Development of Web-Based Nomograms to Predict Treatment Response and Prognosis of Epithelial Ovarian Cancer

Comprehensive analyses of mitophagy-related genes and mitophagy-related lncRNAs for patients with ovarian cancer

BACKGROUND

Both mitophagy and long non-coding RNAs (lncRNAs) play crucial roles in ovarian cancer (OC). We sought to explore the characteristics of mitophagy-related gene (MRG) and mitophagy-related lncRNAs (MRL) to facilitate treatment and prognosis of OC.

METHODS

The processed data were extracted from public databases (TCGA, GTEx, GEO and GeneCards). The highly synergistic lncRNA modules and MRLs were identified using weighted gene co-expression network analysis. Using LASSO Cox regression analysis, the MRL-model was first established based on TCGA and then validated with four external GEO datasets. The independent prognostic value of the MRL-model was evaluated by Multivariate Cox regression analysis. Characteristics of functional pathways, somatic mutations, immunity features, and anti-tumor therapy related to the MRL-model were evaluated using abundant algorithms, such as GSEA, ssGSEA, GSVA, maftools, CIBERSORT, xCELL, MCPcounter, ESTIMATE, TIDE, pRRophetic and so on.

RESULTS

We found 52 differentially expressed MRGs and 22 prognostic MRGs in OC. Enrichment analysis revealed that MRGs were involved in mitophagy. Nine prognostic MRLs were identified and eight optimal MRLs combinations were screened to establish the MRL-model. The MRL-model stratified patients into high- and low-risk groups and remained a prognostic factor (P < 0.05) with independent value (P < 0.05) in TCGA and GEO. We observed that OC patients in the high-risk group also had the unfavorable survival in consideration of clinicopathological parameters. The Nomogram was plotted to make the prediction results more intuitive and readable. The two risk groups were enriched in discrepant functional pathways (such as Wnt signaling pathway) and immunity features. Besides, patients in the low-risk group may be more sensitive to immunotherapy (P = 0.01). Several chemotherapeutic drugs (Paclitaxel, Veliparib, Rucaparib, Axitinib, Linsitinib, Saracatinib, Motesanib, Ponatinib, Imatinib and so on) were found with variant sensitivity between the two risk groups. The established ceRNA network indicated the underlying mechanisms of MRLs.

CONCLUSIONS

Our study revealed the roles of MRLs and MRL-model in expression, prognosis, chemotherapy, immunotherapy, and molecular mechanism of OC. Our findings were able to stratify OC patients with high risk, unfavorable prognosis and variant treatment sensitivity, thus improving clinical outcomes for OC patients.

Peripheral and tumor-infiltrating immune cells are correlated with patient outcomes in ovarian cancer

OBJECTIVE

At present, there is still a lack of reliable biomarkers for ovarian cancer (OC) to guide prognosis prediction and accurately evaluate the dominant population of immunotherapy. In recent years, the relationship between peripheral blood markers and tumor-infiltrating immune cells (TICs) with cancer has attracted much attention. However, the relationship between the survival of OC patients and intratumoral- or extratumoral-associated immune cells remains controversial.

METHODS

In this study, four machine-learning algorithms were used to predict overall survival in OC patients based on peripheral blood indicators. To further screen out immune-related gene and molecular targets, we systematically explored the correlation between TICs and OC patient survival based on The Cancer Genome Atlas database. Using the TICs score method, patients were divided into a low immune infiltrating cell group and a high immune infiltrating cell group.

RESULTS

The results showed that there was a significant statistical significance between the peripheral blood indicators and the survival prognosis of OC patients. Survival analysis showed that TICs play a crucial role in the survival of OC patients. Four core genes, CXCL9, CD79A, MS4A1, and MZB1, were identified by cross-PPI and COX regression analysis. Further analysis found that these genes were significantly associated with both TICs and survival in OC patients.

CONCLUSIONS

These results suggest that both peripheral blood markers and TICs can be used as prognostic predictors in patients with OC, and CXCL9, CD79A, MS4A1, and MZB1 may be potential therapeutic targets for OC immunotherapy.

Prognostic nomogram that predicts progression-free survival and overall survival of patients with ovarian clear cell carcinoma

Objectives

We aims to develop nomograms to predict progression-free survival (PFS) and overall survival (OS) in patients with ovarian clear cell carcinoma (OCCC) after primary treatment and compare the predictive accuracy with the currently used International Federation of Gynecology and Obstetrics (FIGO) system.

