Impact of sex, body mass index and initial pathologic diagnosis age on the incidence and prognosis of different types of cancer

Prognostic factors and prognostic model of non-metastatic clear cell renal cell carcinoma

INTRODUCTION

Although there are some established prognostic evaluation models for clear cell renal cell carcinoma (ccRCC), more robust postoperative prognostic evaluation model is urgently needed. Our study intends to explore new clinical and pathological prognostic factors related to non-metastatic ccRCC, which help to establish a better prognostic risk evaluation model in non-metastatic ccRCC.

PATIENTS AND METHODS

A retrospective cohort study was conducted in non-metastatic ccRCC patients spanning from 2010 to 2018. Clinical and pathological factors of these patients were collected. Cox regression analysis was employed to assess the relationship between these factors and disease-free survival (DFS), and a nomogram risk prediction model was also constructed.

RESULTS

A total of 1467 patients were ultimately included, comprising 994 men (67.8%), with 800 patients aged between 40 and 60 years old (54.5%), and 80 patients (5.5%) experiencing relapse or metastasis of ccRCC within three years after operation. The follow-up duration ranged from 39 to 146 months. Univariate and multivariate Cox regression analysis identified five independent prognostic factors of DFS (P < 0.05) including sex, tumor maximum diameter, T stage, lactate dehydrogenase (LDH), and basophils. Leveraging these five factors, we established a prognostic evaluation model demonstrating good predictive efficacy.

CONCLUSION

Male, tumor maximum diameter, T stage, LDH, and basophils serve as prognostic indicators for DFS in patients with non-metastatic ccRCC. Patients with high scores based on our model exhibit an elevated likelihood of recurrence or metastasis, thereby potentially selecting postoperative patients with high risk for adjuvant therapy.

Development and validation of a prognostic nomogram for patients with ganglioneuroblastoma: A SEER-based study

Background

The objective of this study was to construct a prognostic nomogram for ganglioneuroblastoma (GNB), as the prognosis of GNB is difficult to accurately predict before therapy.

Methods

The data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The patients included in this study were randomly divided into a development group and a validation group at a ratio of 7:3. Univariate and multivariate Cox regression analyses were used to filter the variables. Receiver operating characteristic (ROC) curves and calibration curves were used to assess the nomogram. All patients were redivided into two groups based on their nomogram total points, and overall survival was compared.

Results

A total of 1194 GNB patients were retrospectively included, with 835 and 359 patients in the development and validation groups, respectively. Five independent prognostic factors, including age, primary tumor site, SEER stage, surgery and chemotherapy, were screened out and included in the nomogram. The consistency index (C-index) of the Cox regression model was 0.862 and 0.827 in the development group and the validation group, respectively. The areas under the receiver operating characteristic (ROC) curve (AUC) showed that the nomogram had good accuracy in predicting 3-, 5- and 10-year overall survival for GNB patients. The calibration curves of the nomogram showed good agreement between the predicted outcomes and the actual observations. The Kaplan-Meier (KM) survival curves revealed that patients with nomogram scores below the median had a better prognosis.

Conclusions

Age, primary tumor site, SEER stage, surgery and chemotherapy may be independent prognostic factors for GNB. We constructed a nomogram based on the SEER database to predict the prognosis of GNB, but further optimization by adding more risk factors is needed for clinical application.

A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma

Background

Malignant pleural mesothelioma (MPM) is a rare disease with limited treatment and poor prognosis, and a precise and reliable means to predicting MPM remains lacking for clinical use.

Methods

In the population-based cohort study, we collected clinical characteristics from the Surveillance, Epidemiology, and End Results (SEER) database. According to the time of diagnosis, the SEER data were divided into 2 cohorts: the training cohort (from 2010 to 2016) and the test cohort (from 2017 to 2019). The training cohort was used to train a deep learning-based predictive model derived from DeepSurv theory, which was validated by both the training and the test cohorts. All clinical characteristics were included and analyzed using Cox proportional risk regression or Kaplan-Meier curve to determine the risk factors and protective factors of MPM.

