[Overview of multivariate regression model analysis and application]

Nomogram based on multimodal magnetic resonance combined with B7-H3mRNA for preoperative lymph node prediction in esophagus cancer

BACKGROUND

Accurate preoperative prediction of lymph node metastasis (LNM) in esophageal cancer (EC) patients is of crucial clinical significance for treatment planning and prognosis.

AIM

To develop a clinical radiomics nomogram that can predict the preoperative lymph node (LN) status in EC patients.

METHODS

A total of 32 EC patients confirmed by clinical pathology (who underwent surgical treatment) were included. Real-time fluorescent quantitative reverse transcription-polymerase chain reaction was used to detect the expression of B7-H3 mRNA in EC tissue obtained during preoperative gastroscopy, and its correlation with LNM was analyzed. Radiomics features were extracted from multi-modal magnetic resonance imaging of EC using Pyradiomics in Python. Feature extraction, data dimensionality reduction, and feature selection were performed using XGBoost model and leave-one-out cross-validation. Multivariable logistic regression analysis was used to establish the prediction model, which included radiomics features, LN status from computed tomography (CT) reports, and B7-H3 mRNA expression, represented by a radiomics nomogram. Receiver operating characteristic area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical application value of the model.

RESULTS

The relative expression of B7-H3 mRNA in EC patients with LNM was higher than in those without metastasis, and the difference was statistically significant (P < 0.05). The AUC value in the receiver operating characteristic (ROC) curve was 0.718 (95%CI: 0.528-0.907), with a sensitivity of 0.733 and specificity of 0.706, indicating good diagnostic performance. The individualized clinical prediction nomogram included radiomics features, LN status from CT reports, and B7-H3 mRNA expression. The ROC curve demonstrated good diagnostic value, with an AUC value of 0.765 (95%CI: 0.598-0.931), sensitivity of 0.800, and specificity of 0.706. DCA indicated the practical value of the radiomics nomogram in clinical practice.

CONCLUSION

This study developed a radiomics nomogram that includes radiomics features, LN status from CT reports, and B7-H3 mRNA expression, enabling convenient preoperative individualized prediction of LNM in EC patients.

Identification and Development of Subtypes with Poor Prognosis in Gastric Cancer Based on Both Hypoxia and Immune Cell Infiltration

Purpose

Hypoxia and immune cell infiltration play an important role in the progression and metastasis of gastric cancer. However, the molecular classification of gastric cancer combined with hypoxia and immune cell infiltration remains unknown.

Materials and Methods

ssGSEA was used to evaluate the hypoxic state and immune cell infiltration of 1059 gastric cancer samples collected from the GEO and TCGA database. Based on the results, unsupervised clustering was performed to obtain different gastric cancer subtypes. The differentially expressed genes related to OS between these subtypes were utilized for LASSO analysis to construct a prognostic signature (HIscore). Subsequently, small-molecule drugs were predicted using the Connectivity Map (CMAP) database.

Results

We obtained three hypoxic-immune infiltration patterns (HIcluster A-C) with different prognoses and classified them as low hypoxic/low immune, high hypoxic/high immune, and low hypoxic/high immune subtypes. Based on the differential genes between HIclusters, we have also obtained other three gastric cancer subtypes (genecluster A-C) and a 13-gene signature (HIscore). At the same time, we extensively explored the clinical and transcriptome traits in different clusters and groups with high or low HIscore. We proved that HIscore is an independent prognostic biomarker and an indicator of genome stability and EMT. Using the CMAP database, we found 96 small-molecule drugs that could reverse the poor prognosis and could serve as therapeutic drugs, especially for gastric cancer patients with high HIscore.

Conclusion

Our study evaluated the hypoxic state and immune cell infiltration in gastric tumors, and identified different gastric cancer subtypes. In addition, we established a hypoxia-immune signature to predict prognosis which is tightly linked to tumor EMT and genomic stability. Based on HIscore, we used the CMAP database to explore small-molecule drugs that may have the potential in serving as therapeutic drugs.

