Development and external validation of prognostic nomograms in hepatocellular carcinoma patients: a population based study

Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma

BACKGROUND

Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine learning (ML)-based prognostic models to predict overall survival of patients with AFP-positive HCC.

METHODS

Data on patients with AFP-positive HCC were extracted from the Surveillance, Epidemiology, and End Results database. Six ML algorithms (extreme gradient boosting [XGBoost], logistic regression [LR], support vector machine [SVM], random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3]) were used to develop the prognostic models of patients with AFP-positive HCC at one year, three years, and five years. Area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the model.

RESULTS

A total of 2,038 patients with AFP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survival rates were 60.7%, 28.9%, and 14.3%, respectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The XGBoost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771; test set: AUC = 0.782), 3-year (train set: AUC = 0.763; test set: AUC = 0.749) and 5-year (train set: AUC = 0.807; test set: AUC = 0.740). Furthermore, for 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the XGBoost model, respectively. Calibration curves and DCA exhibited good predictive performance as well.

CONCLUSIONS

The XGBoost model exhibited good predictive performance, which may provide physicians with an effective tool for early medical intervention and improve the survival of patients.

CT-based radiomics for predicting pathological grade in hepatocellular carcinoma

Objective

To construct and validate radiomics models for hepatocellular carcinoma (HCC) grade predictions based on contrast-enhanced CT (CECT).

Methods

Patients with pathologically confirmed HCC after surgery and underwent CECT at our institution between January 2016 and December 2020 were enrolled and randomly divided into training and validation datasets. With tumor segmentation and feature extraction, radiomic models were constructed using univariate analysis, followed by least absolute shrinkage and selection operator (LASSO) regression. In addition, combined models with clinical factors and radiomics scores (Radscore) were constructed using logistic regression. Finally, all models were evaluated using the receiver operating characteristic (ROC) curve with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

In total 242 patients were enrolled in this study, of whom 170 and 72 formed the training and validation datasets, respectively. The arterial phase and portal venous phase (AP+VP) radiomics model were evaluated as the best for predicting HCC pathological grade among all the models built in our study (AUC = 0.981 in the training dataset; AUC = 0.842 in the validation dataset) and was used to build a nomogram. Furthermore, the calibration curve and DCA indicated that the AP+VP radiomics model had a satisfactory prediction efficiency.

Conclusions

Low- and high-grade HCC can be distinguished with good diagnostic performance using a CECT-based radiomics model.

Construction of webbased prediction nomogram models for cancerspecific survival in patients at stage IV of hepatocellular carcinoma depending on SEER database

OBJECTIVES

Hepatocellular carcinoma (HCC) prognosis involves multiple clinical factors. Although nomogram models targeting various clinical factors have been reported in early and locally advanced HCC, there are currently few studies on complete and effective prognostic nomogram models for stage IV HCC patients. This study aims to creat nomograms for cancer-specific survival (CSS) in patients at stage IV of HCC and developing a web predictive nomogram model to predict patient prognosis and guide individualized treatment.

METHODS

Clinicopathological information on stage IV of HCC between January, 2010 and December, 2015 was collected from the Surveillance, Epidemiology, and End Results (SEER) database. The patients at stage IV of HCC were categorized into IVA (without distant metastases) and IVB (with distant metastases) subgroups based on the presence of distant metastasis, and then the patients from both IVA and IVB subgroups were randomly divided into the training and validation cohorts in a 7꞉3 ratio. Univariate and multivariate Cox regression analyses were used to analyze the independent risk factors that significantly affected CSS in the training cohort, and constructed nomogram models separately for stage IVA and stage IVB patients based on relevant independent risk factors. Two nomogram's accuracy and discrimination were evaluated by receiver operator characteristic (ROC) curves and calibration curves. Furthermore, web-based nomogram models were developed specifically for stage IVA and stage IVB HCC patients by R software. A decision analysis curve (DCA) was used to evaluate the clinical utility of the web-based nomogram models.