Methods

We collected data from 358 Chinese patients diagnosed with OCCC and who underwent standard treatment at our hospital. Patients diagnosed from 1982-9 to 2011-12 were classified as the training group and patients diagnosed from 2012-1 to 2016-11 were classified as the validation group. Nomograms were developed based on the training group and was validated in the validation group. The predictive performance was determined by concordance index and calibration curve.

Results

The most predictive nomogram for PFS was constructed using variables: thrombosis, the FIGO staging, residual of the tumor and distant metastasis, with a concordance index of 0.738. While the nomogram for OS consisted of thrombosis, lymph node metastasis, residual of the tumor, malignant ascites/washing, and platinum resistance, with a concordance index of 0.835. The nomograms were internally validated by concordance indexes of 0.775 and 0.807 for predicting PFS and OS, respectively. In comparison, the concordance statistics for OS based on the FIGO staging was significantly lower (P&lt;0.05).

Conclusion

We have established two prognostic nomograms for recurrence and long-term survival in patients with OCCC after primary treatment in a large Chinese center and validated them in patients from the same center. This tool used variables specifically related to OCCC and was more accurate than the FIGO system. It is relatively easy to use in clinic for patient counseling, postoperative management, and follow-up for individual patients.

Predicting preterm birth through vaginal microbiota, cervical length, and WBC using a machine learning model

An association between the vaginal microbiome and preterm birth has been reported. However, in practice, it is difficult to predict premature birth using the microbiome because the vaginal microbial community varies highly among samples depending on the individual, and the prediction rate is very low. The purpose of this study was to select markers that improve predictive power through machine learning among various vaginal microbiota and develop a prediction algorithm with better predictive power that combines clinical information. As a multicenter case–control study with 150 Korean pregnant women with 54 preterm delivery group and 96 full-term delivery group, cervicovaginal fluid was collected from pregnant women during mid-pregnancy. Their demographic profiles (age, BMI, education level, and PTB history), white blood cell count, and cervical length were recorded, and the microbiome profiles of the cervicovaginal fluid were analyzed. The subjects were randomly divided into a training (n = 101) and a test set (n = 49) in a two-to-one ratio. When training ML models using selected markers, five-fold cross-validation was performed on the training set. A univariate analysis was performed to select markers using seven statistical tests, including the Wilcoxon rank-sum test. Using the selected markers, including Lactobacillus spp., Gardnerella vaginalis, Ureaplasma parvum, Atopobium vaginae, Prevotella timonensis, and Peptoniphilus grossensis, machine learning models (logistic regression, random forest, extreme gradient boosting, support vector machine, and GUIDE) were used to build prediction models. The test area under the curve of the logistic regression model was 0.72 when it was trained with the 17 selected markers. When analyzed by combining white blood cell count and cervical length with the seven vaginal microbiome markers, the random forest model showed the highest test area under the curve of 0.84. The GUIDE, the single tree model, provided a more reasonable biological interpretation, using the 10 selected markers (A. vaginae, G. vaginalis, Lactobacillus crispatus, Lactobacillus fornicalis, Lactobacillus gasseri, Lactobacillus iners, Lactobacillus jensenii, Peptoniphilus grossensis, P. timonensis, and U. parvum), and the covariates produced a tree with a test area under the curve of 0.77. It was confirmed that the association with preterm birth increased when P. timonensis and U. parvum increased (AUC = 0.77), which could also be explained by the fact that as the number of Peptoniphilus lacrimalis increased, the association with preterm birth was high (AUC = 0.77). Our study demonstrates that several candidate bacteria could be used as potential predictors for preterm birth, and that the predictive rate can be increased through a machine learning model employing a combination of cervical length and white blood cell count information.

Construction and validation of a transcription factors-based prognostic signature for ovarian cancer

BACKGROUND

Ovarian cancer (OC) is one of the most common and lethal malignant tumors worldwide and the prognosis of OC remains unsatisfactory. Transcription factors (TFs) are demonstrated to be associated with the clinical outcome of many types of cancers, yet their roles in the prognostic prediction and gene regulatory network in patients with OC need to be further investigated.

METHODS

TFs from GEO datasets were collected and analyzed. Differential expression analysis, WGCNA and Cox-LASSO regression model were used to identify the hub-TFs and a prognostic signature based on these TFs was constructed and validated. Moreover, tumor-infiltrating immune cells were analyzed, and a nomogram containing age, histology, FIGO_stage and TFs-based signature were established. Potential biological functions, pathways and the gene regulatory network of TFs in signature was also explored.