Results

The survival model included 3,130 cases (2,208 in the training cohort and 922 in the test cohort). As for model's performance, the area under the receiver operating characteristics curve (AUC) was 0.7037 [95% confidence interval (CI): 0.7030-0.7045] in the training cohort and 0.7076 (95% CI: 0.7067-0.7086) in the test cohort. Older age; male sex, sarcomatoid mesothelioma; and T4, N2, and M1 stage tended to be the risk factors for survival. Meanwhile, epithelioid mesothelioma, surgery, radiotherapy, and chemotherapy tended to be the protective factors. The median overall survival (OS) of patients who underwent surgery combined with radiotherapy was the longest, followed by those who underwent a combination of surgery, radiotherapy, and chemotherapy.

Conclusions

Our deep learning-based model precisely could predict the survival of patients with MPM; moreover, multimode combination therapy might provide more meaningful survival benefits.

Web-based nomogram and risk stratification system constructed for predicting the overall survival of older adults with primary kidney cancer after surgical resection

BACKGROUND

Kidney cancer (KC) is one of the most common malignant tumors in adults which particularly affects the survival of elderly patients. We aimed to construct a nomogram to predict overall survival (OS) in elderly KC patients after surgery.

METHODS

Information on all primary KC patients aged more than 65 years and treated with surgery between 2010 and 2015 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analysis was used to identify the independent prognostic factors. Consistency index (C-index), receiver operating characteristic curve (ROC), the area under curve (AUC), and calibration curve were used to assess the accuracy and validity of the nomogram. Comparison of the clinical benefits of nomogram and the TNM staging system is done by decision curve analysis (DCA) and time-dependent ROC.

RESULTS

A total of 15,989 elderly KC patients undergoing surgery were included. All patients were randomly divided into training set (N = 11,193, 70%) and validation set (N = 4796, 30%). The nomogram produced C-indexes of 0.771 (95% CI 0.751-0.791) and 0.792 (95% CI 0.763-0.821) in the training and validation sets, respectively, indicating that the nomogram has excellent predictive accuracy. The ROC, AUC, and calibration curves also showed the same excellent results. In addition, DCA and time-dependent ROC showed that the nomogram outperformed the TNM staging system with better net clinical benefits and predictive efficacy.

CONCLUSIONS

Independent influencing factors for postoperative OS in elderly KC patients were sex, age, histological type, tumor size, grade, surgery, marriage, radiotherapy, and T-, N-, and M-stage. The web-based nomogram and risk stratification system could assist surgeons and patients in clinical decision-making.

Cardiorespiratory fitness and BMI measured in youth and 5-year mortality after site-specific cancer diagnoses in men-A population-based cohort study with register linkage

BACKGROUND

Our aim was to assess associations between cardiorespiratory fitness (CRF) and body mass index (BMI) in youth and 5-year mortality after site-specific cancer diagnoses in men.

METHODS

Men with cancer from a population who underwent military conscription at ages 16-25 during 1968-2005 in Sweden were included. CRF was assessed as maximal aerobic workload on a cycle ergometer test and was classified as low, moderate, or high. BMI (kg/m2 ) was classified as underweight (<18.5), normal weight (18.5-24.9), overweight (25-29.9), or obesity (>30). Conscription data were linked with register data on cancer diagnosis and mortality. Analyses included CRF, BMI, date of diagnosis, and age, year, and center for conscription.

RESULTS

A total of 84,621 cancer cases were included. Mean age at diagnosis was 52 years. Follow-up data were available during a mean of 6.5 years. There were linear protective associations between CRF and mortality after any cancer diagnosis (hazard ratio [HR] for high vs. low CRF 0.70), malignant skin cancer (HR 0.80), non-Hodgkin lymphoma (HR 0.78), and cancer in the lungs (HR 0.80), head and neck (HR 0.68), pancreas (HR 0.83), stomach (HR 0.78), liver (HR 0.84), rectum (HR 0.79), and bladder (HR 0.71). Overweight and/or obesity were associated with increased mortality after any cancer (HR for obesity vs. normal weight 1.89), malignant skin cancer (HR 2.03), Hodgkin lymphoma (HR 2.86) and cancer in the head and neck (HR 1.38), thyroid (HR 3.04), rectum (HR 1.53), kidney (HR 1.90), bladder (HR 2.10), and prostate (HR 2.44).

