS in these patients (P < 0.05). These factors were used to construct the nomogram to predict the 1-, 3-, and 5-year OS and CSS of advanced GAC patients with surgery. The consistency index and area under the receiver operating characteristic curve demonstrated that the models effectively differentiated between events and nonevents. The calibration plots for 1-, 3-, and 5-year OS and CSS probability showed good consistence between the predicted and the actual events. The decision curve analysis indicated that the nomogram had higher clinical predictive value and more significant net gain than AJCC 6th_TNM stage in predicting OS and CSS of advanced GAC patients with surgery. Cross-validation also revealed good accuracy and stability of the models.

Conclusion

The developed predictive models provided available prognostic estimates for advanced GAC patients with surgery. Our findings suggested that both OS and CSS can benefit from chemotherapy or radiotherapy in these patients.

Clinical Significance of NKD Inhibitor of WNT Signaling Pathway 1 (NKD1) in Glioblastoma

Introduction. As the most malignant type of gliomas, glioblastoma is characterized with disappointing prognosis. Here, we aimed to investigate expression and function of NKD inhibitor of Wnt signaling pathway 1 (NKD1), an antagonist of Wnt-beta-catenin signaling pathways, in glioblastoma. Methods. The mRNA level of NKD1 was firstly retrieved from TCGA glioma dataset to evaluate its correlation with clinical characteristics and its value in prognosis prediction. Then, its protein expression level in glioblastoma was tested by immunohistochemistry staining in a retrospectively cohort collected from our medical center (n = 66). Univariate and multivariate survival analyses were conducted to assess its effect on glioma prognosis. Two glioblastoma cell lines, U87 and U251, were used to further investigate the tumor-related role of NKD1 through overexpression strategy in combination with cell proliferation assays. Immune cell enrichment in glioblastoma and its correlation with NKD1 level was finally assessed using bioinformatics analyses. Results. NKD1 shows a lower expression level in glioblastoma compared to that in the normal brain or other glioma subtypes, which is independently correlated to a worse prognosis in both the TCGA cohort and our retrospective cohort. Overexpressing NKD1 in glioblastoma cell lines can significantly attenuate cell proliferation. In addition, expression of NKD1 in glioblastoma is negatively correlated to the T cell infiltration, indicating it may have crosstalk with the tumor immune microenvironment. Conclusions. NKD1 inhibits glioblastoma progression and its downregulated expression indicates a poor prognosis.

Systematically Prognostic Analyses of Gastric Cancer Patients with Ovarian Metastasis

Ovarian metastasis of gastric cancer indicates that the disease has reached the late stage and the opportunity for radical surgery is restricted. However, the clinical characteristics and prognosis of patients with gastric cancer ovarian metastasis (GCOM) remain to be illustrated. Here, we retrieved the information of 780 GCOM cases from the Surveillance, Epidemiology, and End Results (SEERs) database and analyzed their clinicopathological characteristics as well as their survival. According to our data, most GCOM patients showed poor pathological differentiation, advanced T and N stages. The prognostic factors include patients' age, tumor size, surgical resection, and chemotherapy treatment. Of note, the marriage status was also identified as an independent prognostic factor. Besides the identification of prognostic factors, we established nomograms to help predict the overall survival and cancer-specific survival of GCOM, respectively.