RESULTS

In this study, 6 TFs significantly associated with the prognosis of OC were identified. These TFs were used to build up a TFs-based signature for predicting the survival of patients with OC. Patients with OC in training and testing datasets were divided into high-risk and low-risk groups, according to the median value of risk scores determined by the signature. The two groups were further used to validate the performance of the signature, and the results showed the TFs-based signature had effective prediction ability. Immune infiltrating analysis was conducted and abundance of B cells naïve, T cells CD4 memory resting, Macrophages M2 and Mast cells activated were significantly higher in high-risk group. A nomogram based on the signature was established and illustrated good predictive efficiencies for 1, 2, and 3-year overall survival. Furthermore, the construction of the TFs-target gene regulatory network revealed the potential mechanisms of TFs in OC.

CONCLUSIONS

To our best knowledge, it is for the first time to develop a prognostic signature based on TFs in OC. The TFs-based signature is proven to be effective in predicting the survival of patients with OC. Our study may facilitate the clinical decision-making for patients with OC and help to elucidate the underlying mechanism of TFs in OC.

Toward More Comprehensive Homologous Recombination Deficiency Assays in Ovarian Cancer Part 2: Medical Perspectives

Simple Summary

High-grade serous ovarian cancer (HGSOC—the most frequent and aggressive form of ovarian cancer) represents an important challenge for clinicians. Half of HGSOC cases exhibit homologous recombination deficiency (HRD), mainly through alterations in BRCA1 and BRCA2. This leads to sensitivity to PARP inhibitors, a novel class of breakthrough molecules that improved HGSOC prognoses. To date, three companion diagnostic assays have received FDA approval for the evaluation of HRD status, but their use remains controversial. In this companion review (Part 1: Technical considerations; Part 2: Medical perspectives), we develop an integrative perspective, from translational research to clinical application, that could help physicians and researchers manage HGSOC.

Abstract

High-grade serous ovarian cancer (HGSOC) is the most frequent and aggressive form of ovarian cancer, representing an important challenge for clinicians. Half of HGSOC cases have homologous recombination deficiency (HRD), which has specific causes (mainly alterations in BRCA1/2, but also other alterations encompassed by the BRCAness concept) and consequences, both at molecular (e.g., genomic instability) and clinical (e.g., sensitivity to PARP inhibitor) levels. Based on its prevalence and clinical impact, HRD status merits investigation. To date, three PARP inhibitors have received FDA/EMA approval. For some approvals, the presence of specific molecular alterations is required. Three companion diagnostic (CDx) assays based on distinct technical and medical considerations have received FDA approval to date. However, their use remains controversial due to their technical and medical limitations. In this companion and integrated review, we take a “bench-to-bedside” perspective on HRD definition and evaluation in the context of HGSOC. Part 1 of the review adopts a molecular perspective regarding technical considerations and the development of CDx. Part 2 focuses on the clinical impact of HRD evaluation, primarily through currently validated CDx and prescription of PARP inhibitors, outlining achievements, limitations and medical perspectives.

Nonalcoholic fatty liver disease and early prediction of gestational diabetes using machine learning methods

Background/Aims

To develop an early prediction model for gestational diabetes mellitus (GDM) using machine learning and to evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model.

Methods

This prospective cohort study evaluated pregnant women for NAFLD using ultrasound at 10–14 weeks and screened them for GDM at 24–28 weeks of gestation. The clinical variables before 14 weeks were used to develop prediction models for GDM (setting 1, conventional risk factors; setting 2, addition of new risk factors in recent guidelines; setting 3, addition of routine clinical variables; setting 4, addition of NALFD-associated variables, including the presence of NAFLD and laboratory results; and setting 5, top 11 variables identified from a stepwise variable selection method). The predictive models were constructed using machine learning methods, including logistic regression, random forest, support vector machine, and deep neural networks.

Results

Among 1,443 women, 86 (6.0%) were diagnosed with GDM. The highest performing prediction model among settings 1–4 was setting 4, which included both clinical and NAFLD-associated variables (area under the receiver operating characteristic curve [AUC] 0.563–0.697 in settings 1–3 vs. 0.740–0.781 in setting 4). Setting 5, with top 11 variables (which included NAFLD and hepatic steatosis index), showed similar predictive power to setting 4 (AUC 0.719–0.819 in setting 5, P=not significant between settings 4 and 5).