CONCLUSION

We report dose-dependent associations between CRF and BMI in youth and mortality after site-specific cancer diagnoses in men. The associations with mortality could be due to both cancer inhibition and an improved tolerance to withstand cancer treatment. These results strengthen the incentive for public health efforts aimed at establishing a high CRF and normal weight in youth.

Deciphering key genes involved in cisplatin resistance in kidney renal clear cell carcinoma through a combined in silico and in vitro approach

The low survival rate of Kidney renal clear cell carcinoma (KIRC) patients is largely attributed to cisplatin resistance. Rather than focusing solely on individual proteins, exploring protein-protein interactions could offer greater insight into drug resistance. To this end, a series of in silico and in vitro experiments were conducted to identify hub genes in the intricate network of cisplatin resistance-related genes in KIRC chemotherapy. The genes involved in cisplatin resistance across KIRC were retrieved from the National Center for Biotechnology Information (NCBI) database using search terms as "Kidney renal clear cell carcinoma" and "Cisplatin resistance". The genes retrieved were analyzed for hub gene identification using the STRING database and Cytoscape tool. Expression and promoter methylation profiling of the hub genes was done using UALCAN, GEPIA, OncoDB, and HPA databases. Mutational, survival, functional enrichment, immune cell infiltration, and drug prediction analyses of the hub genes were performed using the cBioPortal, GEPIA, GSEA, TIMER, and DrugBank databases. Lastly, expression and methylation levels of the hub genes were validated on two cisplatin-resistant RCC cell lines (786-O and A-498) and a normal renal tubular epithelial cell line (HK-2) using two high throughput techniques, including targeted bisulfite sequencing (bisulfite-seq) and RT-qPCR. A total of 124 genes were identified as being associated with cisplatin resistance in KIRC. Out of these genes, MCL1, IGF1R, CCND1, and PTEN were identified as hub genes and were found to have significant (p < 0.05) variations in their mRNA and protein expressions and effects on the overall survival (OS) of the KIRC patients. Moreover, an aberrant promoter methylation pattern was found to be associated with the dysregulation of the hub genes. In addition to this, hub genes were also linked with different cisplatin resistance-causing pathways. Thus, hub genes can be targeted with Alvocidib, Estradiol, Tretinoin, Capsaicin, Dronabinol, Metribolone, Calcitriol, Acetaminophen, Acitretin, Cyclosporine, Azacitidine, Genistein, and Resveratrol drugs. As the pathogenesis of KIRC is complex, targeting hub genes and associated pathways involved in cisplatin resistance could bring a milestone change in the drug discovery and management of drug resistance, which might uplift overall survival among KIRC patients.

Establishment of predictive nomogram and web-based survival risk calculator for malignant pleural mesothelioma: A SEER database analysis

Background

Malignant pleural mesothelioma (MPM) is an uncommon condition with limited available therapies and dismal prognoses. The purpose of this work was to create a multivariate clinical prognostic nomogram and a web-based survival risk calculator to forecast patients’ prognoses.

Methods

Using a randomization process, training and validation groups were created for a retrospective cohort study that examined the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 for individuals diagnosed with MPM (7:3 ratio). Overall survival (OS) and cancer-specific survival (CSS) were the primary endpoints. Clinical traits linked to OS and CSS were identified using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, which was also utilized to develop nomogram survival models and online survival risk calculators. By charting the receiver operating characteristic (ROC), consistency index (C-index), calibration curve, and decision curve analysis (DCA), the model’s performance was assessed. The nomogram was used to classify patients into various risk categories, and the Kaplan-Meier method was used to examine each risk group’s survival rate.

Results

The prognostic model comprised a total of 1978 patients. For the total group, the median OS and CSS were 10 (9.4-10.5) and 11 (9.4-12.6) months, respectively. As independent factors for OS and CSS, age, gender, insurance, histology, T stage, M stage, surgery, and chemotherapy were chosen. The calibration graphs demonstrated good concordance. In the training and validation groups, the C-indices for OS and CSS were 0.729, 0.717, 0.711, and 0.721, respectively. Our nomogram produced a greater clinical net benefit than the AJCC 7th edition, according to DCA and ROC analysis. According to the cut-off values of 171 for OS and 189 for CSS of the total scores from our nomogram, patients were classified into two risk groups. The P-value &lt; 0.001 on the Kaplan-Meier plot revealed a significant difference in survival between the two patient groups.