An Exploratory Cohort Study of the Association between the Level of Testosterone and Suicidal Ideation in Hospitalized Adolescent Females with Depression in China

BACKGROUND

To date, around 4 per 100,000 adolescents committed suicide within the 29 OECD countries. The suicidal behavior is related to psychological factors, genetics, neurobiology, and other biomarkers. The aim of this study was to examine risk factors for the development of suicidal ideation in adolescent females with depression, focusing on the relationship between different testosterone levels and suicidal ideation, in order to help develop strategies to intervene in suicidal behavior in female adolescents with depression.

METHOD

In this single-center prospective cohort study, we enrolled adolescent females with depression. We collected information on their baseline data, testosterone levels, symptom self-rating scale scores, suicidal ideation, non-suicidal self-injurious (NSSI) behaviours, and suicide attempts. We used multivariate logistic regression to identify risk factors for the development of suicidal ideation in adolescent females with depression.

RESULTS

A total of 113 hospitalized adolescent females were enrolled with a mean age of 13.5 (1.20). Among these patients, there were 86 (76.11%) subjects who suffered from suicidal ideation, 59 (52.21%) had NSSI and 23 (20.35%) had suicide attempt behavior. In the final model, higher level of testosterone (p=0.04) and higher age (p=0.02) were associated with the higher odds of having suicidal ideation.

CONCLUSION

In this exploratory cohort study, the emergence of suicidal ideation was common among adolescent females with depression. This study is consistent with the other studies. It shows that the age is a potential predictor for suicidal ideation in hospitalized adolescent females with depression.

Development and validation of a prognostic nomogram model for Chinese patients with primary small cell carcinoma of the esophagus

BACKGROUND

Primary small cell carcinoma of the esophagus (PSCE) is a highly invasive malignant tumor with a poor prognosis compared with esophageal squamous cell carcinoma. Due to the limited samples size and the short follow-up time, there are few reports on elucidating the prognosis of PSCE, especially on the establishment and validation of a survival prediction nomogram model covering general information, pathological factors and specific biological proteins of PSCE patients.

AIM

To establish an effective nomogram to predict the overall survival (OS) probability for PSCE patients in China.

METHODS

The nomogram was based on a retrospective study of 256 PSCE patients. Univariate analysis and multivariate Cox proportional hazards regression analysis were used to examine the prognostic factors associated with PSCE, and establish the model for predicting 1-, 3-, and 5-year OS based on the Akaike information criterion. Discrimination and validation were assessed by the concordance index (C-index) and calibration curve and decision curve analysis (DCA). Histology type, age, tumor invasion depth, lymph node invasion, detectable metastasis, chromogranin A, and neuronal cell adhesion molecule 56 were integrated into the model.

RESULTS

The C-index was prognostically superior to the 7^th tumor node metastasis (TNM) staging in the primary cohort [0.659 (95%CI: 0.607-0.712) vs 0.591 (95%CI: 0.517-0.666), P = 0.033] and in the validation cohort [0.700 (95%CI: 0.622-0.778) vs 0.605 (95%CI: 0.490-0.721), P = 0.041]. Good calibration curves were observed for the prediction probabilities of 1-, 3-, and 5-year OS in both cohorts. DCA analysis showed that our nomogram model had a higher overall net benefit compared to the 7^th TNM staging .

CONCLUSION

Our nomogram can be used to predict the survival probability of PSCE patients, which can help clinicians to make individualized survival predictions.

Development and Validation of Predictors for the Survival of Patients With COVID-19 Based on Machine Learning

Background: The outbreak of COVID-19 attracted the attention of the whole world. Our study aimed to explore the predictors for the survival of patients with COVID-19 by machine learning. Methods: We conducted a retrospective analysis and used the idea of machine learning to train the data of COVID-19 patients in Leishenshan Hospital through the logical regression algorithm provided by scikit-learn. Results: Of 2010 patients, 42 deaths were recorded until March 29, 2020. The mortality rate was 2.09%. There were 6,812 records after data features combination and data arrangement, 3,025 records with high-quality after deleting incomplete data by manual checking, and 5,738 records after data balancing finally by the method of Borderline-1 Smote. The results of 10 times of data training by logistic regression model showed that albumin, saturation of pulse oxygen at admission, alanine aminotransferase, and percentage of neutrophils were possibly associated with the survival of patients. The results of 10 times of data training including age, sex, and height beyond the laboratory measurements showed that percentage of neutrophils, saturation of pulse oxygen at admission, alanine aminotransferase, sex, and albumin were possibly associated with the survival of patients. The rates of precision, recall, and f1-score of the two training models were all higher than 0.9 and relatively stable. Conclusions: We demonstrated that percentage of neutrophils, saturation of pulse oxygen at admission, alanine aminotransferase, sex, and albumin were possibly associated with the survival of patients with COVID-19.