RESULTS

A total of 3 060 patients were included in this study, of which 883 were in stage IVA, and 2 177 were in stage IVB. Based on multivariate analysis results, tumor size, alpha-fetoprotein (AFP), T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent prognostic factors for patients with stage IVA of HCC; and tumor size, AFP, T stage, N stage, histological grade, lung metastasis, surgery, radiotherapy, and chemotherapy were independent prognostic factors for patients with stage IVB HCC. In stage IVA patients, the 3-, 6-, 9-, 12-, 15-, and 18-month areas under the ROC curves for the training cohort were 0.823, 0.800, 0.772, 0.784, 0.784, and 0.786, respectively; and the 3-, 6-, 9-, 12-, 15-, and 18-month areas under the ROC curves for the validation cohort were 0.793, 0.764, 0.739, 0.773, 0.798, and 0.799, respectively. In stage IVB patients, the 3-, 6-, 9-, and 12-month areas under the ROC curves for the training cohort were 0.756, 0.750, 0.755, and 0.743, respectively; and the 3-, 6-, 9-, and 12-month areas under the ROC curves for the validation cohort were 0.744, 0.747, 0.775, and 0.779, respectively; showing that the nomograms had an excellent predictive ability. The calibration curves showed a good consistency between the predictions and actual observations.

CONCLUSIONS

Predictive nomogram models for CSS in stage IVA and IVB HCC patients are developed and validated based on the SEER database, which might be used for clinicians to predict the prognosis, implement individualized treatment, and follow up those patients.

Prognostic factors and survival prediction in hepatocellular carcinoma: development and validation of a novel nomogram based on the SEER database

Background

Current staging systems for hepatocellular carcinoma (HCC) still have limitations in clinical practice. Our study aimed to explore the prognostic factors and develop a new nomogram to predict the cancer-specific survival (CSS) for patients with HCC.

Methods

A total of 6,166 HCC patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly grouped into the training cohort (70%) and validation cohort (30%). Multivariate Cox analysis was used to identify prognostics factors for CSS of patients, then we incorporated these variables and presented a new nomogram to predict 2- and 5-year CSS. The performance of the nomogram was assessed with respect to its calibration, concordance index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), and decision curve analysis (DCA).

Results

Multivariate Cox analysis revealed that American Joint Committee on Cancer (AJCC) stage, race, grade, surgery, chemotherapy, radiation, tumor size, bone metastasis (BM), and alpha-fetoprotein (AFP) were independently associated with CSS. The prediction nomogram which contained these predictors showed good performance, with a C-index of 0.802 [95% confidence interval (CI), 0.792-0.812] in the training cohort and 0.801 (95% CI, 0.787-0.815) in the validation cohort. The calibration curves demonstrated good agreement between the actual observation and the nomogram prediction. Furthermore, the nomogram showed improved discriminative capacity (AUC, 0.873 and 0.875 for 2- and 5-year CSS in validation set) compared to the 7th tumor-node-metastasis (TNM) staging system (AUC, 0.735 and 0.717). The DCA also indicated good application of the nomogram.

Conclusions

This study presents a novel nomogram that incorporates the important prognostic factors of HCC, which can be conveniently used to accurately predict the 2- and 5-year CSS of patients with HCC, thus assisting individualized clinical decision making.