Conclusions

Patient survival in MPM was correctly predicted by the risk evaluation model. This will support clinicians in the practice of individualized medicine.

Machine Learning of Dose-Volume Histogram Parameters Predicting Overall Survival in Patients with Cervical Cancer Treated with Definitive Radiotherapy

Purpose

To analyze the effects of dosimetric parameters and clinical characteristics on overall survival (OS) by machine learning algorithms.

Methods and Materials

128 patients with cervical cancer were treated with definitive pelvic radiotherapy with or without chemotherapy followed by image-guided brachytherapy. The elastic-net models with integrating DVH parameters and baseline clinical factors, only DVH parameters and only baseline clinical factors were constructed in 5-folds cross-validations for 100 iteration bootstrapping, and then were compared using concordance index (C-index) criteria. Finally, the selected important factors were used to build multivariable Cox-pH models for OS and also shown in nomograms for clinical usage.

Results

The median OS occurred was 25.78 months with 25 (19.53%) deaths. The elastic-net models integrating clinical and DVH factors had the best prediction performances (C-index 0.76 in the train set and C-index 0.74 in the test set). Three important factors were selected, including baseline hemoglobin level as the protective factor, primary tumor volume (GTV_P) volume, and body V5 as the risk factors. The final multivariable Cox-pH models were constructed using these important factors and had prediction performance (C-index: 0.78, 95%CI: 0.73–0.81).

Conclusions

This is the first attempt to establish elastic-net models to study the contributions of DVH parameters for predicting OS in patients with cervical cancer. These results can facilitate individualized tailoring of radiation treatment in cervical cancer patients.

A Web-Based Prediction Model for Cancer-Specific Survival of Middle-Aged Patients With Non-metastatic Renal Cell Carcinoma: A Population-Based Study

Background

Renal cell carcinoma (RCC) is one of the most common cancers in middle-aged patients. We aimed to establish a new nomogram for predicting cancer-specific survival (CSS) in middle-aged patients with non-metastatic renal cell carcinoma (nmRCC).

Methods

The clinicopathological information of all patients from 2010 to 2018 was downloaded from the SEER database. These patients were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate COX regression analyses were used to identify independent risk factors for CSS in middle-aged patients with nmRCC in the training set. Based on these independent risk factors, a new nomogram was constructed to predict 1-, 3-, and 5-year CSS in middle-aged patients with nmRCC. Then, we used the consistency index (C-index), calibration curve, and area under receiver operating curve (AUC) to validate the accuracy and discrimination of the model. Decision curve analysis (DCA) was used to validate the clinical application value of the model.

Results

A total of 27,073 patients were included in the study. These patients were randomly divided into a training set (N = 18,990) and a validation set (N = 8,083). In the training set, univariate and multivariate Cox regression analysis indicated that age, sex, histological tumor grade, T stage, tumor size, and surgical method are independent risk factors for CSS of patients. A new nomogram was constructed to predict patients' 1-, 3-, and 5-year CSS. The C-index of the training set and validation set were 0.818 (95% CI: 0.802-0.834) and 0.802 (95% CI: 0.777-0.827), respectively. The 1 -, 3 -, and 5-year AUC for the training and validation set ranged from 77.7 to 80.0. The calibration curves of the training set and the validation set indicated that the predicted value is highly consistent with the actual observation value, indicating that the model has good accuracy. DCA also suggested that the model has potential clinical application value.

Conclusion

We found that independent risk factors for CSS in middle-aged patients with nmRCC were age, sex, histological tumor grade, T stage, tumor size, and surgery. We have constructed a new nomogram to predict the CSS of middle-aged patients with nmRCC. This model has good accuracy and reliability and can assist doctors and patients in clinical decision making.

A web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study

Background

The number of elderly patients with early renal cell carcinoma (RCC) is on the rise. However, there is still a lack of accurate prediction models for the prognosis of early RCC in elderly patients. It is necessary to establish a new nomogram to predict the prognosis of elderly patients with early RCC.

