Development and validation of metabolic scoring to individually predict prognosis and monitor recurrence early in gastric cancer: A large-sample analysis

A Transfer Learning Radiomics Nomogram to Predict the Postoperative Recurrence of Advanced Gastric Cancer

BACKGROUND AND AIM

In this study, a transfer learning (TL) algorithm was used to predict postoperative recurrence of advanced gastric cancer (AGC) and to evaluate its value in a small-sample clinical study.

METHODS

A total of 431 cases of AGC from three centers were included in this retrospective study. First, TL signatures (TLSs) were constructed based on different source domains, including whole slide images (TLS-WSIs) and natural images (TLS-ImageNet). Clinical model and non-TLS based on CT images were constructed simultaneously. Second, TL radiomic model (TLRM) was constructed by combining optimal TLS and clinical factors. Finally, the performance of the models was evaluated by ROC analysis. The clinical utility of the models was assessed using integrated discriminant improvement (IDI) and decision curve analysis (DCA).

RESULTS

TLS-WSI significantly outperformed TLS-ImageNet, non-TLS, and clinical models (p < 0.05). The AUC value of TLS-WSI in training cohort was 0.9459 (95CI%: 0.9054, 0.9863) and ranged from 0.8050 (95CI%: 0.7130, 0.8969) to 0.8984 (95CI%: 0.8420, 0.9547) in validation cohorts. TLS-WSI and the nodular or irregular outer layer of gastric wall were screened to construct TLRM. The AUC value of TLRM in training cohort was 0.9643 (95CI%: 0.9349, 0.9936) and ranged from 0.8561 (95CI%: 0.7571, 0.9552) to 0.9195 (95CI%: 0.8670, 0.9721) in validation cohorts. The IDI and DCA showed that the performance of TLRM outperformed the other models.

CONCLUSION

TLS-WSI can be used to predict postoperative recurrence in AGC, whereas TLRM is more effective. TL can effectively improve the performance of clinical research models with a small sample size.

The role of 1400 plasma metabolites in gastric cancer: A bidirectional Mendelian randomization study and metabolic pathway analysis

While observational studies have illustrated correlations between plasma metabolites and gastric cancer (GC), the causal association between the 2 is still unclear. Our study aims to delineate the bidirectional relationship between plasma metabolites and GC and find potential metabolic pathways. We undertook a bidirectional 2-sample Mendelian randomization (MR) analysis to investigate the causal relationship, specificity, and direction of association between 1400 plasma metabolites and GC. The GWAS data for metabolites was obtained from a cohort of 8299 European individuals. And the GC's GWAS data was from FinnGen Consortium with 2384 European individuals, and the GWAS catalog with 1029 European ancestry cases for validation. Causal estimates were primarily calculated by the inverse-variance weighted (IVW) method. To ensure robustness, we performed comprehensive sensitivity analyses to assess heterogeneity and address concerns regarding horizontal pleiotropy. We validated the forward relationship between metabolites and GC from another database and implemented meta-analysis. Furthermore, we conducted metabolic enrichment and pathway analysis of these causal metabolites using MetaboAnalyst5.0/6.0 with the database of Kyoto Encyclopedia of Genes and Genomes. All statistical analysis was carried out using R software. Metabolites like 2s, 3R-dihydroxybutyrate, 4-acetamidobutanoate, ferulic acid 4-sulfate and methyl indole-3-acetate was proven positively linked with the development of GC. Asparagine, glucose to maltose ratio, glycohyocholate, Gulonate levels, linoleoyl ethanolamide and Spermidine to (N(1) + N(8))-acetylspermidine ratio was proven to be negatively associated with GC. Moreover, linoleic acid, histidine, glutamine, bilirubin, Succinate to proline ratio were found to be potentially linked to the development of GC. Furthermore, our analysis identified 18 significant metabolic pathways, including Arginine and proline metabolism (P < .009) and Valine, leucine, and isoleucine biosynthesis (P < .031). Our findings offer evidence supporting potential casual relations between multiple plasma metabolites and GC. These findings may offer great potential for future application of these biomarkers in GC screening and clinical prevention strategies.

Development and validation of a nomogram for obesity and related factors to detect gastric precancerous lesions in the Chinese population: a retrospective cohort study

Objectives

The purpose of this study was to construct a nomogram to identify patients at high risk of gastric precancerous lesions (GPLs). This identification will facilitate early diagnosis and treatment and ultimately reduce the incidence and mortality of gastric cancer.

Methods

In this single-center retrospective cohort study, 563 participants were divided into a gastric precancerous lesion (GPL) group (n=322) and a non-atrophic gastritis (NAG) group (n=241) based on gastroscopy and pathology results. Laboratory data and demographic data were collected. A derivation cohort (n=395) was used to identify the factors associated with GPLs to develop a predictive model. Then, internal validation was performed (n=168). We used the area under the receiver operating characteristic curve (AUC) to determine the discriminative ability of the predictive model; we constructed a calibration plot to evaluate the accuracy of the predictive model; and we performed decision curve analysis (DCA) to assess the clinical practicability predictive model.