Impact of marital status on overall survival in patients with early-stage hepatocellular carcinoma

The purpose of the present research was to assess the prognostic impact of marital status in hepatocellular carcinoma (HCC) patients with tumors ≤ 2 cm (stage Ia) based on the data from the Surveillance, Epidemiology, and End Results (SEER) database. Patients who received a histopathologic HCC diagnosis between 2004 and 2016 were recruited. Overall survival (OS) was the major outcome measure. The Cox regression model and the Fine-Gray regression model were used for the purpose of comparing and examining the prognostic value of marital status for OS. The data for a total of 2446 stage Ia HCC patients were extracted from the database. The median overall survival time was 96.0 months, with 5-year and 10-year overall survival rates of 58.2% and 45.8%, respectively. In both the Fine-Gray regression model and Cox regression model, marital status [married vs. unmarried and others, both P < 0.001, hazard ratio (HR) = 1.389 for Cox and HR = 1.378 for Fine-Gray], age at diagnosis, tumor grade, and surgery at the primary site independently served as prognostic indicators associated with OS. In conclusion, positive marital status was independently associated with better OS for stage Ia HCC patients, and its prognostic influence should be validated in the near future.

Development and Validation of Nomogram for Predicting Survival of Primary Liver Cancers Using Machine Learning

Background and Aims

Primary liver cancer (PLC) is a common malignancy with poor survival and requires long-term follow-up. Hence, nomograms need to be established to predict overall survival (OS) and cancer-specific survival (CSS) from different databases for patients with PLC.

Methods

Data of PLC patients were downloaded from Surveillance, Epidemiology, and End Results (SEER) and the Cancer Genome Atlas (TCGA) databases. The Kaplan Meier method and log-rank test were used to compare differences in OS and CSS. Independent prognostic factors for patients with PLC were determined by univariate and multivariate Cox regression analyses. Two nomograms were developed based on the result of the multivariable analysis and evaluated by calibration curves and receiver operating characteristic curves.

Results

OS and CSS nomograms were based on age, race, TNM stage, primary diagnosis, and pathologic stage. The area under the curve (AUC) was 0.777, 0.769, and 0.772 for 1-, 3- and 5-year OS. The AUC was 0.739, 0.729 and 0.780 for 1-, 3- and 5-year CSS. The performance of the two new models was then evaluated using calibration curves.

Conclusions

We systematically reviewed the prognosis of PLC and developed two nomograms. Both nomograms facilitate clinical application and may benefit clinical decision-making.

Construction and Validation of a Risk Prediction Model for Postoperative Urinary Retention in Lung Cancer Patients

Indwelling catheter is a routine procedure in surgical patients. Studies have shown that prolonged indwelling urinary catheterization increases the risk of postoperative urinary tract infection. Although early removal of the urinary catheter after operation can reduce the risk of postoperative urinary symptoms and tract infections, it may lead to postoperative anesthetic dysuria. Therefore, this study investigates the urinary retention and related risk factors in patients after thoracoscopic lobectomy under general anesthesia. The clinical data of 214 patients who underwent thoracoscopic lobectomy in the Department of Thoracic Surgery of a tertiary class A cancer hospital in Beijing from July 2020 to April 2021 were collected. A risk prediction model was established by logistic regression analysis, and the prediction effect was determined using the area under the receiver operating characteristic (ROC) curve. The incidence of indwelling catheter after thoracoscopic lobectomy was 44.8% (96/214). Sex (OR = 21.102, 95% CI: 2.906-153.239, P=0.003), perception of shame (OR = 74.256, 95% CI: 6.171-893.475, P=0.001), age (OR = 1.095, 95% CI: 1.014-1.182, P=0.021), and bed rest time (OR = 1.598, 95% CI: 1.263-2.023, P < 0.021) were the factors influencing urinary retention after thoracoscopic lobectomy. This model can effectively predict the occurrence of postoperative urinary retention in patients with lung cancer and help medical staff to intervene effectively before the onset of urinary retention, which provides reference for preventive treatment and nursing intervention.

Development and Validation of a Nomogram to Predict Cancer-Specific Survival for Middle-Aged Patients With Early-Stage Hepatocellular Carcinoma

Background

Hepatocellular carcinoma is a common cause of death in middle-aged patients. We aimed to construct a new nomogram to predict cancer-specific survival (CSS) in middle-aged patients with hepatocellular carcinoma at an early stage.