Methods

The data of patients aged above 65 years old with TNM stage I and II RCC were downloaded from the SEER database between 2010 and 2018. The patients from 2010 to 2017 were randomly assigned to the training cohort (n = 7233) and validation cohort (n = 3024). Patient data in 2018(n = 1360) was used for external validation. We used univariable and multivariable Cox regression model to evaluate independent prognostic factors and constructed a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) rates of patients with early-stage RCC. Multiple parameters were used to validate the nomogram, including the consistency index (C-index), the calibration plots, the area under the receiver operator characteristics (ROC) curve, and the decision curve analysis (DCA).

Results

The study included a total of 11,617 elderly patients with early RCC. univariable and multivariable Cox regression analysis based on predictive variables such as age, sex, histologic type, Fuhrman grade, T stage, surgery type, tumors number, tumor size, and marriage were included to establish a nomogram. The C-index of the training cohort and validation cohort were 0.748 (95% CI: 0.760–0.736) and 0.744 (95% CI: 0.762–0.726), respectively. In the external validation cohort, C-index was 0.893 (95% CI: 0.928–0.858). The calibration plots basically coincides with the diagonal, indicating that the observed OS was almost equal to the predicted OS. It was shown in DCA that the nomogram has more important clinical significance than the traditional TNM stage.

Conclusion

A novel nomogram was developed to assess the prognosis of an elderly patient with early RCC and to predict prognosis and formulate treatment and follow-up strategies.

A novel clinical signature predicts the survival of elderly patients with oral squamous cell carcinoma

BACKGROUND

The risk factors for the survival of elderly patients with oral squamous cell carcinoma (OSCC) are multifarious. Here, we developed a novel clinical signature to serve as an indicator of prognosis in these patients.

MATERIALS AND METHODS

Clinicopathological data were collected for 554 patients aged ≥ 60 years who were treated for primary OSCC. Overall survival (OS), disease-specific survival, and disease-free survival were the primary outcomes.

RESULTS

Multivariate cox regression analysis showed that high N stage, low hemoglobin level, low body mass index (BMI), and high neutrophil-to-lymphocyte ratio (NLR) showed a poor survival (P < 0.05). A nomogram was constructed with a c-index of 0.702.

CONCLUSION

A novel clinical signature including hemoglobin level, BMI, and NLR, which are obtained through noninvasive examinations can be used as prognostic indicators in clinical practice for elderly patients with OSCC.

Impact of Body Mass Index on Tumor Recurrence in Patients Undergoing Liver Resection for Perihilar Cholangiocarcinoma (pCCA)

Simple Summary

Perihilar cholangiocarcinoma (pCCA) is a relatively rare and aggressive hepatobiliary tumor with a general poor prognosis. Surgical therapy remains the only curative treatment option with the best prospects for long-term survival. However, tumor recurrence is frequent, and is associated with a poor prognosis. The identification of risk factors as well as appropriate selection of surgical candidates is essential to accurately predict prognosis and to maximize survival while decreasing tumor recurrence rates. Previous studies have already established a link between an increased BMI and the occurrence of various tumors. At present, data on BMI-associated long-term outcome following curative liver resection in pCCA patients are warranted. This study aims to investigate the impact of increased BMI on patient’s outcome, especially on tumor recurrence, following liver resection in patients with pCCA as well as to evaluate prognostic and risk factors for accurate prediction of outcome in this selective group of patients.