Malignant Tumor Purity Reveals the Driven and Prognostic Role of CD3E in Low-Grade Glioma Microenvironment

The tumor microenvironment (TME) contributes to the initiation and progression of many neoplasms. However, the impact of low-grade glioma (LGG) purity on carcinogenesis remains to be elucidated. We selected 509 LGG patients with available genomic and clinical information from the TCGA database. The percentage of tumor infiltrating immune cells and the tumor purity of LGG were evaluated using the ESTIMATE and CIBERSORT algorithms. Stromal-related genes were screened through Cox regression, and protein-protein interaction analyses and survival-related genes were selected in 487 LGG patients from GEO database. Hub genes involved in LGG purity were then identified and functionally annotated using bioinformatics analyses. Prognostic implications were validated in 100 patients from an Asian real-world cohort. Elevated tumor purity burden, immune scores, and stromal scores were significantly associated with poor outcomes and increased grade in LGG patients from the TCGA cohort. In addition, CD3E was selected with the most significant prognostic value (Hazard Ratio=1.552, P<0.001). Differentially expressed genes screened according to CD3E expression were mainly involved in stromal related activities. Additionally, significantly increased CD3E expression was found in 100 LGG samples from the validation cohort compared with adjacent normal brain tissues. High CD3E expression could serve as an independent prognostic indicator for survival of LGG patients and promotes malignant cellular biological behaviors of LGG. In conclusion, tumor purity has a considerable impact on the clinical, genomic, and biological status of LGG. CD3E, the gene for novel membrane immune biomarker deeply affecting tumor purity, may help to evaluate the prognosis and develop individual immunotherapy strategies for LGG patients. Evaluating the ratio of differential tumor purity and CD3E expression levels may provide novel insights into the complex structure of the LGG microenvironment and targeted drug development.

Model of genetic and environmental factors associated with type 2 diabetes mellitus in a Chinese Han population

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a metabolic disorder which accounts for high morbidity and mortality due to complications like renal failure, amputations, cardiovascular disease, and cerebrovascular events.

METHODS

We collected medical reports, lifestyle details, and blood samples of individuals and used the polymerase chain reaction-ligase detection reaction method to genotype the SNPs, and a visit was conducted in August 2016 to obtain the incidence of Type 2 diabetes in the 2113 eligible people. To explore which genes and environmental factors are associated with type 2 diabetes mellitus in a Chinese Han population, we used elastic net to build a model, which is to explain which variables are strongly associated with T2DM, rather than predict the occurrence of T2DM.

RESULT

The genotype of the additive of rs964184, together with the history of hypertension, regular intake of meat and waist circumference, increased the risk of T2DM (adjusted OR = 2.38, p = 0.042; adjusted OR = 3.31, p < 0.001; adjusted OR = 1.05, p < 0.001). The TT genotype of the additive and recessive models of rs12654264, the CC genotype of the additive and dominant models of rs2065412, the TT genotype of the additive and dominant models of rs4149336, together with the degree of education, regular exercise, reduced the risk of T2DM (adjusted OR = 0.46, p = 0.017; adjusted OR = 0.53, p = 0.021; adjusted OR = 0.59, p = 0.021; adjusted OR = 0.57, p = 0.01; adjusted OR = 0.59, p = 0.021; adjusted OR = 0.57, p = 0.01; adjusted OR = 0.50, p = 0.007; adjusted OR = 0.80, p = 0.032) .

CONCLUSION

Eventually we identified a set of SNPs and environmental factors: rs5805 in the SLC12A3, rs12654264 in the HMGCR, rs2065412 and rs414936 in the ABCA1, rs96418 in the ZPR1 gene, waistline, degree of education, exercise frequency, hypertension, and the intake of meat. Although there was no interaction between these variables, people with two risk factors had a higher risk of T2DM than those only having one factor. These results provide the theoretical basis for gene and other risk factors screening to prevent T2DM.