Method

We collected clinicopathological information on early middle-aged patients with hepatocellular carcinoma from the SEER database. Univariate and multivariate Cox regression models were used to screen the independent risk factors for prognosis. These risk factors were used to construct predictions of CSS in patients with hepatocellular carcinoma. Consistency index (C- index), calibration curve, area under the receiver operating curve (AUC) were used. A decision analysis curve (DCA) was used to evaluate the clinical utility of the predictive model.

Results

A total of 6,286 patients with hepatocellular carcinoma in early middle age were enrolled. Univariate and multivariate Cox regression analysis showed that sex, marriage, race, histological tumor grade, T stage, surgery, chemotherapy, AFP, and tumor size were independent risk factors for prognosis. All independent risk factors were included in the nomogram to predict CSS at 1-, 3-, and 5-years in early middle age patients with hepatocellular carcinoma. In the training cohort and validation cohort, the C-index of the prediction model was 0.728 (95%CI: 0.716–0.740) and 0.733 (95%CI: 0.715–0.751), respectively. The calibration curve showed that the predicted value of the prediction model is highly consistent with the observed value. AUC also suggested that the model has good discrimination. DCA suggested that the nomogram had better predictive power than T staging.

Conclusion

We constructed a new nomogram to predict CSS in middle-aged patients with early-stage hepatocellular carcinoma. This prediction model has good accuracy and reliability, which can help patients and doctors to judge prognosis and make clinical decisions.

A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Early Hepatocellular Carcinoma: A Study Based on SEER Database

Background: Primary liver cancer is a common malignant tumor primarily represented by hepatocellular carcinoma (HCC). The number of elderly patients with early HCC is increasing, and older age is related to a worse prognosis. However, an accurate predictive model for the prognosis of these patients is still lacking.

Methods: Data of eligible elderly patients with early HCC in Surveillance, Epidemiology, and End Results database from 2010 to 2016 were downloaded. Patients from 2010 to 2015 were randomly assigned to the training cohort (n = 1093) and validation cohort (n = 461). Patients' data in 2016 (n = 431) was used for external validation. Independent prognostic factors were obtained using univariate and multivariate analyses. Based on these factors, a cancer-specific survival (CSS) nomogram was constructed. The predictive performance and clinical practicability of our nomogram were validated. According to the risk scores of our nomogram, patients were divided into low-, intermediate-, and high-risk groups. A survival analysis was performed using Kaplan–Meier curves and log-rank tests.

Results: Age, race, T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent predictors for CSS and thus were included in our nomogram. In the training cohort and validation cohort, the concordance indices (C-indices) of our nomogram were 0.739 (95% CI: 0.714–0.764) and 0.756 (95% CI: 0.719–0.793), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves (AUCs) showed similar results. Calibration curves revealed high consistency between observations and predictions. In external validation cohort, C-index (0.802, 95%CI: 0.778–0.826) and calibration curves also revealed high consistency between observations and predictions. Compared with the TNM stage, nomogram-related decision curve analysis (DCA) curves indicated better clinical practicability. Kaplan–Meier curves revealed that CSS significantly differed among the three different risk groups. In addition, an online prediction tool for CSS was developed.

Conclusions: A web-based prediction model for CSS of elderly patients with early HCC was constructed and validated, and it may be helpful for the prognostic evaluation, therapeutic strategy selection, and follow-up management of these patients.

A prediction model for postoperative urinary retention after thoracic surgery

Background

Urinary retention remains a frequent postoperative complication, associated with patient discomfort and delayed discharge following general thoracic surgery (GTS). We aimed to develop and prospectively validate a predictive model of postoperative urinary retention (POUR) among GTS patients.

Methods

We retrospectively developed a predictive model using data from the Society of Thoracic Surgeons GTS Database at our institution. The patient study cohort included adults undergoing elective in-patient surgical procedures without a history of renal failure or Foley catheter on entry to the recovery suite (August 2013 to March 2017). Multivariable logistic regression models identified factors associated with urinary retention, and a nomogram to aid medical decision making was developed. The predictive model was validated in a cohort of GTS patients between April 2017 and November 2018 using receiver operating characteristic (ROC) analysis.