Abstract

Background: The association of body mass index (BMI) and long-term prognosis and outcome of patients with perihilar cholangiocarcinoma (pCCA) has not been well defined. The aim of this study was to evaluate clinicopathologic and oncologic outcomes with pCCA undergoing resection, according to their BMI. Methods: Patients undergoing liver resection in curative intention for pCCA at a tertiary German hepatobiliary (HPB) center were identified from a prospective database. Patients were classified as normal weight (BMI 18.5–24.9 kg/m²), overweight (BMI 25.0–29.9 kg/m²) and obese (>30 kg/m²) according to their BMI. Impact of clinical and histo-pathological characteristics on recurrence-free survival (RFS) were assessed using Cox proportional hazard regression analysis among patients of all BMI groups. Results: Among a total of 95 patients undergoing liver resection in curative intention for pCCA in the analytic cohort, 48 patients (50.5%) had normal weight, 33 (34.7%) were overweight and 14 patients (14.7%) were obese. After a median follow-up of 4.3 ± 2.9 years, recurrence was observed in totally 53 patients (56%). The cumulative recurrence probability was higher in obese and overweight patients than normal weight patients (5-year recurrence rate: obese: 82% versus overweight: 81% versus normal weight: 58% at 5 years; p = 0.02). Totally, 1-, 3-, 5- and 10-year recurrence-free survival rates were 68.5%, 44.6%, 28.9% and 13%, respectively. On multivariable analysis, increased BMI (HR 1.08, 95% CI: 1.01–1.16; p = 0.021), poor/moderate tumor differentiation (HR 2.49, 95% CI: 1.2–5.2; p = 0.014), positive lymph node status (HR 2.01, 95% CI: 1.11–3.65; p = 0.021), positive resection margins (HR 1.89, 95% CI:1.02–3.4; p = 0.019) and positive perineural invasion (HR 2.92, 95% CI: 1.02–8.3; p = 0.045) were independent prognostic risk factors for inferior RFS. Conclusion: Our study shows that a high BMI is significantly associated with an increased risk of recurrence after liver resection in curative intention for pCCA. This factor should be considered in future studies to better predict patient’s individual prognosis and outcome based on their BMI.

Serum C-Reactive Protein-to-Body Mass Index Ratio Predicts Overall Survival in Patients With Resected Colorectal Cancer

BACKGROUND AND PURPOSE

Systemic inflammation and nutritional status have been shown to be associated with the prognosis of colorectal cancer. The purpose of this study was to evaluate the impact of the serum C-reactive protein-to-body mass index ratio on the prognosis of patients with curatively resected colorectal cancer.

METHODS

We conducted a retrospective analysis of a database of 2,471 eligible patients with colorectal cancer who underwent curative resection at our hospital between 2004 and 2019. The optimal cut-off for CPR-to-BMI ratio was determined using maximally selected rank statistics. Patients were divided into 2 groups according to the cut-off value of the serum C-reactive protein-to-body mass index ratio. Kaplan-Meier curves and Cox regression analysis were used to compare overall survival. A two-sided P-value < 0.05 was considered statistically significant.

RESULTS

The proportion of patients with a high C-reactive protein-to-body mass index ratio increased with increasing age, male sex, right-sided colon cancer, poorly differentiated tumors, advanced-stage disease, local/distant metastases, tumor-node-metastasis stage, and microsatellite instability. In subgroup analysis according to tumor-node-metastasis stage, the overall survival of the high C-reactive protein-to-body mass index ratio group was significantly shorter than that of the low C-reactive protein-to-body mass index ratio group (P < 0.001). Multivariate analysis identified age, differentiation, tumor-node-metastasis stage, carcinoembryonic antigen level, and the C-reactive protein-to-body mass index ratio as independent poor prognostic factors for overall survival.

CONCLUSIONS

The C-reactive protein-to-body mass index ratio predicts the prognosis of patients with curatively resected colorectal cancer and is an independent risk factor for overall survival in patients with colorectal cancer.

Survival nomogram for patients with bone metastatic renal cell carcinoma: A population-based study

Purpose:

Increased attention has been focused on the survival of renal cell carcinoma (RCC) patients with bone metastasis. This study proposed to establish and evaluate a nomogram for predicting the overall survival (OS) and cancer-specific survival (CSS) of RCC patients with bone metastasis.

Materials and Methods:

RCC patients with bone metastasis between 2010 and 2015 were captured from the surveillance, epidemiology and end results (SEER) database. Univariate and multivariate cox regressions were performed to assess the effects of clinical variables on OS and CSS. The nomogram based on the Cox hazards regression model was developed. Concordance index (C-index) and calibration curve were performed to evaluate the accuracy of nomogram models, receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were conducted to assess the predict performance.