Results

Four -predictors (i.e., age, body mass index, smoking status, and -triglycerides) were included in the predictive model. The AUC values of this predictive model were 0.715 (95% CI: 0.665-0.765) and 0.717 (95% CI: 0.640-0.795) in the derivation and internal validation cohorts, respectively. These values indicated that the predictive model had good discrimination ability. The calibration plots and DCA suggested that the predictive model had good accuracy and clinical net benefit. The Hosmer-Lemeshow test results in the derivation and validation cohorts for this predictive model were 0.774 and 0.468, respectively.

Conclusion

The nomogram constructed herein demonstrated good performance in terms of predicting the risk of GPLs. This nomogram can be beneficial for the early detection of patients at high risk of GPLs, thus facilitating early treatment and ultimately reducing the incidence and mortality of gastric cancer.

Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism

Background

Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients.

Methods

The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019-2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019-2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan-Meier survival curves.

Findings

Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95-0.99) for internal validation. External validation further confirmed the RF model's effectiveness, yielding an AUC of 0.97 (95% CI 0.94-0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27 ± 3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients.

Interpretation

This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies.

Funding

This work is supported by the National Natural Science Foundation of China (NO.81974315); Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).

Research progress on the biological basis of Traditional Chinese Medicine syndromes of gastrointestinal cancers

Gastrointestinal cancers account for 11.6 % of all cancers, and are the second most frequently diagnosed type of cancer worldwide. Traditional Chinese medicine (TCM), together with Western medicine or alone, has unique advantages for the prevention and treatment of cancers, including gastrointestinal cancers. Syndrome differentiation and treatment are basic characteristics of the theoretical system of TCM. TCM syndromes are the result of the differentiation of the syndrome and the basis of treatment. Genomics, transcriptomics, proteomics, metabolomics, intestinal microbiota, and serology, generated around the central law, are used to study the biological basis of TCM syndromes in gastrointestinal cancers. This review summarizes current research on the biological basis of TCM syndrome in gastrointestinal cancers and provides useful references for future research on TCM syndrome in gastrointestinal cancers.

A Novel Systematic Oxidative Stress Score Predicts the Survival of Patients with Early-Stage Lung Adenocarcinoma

This study aimed to construct an effective nomogram based on the clinical and oxidative stress-related characteristics to predict the prognosis of stage I lung adenocarcinoma (LUAD). A retrospective study was performed on 955 eligible patients with stage I LUAD after surgery at our hospital. The relationship between systematic-oxidative-stress biomarkers and the prognosis was analyzed. The systematic oxidative stress score (SOS) was established based on three biochemical indicators, including serum creatinine (CRE), lactate dehydrogenase (LDH), and uric acid (UA). SOS was an independent prognostic factor for stage I LUADs, and the nomogram based on SOS and clinical characteristics could accurately predict the prognosis of these patients. The nomogram had a high concordance index (C-index) (0.684, 95% CI, 0.656-0.712), and the calibration curves for recurrence-free survival (RFS) probabilities showed a strong agreement between the nomogram prediction and actual observation. Additionally, the patients were divided into two groups according to the cut-off value of risk points based on the nomogram, and a significant difference in RFS was observed between the high-risk and low-risk groups (p < 0.0001). SOS is an independent prognostic indicator for stage I LUAD. These things considered, the constructed nomogram based on SOS could accurately predict the survival of those patients.

History of keratinocyte carcinoma and survival after a second primary malignancy: the Moffitt Cancer Center patient experience

PURPOSE

History of keratinocyte carcinoma (KC) has been associated with survival following the diagnosis of a second primary malignancy (SPM), with the direction of the association varying by cancer type. Research is needed to elucidate the role of other key factors in this association.

METHODS

A retrospective cohort study was conducted among patients newly diagnosed and/or treated at Moffitt Cancer Center in December 2008-April 2020 with breast cancer, lung cancer, melanoma, colon cancer, prostate cancer, and non-Hodgkin lymphoma/chronic lymphocytic leukemia (NHL/CLL) (n = 29,156). History of KC was obtained from new patient intake questionnaires. Age- and stage-adjusted hazard ratios (HR) and 95% confidence intervals (CI) were calculated to estimate the association between history of KC and survival following each cancer, stratified by demographic/clinical characteristics.

RESULTS

KC history was most prevalent in patients with melanoma (28.7%), CLL (19.8%) and lung cancer (16.1%). KC history was associated with better overall survival following prostate cancer (HR = 0.74, 95% CI = 0.55-0.99) and poorer overall survival following CLL (HR = 1.73, 95% CI = 1.10-2.71). Patients with a history of KC experienced better survival within the first four years of a melanoma diagnosis (HR = 0.79, 95% CI = 0.67-0.92); whereas poorer survival was observed for patients who survived 7 + years after a melanoma diagnosis (HR = 2.18, 95% CI = 1.17-4.05). Stratification by treatment and stage revealed directional differences in the associations between KC history and survival among patients with breast cancer and melanoma.

CONCLUSIONS

KC history may be a predictor of survival following an SPM, possibly serving as a marker of immune function and/or DNA damage repair capacity.