Results

The predictive model was developed from 1484 GTS patients, 284 of whom (19%) experienced postoperative urinary retention within 24 hours of the operation. Risk factors for POUR included older age, male sex, higher preoperative creatinine, chronic obstructive pulmonary disease, primary diagnosis, primary procedure, and use of postoperative patient-controlled analgesia. A logistic nomogram for estimating the risk of POUR was created and validated in 646 patients, 65 of whom (10%) had urinary retention. The ROC curves of development and validation models had similar favorable c-statistics (0.77 vs 0.72; P > .05).

Conclusions

Postoperative urinary retention occurs in nearly 20% of patients undergoing major GTS. Using a validated predictive model may help by targeting certain patients with prophylactic measures to prevent this complication.

A Prognostic Scoring System for Predicting Overall Survival of Patients with the TNM 8th Edition Stage I and II Hepatocellular Carcinoma After Surgery: A Population-Based Study

PURPOSE

Postoperative prognosis prediction models for patients with stage Ⅰ and Ⅱ hepatocellular carcinoma (HCC) according to the 8th edition of the Tumor-Node-Metastasis staging system after surgery are rare. This study aimed to build a prognostic score to predict survival outcomes and stratify these patients into different prognostic strata.

PATIENTS AND METHODS

We developed a web-based nomogram that incorporated four selected risk factors based on the multivariate Cox regression, using a training set (n=3567) from the Surveillance, Epidemiology, and End Results (SEER) database. It was validated with an independent internal set from the SEER database (n=1783) and an external validation set of 516 Chinese patients. The predictive performance and discrimination ability of our model were further evaluated and compared with those of the conventional HCC staging systems.

RESULTS

Our nomogram consistently outperformed the conventional staging systems in the training, internal validation set, and external validation set. We quantified the nomogram model into a numerical SNIG (an abbreviation of the incorporated variables - size, number, MVI, and grade) score by summing the points assigned to each incorporated variable, leading to the optimal cut-off values of 6 and 10, which could stratify patients into 3 categories (SNIG score <6, 6-10, ≥10). This yielded significantly different median overall survivals (interquartile ranges) of 42.0 (20.0-72.0) and 37.0 (17.0-67.0); 28.0 (12.0-60.0) and 42.0 (21.75-82.0); 40.0 (18.0-70.0) and 29.0 (11.5-61.0) months for the 3 categories in the entire SEER and external validation sets, respectively.

CONCLUSION

We developed a web-based SNIG model to graphically and numerically predict the overall survival of stage Ⅰ and Ⅱ HCC. This scoring system may shed light on risk stratification for these patients in clinical practice and clinical trials.

Estradiol is significantly associated with prognosis in non-surgical liver cancer patients: from bench to bedside

There are rarely systematic studies to analyze the prognostic factors among non-surgical liver cancer patients. Whether there is a gender difference in the survival of non-surgical liver cancer patients and what may cause this difference is still unclear. A total of 12,312 non-surgical liver cancer patients were enrolled in this study. Age, race, sex, grade, tumor TNM stage, marital status, tumor size, and histological type were independent risk factors in liver cancer and were confirmed in the validation cohort. Before menopause, females demonstrated a better mean survival probability than males (39.4±1.4 vs. 32.7±0.8 months, respectively; p<0.001), and continued in post-menopause. The results of differentially expressed genes (DEGs) and KEGG pathway analysis showed that there were significant differences in steroid hormone biosynthesis between male and female liver cancer patients. In vitro experiments revealed that estradiol inhibited the proliferation of hepatocellular cancer cell lines and increased apoptosis, but estrone exerted no effect. In conclusion, gender differences in prognosis among non-surgical liver cancer patients were confirmed and attributable primarily to estradiol.