Results:

A total of 2.471 eligible patients were enrolled in this study. The patients were assigned to primary (n=1.672) and validation (n=799) cohorts randomly. The 1-, 2-, and 3-year OS and CSS nomogram models were constructed based on age at diagnosis, sex, marital status, pathological grade, T-stage, N-stage, brain/liver/lung metastasis, surgery, radiotherapy and chemotherapy. The c for OS and CSS prediction was 0.730 (95% confidence interval [CI]: 0.719-0.741) and 0.714 (95%CI:0.702-0.726). The calibration curves showed significant agreement between nomogram models and actual observations. ROC and DCA indicated nomograms had better predict performance.

Conclusions:

The nomograms for predicting prognosis provided an accurate prediction of OS and CSS in RCC patients with bone metastasis, and contributed clinicians to optimize individualized treatment plans.

Sex Differences on Mitotane Concentration and Treatment Outcome in Patients with Adrenocortical Carcinoma

(1) Background: In clinical settings, data regarding sex are rarely investigated. In women, factors such as body size and composition, hormonal variations, metabolism, and access to care systems and therapy could strongly influence the pharmacological management and the outcome of the therapy. To underline this sex-related difference, we retrospectively collected data from adrenocortical carcinoma patients treated with mitotane, and then evaluated sex-related pharmacokinetics parameters. (2) Methods: A fully validated chromatographic method was used to quantify mitotane concentration in plasma collected from adult patients, also considering the active metabolite ortho,para,dichlorodiphenylethene (o,p'-DDE). Statistical analyses were used to evaluate the sex influence on drugs pharmacokinetics. (3) Results: We found that sex resulted as predictive factor of plasma mitotane and o,p'-DDE concentrations and significantly influenced the attainment of the therapeutic target of mitotane, implying that female sex could be a risk factor of treatment failure. (4) Conclusions: These results suggest that mitotane therapy should be modulated according to patient sex. Furthermore, the proposed approach could contribute to facilitating and disseminating sex-specific pharmacology.

A Combined Effect of Expression Levels of Obesity-Related Genes and Clinical Factors on Cancer Survival Rate

Obesity is directly associated with the risk of cancer in different organs, including breast, colon, and kidney. However, adipocytes could be utilized to control progression for some types of cancer, such as leukemia and breast cancer. To explore the potential correlation between adipocytes and cancer, the combined effect of expression levels of obesity-related genes and clinical factors (i.e., gender, race, menopausal status, history of smoking, tumor grade, body mass index (BMI), and history of drinking) on cancer survival rate was systemically studied. The expression levels of obesity-related genes in cancer tissues and normal tissues were downloaded from The Cancer Genome Atlas (TCGA). Kaplan–Meier curves were plotted using R programming language. The log-rank test was applied to explore the correlation between different clinical subgroups. The overexpression of the nine obesity-related genes (MC4R, TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2, FTO, PCSK1, and GPR120) may associate with tumor-promoting factors in some organs (head and neck, gastrointestinal tract, liver, and gallbladder). Underexpressed LEPR, NEGR1, TMEM18, and SH2B1 genes prevented the progression and metastasis of kidney cancer. The combined effect of clinical factors and the expression levels of obesity-related genes on patients' survival was found to be significant. Our outcomes suggested that the alternations of DNA methylation patterns could result in the changes of expression levels of obesity-related genes, playing a critical role in tumor progression. The results of the current study may be utilized to supplement precision and personalized medicine, as well as provide novel insights for the development of treatment approaches for cancer.

Sex-biased differences in the correlation between epithelial-to-mesenchymal transition-associated genes in cancer cell lines