Conclusions

We developed an early prediction model for GDM using machine learning. The inclusion of NAFLD-associated variables significantly improved the performance of GDM prediction. (ClinicalTrials.gov Identifier: NCT02276144)

M2 Macrophage-Based Prognostic Nomogram for Gastric Cancer After Surgical Resection

A good prediction model is useful to accurately predict patient prognosis. Tumor-node-metastasis (TNM) staging often cannot accurately predict prognosis when used alone. Some researchers have shown that the infiltration of M2 macrophages in many tumors indicates poor prognosis. This approach has the potential to predict prognosis more accurately when used in combination with TNM staging, but there is less research in gastric cancer. A multivariate analysis demonstrated that CD163 expression, TNM staging, age, and gender were independent risk factors for overall survival. Thus, these parameters were assessed to develop the nomogram in the training data set, which was tested in the validation and whole data sets. The model showed a high degree of discrimination, calibration, and good clinical benefit in the training, validation, and whole data sets. In conclusion, we combined CD163 expression in macrophages, TNM staging, age, and gender to develop a nomogram to predict 3- and 5-year overall survivals after curative resection for gastric cancer. This model has the potential to provide further diagnostic and prognostic value for patients with gastric cancer.

Prediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study

BACKGROUND

Limited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness.

OBJECTIVE

This study aimed to develop and validate machine learning models based on clinical features to assess the risk of severe disease and triage for COVID-19 patients upon hospital admission.

METHODS

This retrospective multicenter cohort study included patients with COVID-19 who were released from quarantine until April 30, 2020, in Korea. A total of 5628 patients were included in the training and testing cohorts to train and validate the models that predict clinical severity and the duration of hospitalization, and the clinical severity score was defined at four levels: mild, moderate, severe, and critical.

RESULTS

Out of a total of 5601 patients, 4455 (79.5%), 330 (5.9%), 512 (9.1%), and 301 (5.4%) were included in the mild, moderate, severe, and critical levels, respectively. As risk factors for predicting critical patients, we selected older age, shortness of breath, a high white blood cell count, low hemoglobin levels, a low lymphocyte count, and a low platelet count. We developed 3 prediction models to classify clinical severity levels. For example, the prediction model with 6 variables yielded a predictive power of >0.93 for the area under the receiver operating characteristic curve. We developed a web-based nomogram, using these models.

CONCLUSIONS

Our prediction models, along with the web-based nomogram, are expected to be useful for the assessment of the onset of severe and critical illness among patients with COVID-19 and triage patients upon hospital admission.

Development of Machine Learning Models to Predict Platinum Sensitivity of High-Grade Serous Ovarian Carcinoma

To support the implementation of individualized disease management, we aimed to develop machine learning models predicting platinum sensitivity in patients with high-grade serous ovarian carcinoma (HGSOC). We reviewed the medical records of 1002 eligible patients. Patients' clinicopathologic characteristics, surgical findings, details of chemotherapy, treatment response, and survival outcomes were collected. Using the stepwise selection method, based on the area under the receiver operating characteristic curve (AUC) values, six variables associated with platinum sensitivity were selected: age, initial serum CA-125 levels, neoadjuvant chemotherapy, pelvic lymph node status, involvement of pelvic tissue other than the uterus and tubes, and involvement of the small bowel and mesentery. Based on these variables, predictive models were constructed using four machine learning algorithms, logistic regression (LR), random forest, support vector machine, and deep neural network; the model performance was evaluated with the five-fold cross-validation method. The LR-based model performed best at identifying platinum-resistant cases with an AUC of 0.741. Adding the FIGO stage and residual tumor size after debulking surgery did not improve model performance. Based on the six-variable LR model, we also developed a web-based nomogram. The presented models may be useful in clinical practice and research.

Development and validation for prognostic nomogram of epithelial ovarian cancer recurrence based on circulating tumor cells and epithelial-mesenchymal transition