Prognostic nomogram to predict the overall survival of patients with early-onset colorectal cancer: a population-based analysis

PURPOSE

The present study aimed to identify independent clinicopathological and socio-economic prognostic factors associated with overall survival of early-onset colorectal cancer (EO-CRC) patients and then establish and validate a prognostic nomogram for patients with EO-CRC.

METHODS

Eligible patients with EO-CRC diagnosed from 2010 to 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into a training cohort and a testing cohort. Independent prognostic factors were obtained using univariate and multivariate Cox analyses and were used to establish a nomogram for predicting 3- and 5-year overall survival (OS). The discriminative ability and calibration of the nomogram were assessed using C-index values, AUC values, and calibration plots.

RESULTS

In total, 5585 patients with EO-CRC were involved in the study. Based on the univariate and multivariate analyses, 15 independent prognostic factors were assembled into the nomogram to predict 3- and 5-year OS. The nomogram showed favorable discriminatory ability as indicated by the C-index (0.840, 95% CI 0.827-0.850), and the 3- and 5-year AUC values (0.868 and 0.84869 respectively). Calibration plots indicated optimal agreement between the nomogram-predicted survival and the actual observed survival. The results remained reproducible in the testing cohort. The C-index of the nomogram was higher than that of the TNM staging system (0.840 vs 0.804, P < 0.001).

CONCLUSION

A novel prognostic nomogram for EO-CRC patients based on independent clinicopathological and socio-economic factors was developed, which was superior to the TNM staging system. The nomogram could facilitate postoperative individual prognosis prediction and clinical decision-making.

A practical nomogram and risk stratification system predicting the cancer-specific survival for patients with early hepatocellular carcinoma

BACKGROUND

Our purpose was to establish and validate a nomogram model in early hepatocellular carcinoma (HCC) patients for predicting the cancer-specific survival (CSS).

METHODS

We extracted eligible data of relevant patients between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Further, we divided all patients into two groups (training and validation cohorts) at random (7:3). Nomogram was established using effective risk factors based on univariate and multivariate analysis. The effective performance of nomogram was evaluated using concordance index (C-index), calibration plots, decision curve analysis (DCA), and receiver operating characteristic curve (ROC).

RESULTS

We selected 3620 patients with early HCC including the training cohort (70%, 2536) and the validation cohort (30%, 1084). The nomogram-related C-indexes were 0.755 (95% CI: 0.739-0.771) and 0.737 (95% CI: 0.712-0.762), in the training and validation cohorts, respectively. The calibration plots showed good consistency of 3-and 5-year CSS between the actual observation and the nomogram prediction. The 3-, 5-year DCA curves also indicated that the nomogram has excellent clinical utility. The 3-, 5-year area under curve (AUC) of ROC in the training cohort were 0.783, 0.779, respectively, and 0.767, 0.766 in the validation cohort, respectively. With the establishment of nomogram, a risk stratification system was also established that could divide all patients into three risk groups, and the CSS in different groups (i.e., low risk, intermediate risk, and high risk) had a good regional division.

CONCLUSIONS

We developed a practical nomogram in early HCC patients for predicting the CSS, and a risk stratification system follow arisen, which provided an applicable tool for clinical management.

High-throughput sequencing-based analysis of gene expression of hepatitis B virus infection-associated human hepatocellular carcinoma