There is a wide disparity in the incidence, malignancy and mortality of different types of cancer between each sex. The sex-specificity of cancer seems to be dependent on the type of cancer. Cancer incidence and mortality have been demonstrated as sex-specific in a number of different types of cancer, such as liver cancer, whereas sex-specificity is not noticeable in certain other types of cancer, including colon and lung cancer. The present study aimed to elucidate the molecular basis for sex-biased gene expression in cancer. The mRNA expression of the epithelial-to-mesenchymal transition-associated genes was investigated, including E-cadherin (also termed CDH1), vimentin (VIM), discoidin domain receptor 1 (DDR1) and zinc finger E-box binding homeobox 1 (ZEB1) in female- and male-derived cancer cell lines by reverse transcription (RT)-PCR and the Broad-Novartis Cancer Cell Line Encyclopedia (CCLE) database analysis. A negative correlation was observed between DDR1 and ZEB1 only in the female-derived cancer cell lines via RT-PCR analysis. A negative correlation between DDR1 index (defined by the logarithmic value of DDR1 divided by ZEB1, based on the mRNA data from the RT-PCR analysis) and an invasive phenotype was observed in cancer cell lines in a sex-specific manner. Analysis of the CCLE database demonstrated that DDR1 and ZEB1, which are already known to be sex-biased, were negatively correlated in female-derived liver cancer cell lines, but not in male-derived liver cancer cell lines. In contrast, cell lines of colon and lung cancer did not reveal any sex-dependent difference in the correlation between DDR1 and ZEB1. Kaplan-Meier survival curves using the transcriptomic datasets such as Gene Expression Omnibus, European Genome-phenome Archiva and The Cancer Genome Atlas databases suggested a sex-biased difference in the correlation between DDR1 expression pattern and overall survival in patients with liver cancer. The results of the present study indicate that sex factors may affect the regulation of gene expression, contributing to the sex-biased progression of the different types of cancer, particularly liver cancer. Overall, these findings suggest that analyses of the correlation between DDR1 and ZEB1 may prove useful when investigating sex-biased cancers.

Excess Body Weight and the Risk of Liver Cancer: Systematic Review and a Meta-Analysis of Cohort Studies

Objective: To update and expand the previous meta-analysis including all prospective studies on the issue of the associations between overweight, obesity, and liver cancer risk. We also performed a meta-regression to investigate a potential nonlinear and/or linear association between body mass index (BMI) and liver cancer risk.Methods: Literature search was conducted in four libraries from the beginning of indexing for each database to 1st September, 2018.Results: The summary risk estimate was statistically significant on the association between overweight and the risk of liver cancer incidence (relative ratio [RR] = 1.19). The RRs were significantly stronger in people with known liver disease with overweight than in the general population with overweight (RR = 1.50 vs. RR = 1.10; P_difference = .02). The meta-analysis showed an increase by 87% on the risk of liver cancer incidence in obesity categories, relative to categories of normal BMI (RR = 1.87, P < .01). Moreover, the results showed that, overweight was associated with 9% increased and obesity with 66% increased for risk of liver cancer mortality. In linear model, the relative risks of liver cancer were 1.32 for continuous BMI per 5 kg/m² increase.Conclusion: This meta-analysis supports the hypothesis that overweight, obesity may significantly increase liver cancer risk.

Estrogen Receptor Beta-Mediated Modulation of Lung Cancer Cell Proliferation by 27-Hydroxycholesterol

27-hydroxycholesterol (27HC) is an abundant cholesterol metabolite in human circulation and promotes breast cancer cell proliferation. Although lung is one of the organs that contain high levels of 27HC, the role of 27HC in lung is unknown. In this study, we found that 27HC promotes lung cancer cell proliferation in an estrogen receptor β (ERβ)-dependent manner. The expression of 27HC-generating enzyme CYP27A1 is higher in lung cancer cells than in normal lung cells. Treatment with 27HC increased cell proliferation in ERβ-positive lung cancer cells, but not in ERα-positive or ER-negative cells. The effect on cell proliferation is specific to 27HC and another oxysterol, 25-hydroxycholesterol that has a similar oxysterol structure with 27HC. Moreover, among ligands for nuclear receptors tested, only estrogen had the proliferative effect, and the effect by 27HC and estrogen was inhibited by ERβ-specific, but not ERα-specific, inhibitors. In addition, the effect by 27HC was not affected by membrane-bound estrogen receptor GPR30. Interestingly, despite the high expression of CYP27A1, endogenously produced 27HC was not the major contributor of the 27HC-induced cell proliferation. Using kinase inhibitors, we found that the effect by 27HC was mediated by the PI3K-Akt signaling pathway. These results suggest that 27HC promotes lung cancer cell proliferation via ERβ and PI3K-Akt signaling. Thus, lowering 27HC levels may lead to a novel approach for the treatment of lung cancer.