We aimed to determine the prognosis value of circulating tumor cells (CTCs) undergoing epithelial–mesenchymal transition in epithelial ovarian cancer (EOC) recurrence. We used CanPatrol CTC-enrichment technique to detect CTCs from blood samples and classify subpopulations into epithelial, mesenchymal, and hybrids. To construct nomogram, prognostic factors were selected by Cox regression analysis. Risk stratification was performed through Kaplan–Meier analysis among the training group (n = 114) and validation group (n = 38). By regression screening, both CTC counts (HR 1.187; 95% CI 1.098–1.752; p = 0.012) and M-CTC (HR 1.098; 95% CI 1.047–1.320; p = 0.009) were demonstrated as independent factors for recurrence. Other variables including pathological grade, FIGO stage, lymph node metastasis, ascites, and CA-125 were also selected (p < 0.005) to construct nomogram. The C-index of internal and external validation for nomogram was 0.913 and 0.874. We found significant predictive values for the nomogram with/without CTCs (AUC 0.8705 and 0.8097). Taking CTC counts and M-CTC into separation, the values were 0.8075 and 0.8262. Finally, survival curves of risk stratification based on CTC counts (p = 0.0241), M-CTC (p = 0.0107), and the nomogram (p = 0.0021) were drawn with significant differences. In conclusion, CTCs could serve as a novel factor for EOC prognosis. Nomogram model constructed by CTCs and other clinical parameters could predict EOC recurrence and perform risk stratification for clinical decision-making.

Trial registration Chinese Clinical Trial Registry, ChiCTR-DDD-16009601, October 25, 2016.

Sphingolipids as multifaceted mediators in ovarian cancer

Ovarian cancer is the most lethal gynaecological malignancy. It is commonly diagnosed at advanced stage when it has metastasised to the abdominal cavity and treatment becomes very challenging. While current standard therapy involving debulking surgery and platinum + taxane-based chemotherapy is associated with high response rates initially, the large majority of patients relapse and ultimately succumb to chemotherapy-resistant disease. In order to improve survival novel strategies for early detection and therapeutics against treatment-refractory disease are urgently needed. A promising new target against ovarian cancer is the sphingolipid pathway which is commonly hijacked in cancer to support cell proliferation and survival and has been shown to promote chemoresistance and metastasis in a wide range of malignant neoplasms. In particular, the sphingosine kinase 1-sphingosine 1-phosphate receptor 1 axis has been shown to be altered in ovarian cancer in multiple ways and therefore represents an attractive therapeutic target. Here we review the roles of sphingolipids in ovarian cancer progression, metastasis and chemoresistance, highlighting novel strategies to target this pathway that represent potential avenues to improve patient survival.

Prognosis for intrahepatic cholangiocarcinoma patients treated with postoperative adjuvant transcatheter hepatic artery chemoembolization

OBJECTIVE

We used meta-analysis to evaluate the efficacy of transcatheter hepatic arterial chemoembolization (TACE) for the treatment of intrahepatic cholangiocarcinoma (ICC).

METHODS

We performed the meta-analysis using the R 3.12 software and the quality evaluation of data using the Newcastle-Ottawa Scale. The main outcomes were recorded as 1-year overall survival (OS), 3-year OS, 5-year OS, and hazard ratio (HR) of TACE treatment or non-TACE treatment. The heterogeneity test was performed using the Q-test based on chi-square and I² statistics. Egger's test was used to test the publication bias. The odds ratio or HR and 95% confidence interval (CI) were used to represent the effect index.

RESULTS

Nine controlled clinical trials involving 1724 participants were included in this study; patients came mainly from China, Italy, South Korea, and Germany. In the OS meta-analysis, the 1-year and 3-year OS showed significant heterogeneity, but not the 5-year OS. TACE increased the 1-year OS (odds ratio = 2.66, 95% CI: 1.10-6.46) of the patients with ICC, but the 3- and 5-year OS rates were not significantly increased. The results had no publication bias, but the stability was weak. The HR had significant heterogeneity (I² = 0%, P= 0.54). TACE significantly decreased the HR of ICC patients (HR = 0.59, 95% CI: 0.48-0.73). The results had no publication bias, and the stability was good.

CONCLUSIONS

Treatment with TACE is effective for patients with ICC. Regular updating and further research and analysis still need to be carried out.

Can we predict who lives long with ovarian cancer?

Women with ovarian cancer benefit from individualized management that incorporates advanced imaging technologies, sophisticated cytoreductive surgery integrated with combination chemotherapy, genetic risk assessment, and tumor molecular profiling. However, advanced ovarian cancer remains a highly lethal disease because of early peritoneal dissemination, rapid development of resistance to key therapeutic agents, and evasion of the host immune response. Over the last 15 years, several models and nomograms have been developed to predict surgical outcomes, progression-free survival, or overall survival on the basis of clinical and pathologic data available at the primary diagnosis and recurrence. Each of these models has its strengths and limitations, and they provide a basis for future models that will incorporate functional imaging and molecular characteristics.