Hepatitis B virus (HBV) infection is a critical factor for the initiation and progression of hepatocellular carcinoma (HCC). Gene expression profiles for HBV-associated HCC may provide valuable insight for the diagnosis and treatment of this type of HCC. The present study aimed to screen the differential genes in human HCC tissues based on high-throughput sequencing and to predict the potential therapeutic targets. Total mRNA was extracted from human HCC tissues and paracancerous tissues and sequenced using the Hiseq4000 sequencing platform. Differential gene expressions were screened and further analyzed using quantitative PCR and immunohistochemistry. A total of 2,386 differentially expressed genes were screened. Of these, 1119 were upregulated and 1,267 were downregulated in paracancerous tissues compared with tumor tissues. Gene Ontology term analysis demonstrated that differentially expressed genes were involved in carboxylic acid catabolism, monocarboxylic acid metabolic processes and α-amino acid metabolic processes. Molecular functional analysis revealed that the differentially expressed genes functioned in oxidoreductase activity, for example acting on CH-OH group of donors and permitting identical protein binding, anion binding, coenzyme binding and monocarxylic acid transporter activity. The Kyoto Encyclopedia of Genes and Genomes analysis reported that the differentially expressed genes were primarily concentrated in 20 signaling pathways, such as valine, leucine and leucine degradation, retinol metabolism and the cell cycle. Differential expression of proteins regulating the cell cycle, including stratifin, cyclin B1 and cyclin-dependent kinase 1, were significantly higher in tumor tissue compared with those in paracancerous tissue at both the mRNA and protein levels. These results were consistent with those obtained from high-throughput sequencing, indicating the reliability of the high-throughput sequencing. Together, these results identified differentially expressed genes and predicted the subsequent signaling pathways, which may be involved in the occurrence and development of HCC. Therefore, the present study may provide novel implications in the therapeutic and diagnosis of HCC.

Diagnostic and prognostic values of upregulated SPC25 in patients with hepatocellular carcinoma

Background

Spindle pole body component 25 (SPC25) plays a vital role in many cellular processes, such as tumorigenesis. However, the clinical significance of SPC25 in hepatocellular carcinoma (HCC) has not been investigated. This study aimed to explore the expression patterns of SPC25 in HCC and non-neoplastic tissues and to investigate the diagnostic and prognostic values of SPC25.

Method

The expression of SPC25 was examined in 374 HCC issues and 50 non-neoplastic tissues from The Cancer Genome Atlas (TCGA) cohort. The diagnostic and prognostic values of SPC25 were analyzed via receiver operating characteristic (ROC) curve and survival analyses, respectively. Univariate and multivariate Cox regression analyses were used to identify the prognostic factors and to establish a nomogram. The diagnostic and prognostic values were further validated in an external cohort from the International Cancer Genome Consortium (ICGC) database.

Results

The expression of SPC25 in HCC tissues was significantly higher than that in normal tissues in both cohorts (all P < 0.001). The ROC curve analysis indicated that SPC25 expression has high diagnostic value in HCC with area under the curve (AUC) value of 0.969 (95% confidence interval [CI] [0.948-0.984]) and 0.945 (95% CI [0.920-0.965]) for TCGA and ICGC cohorts, respectively. Patients with HCC exhibiting high SPC25 expression were associated with worse prognosis than those exhibiting low SPC25 expression in both cohorts (all P < 0.001). SPC25 was independently associated with overall survival in both cohorts (all P < 0.001). The concordance indices of the nomogram for predicting overall survival in TCGA and ICGC cohorts were 0.647 and 0.805, respectively, which were higher than those of the American Joint Committee on Cancer (AJCC) staging system.

Conclusion

SPC25 was upregulated in HCC and independently predicted poor overall survival of patients with HCC. Therefore, SPC25 is an effective diagnostic and prognostic biomarker for HCC. An SPC25-based nomogram was more accurate and useful than the AJCC staging system to predict prognosis of HCC.

A competing risk nomogram predicting cause-specific mortality in patients with lung adenosquamous carcinoma

Background

Adenosquamous carcinoma (ASC) is an uncommon histological subtype of lung cancer. The purpose of this study was to assess the cumulative incidences of lung cancer-specific mortality (LC-SM) and other cause-specific mortality (OCSM) in lung ASC patients, and construct a corresponding competing risk nomogram for LC-SM.

Methods

Data on 2705 patients with first primary lung ASC histologically diagnosed between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The cumulative incidence function (CIF) was utilized to calculate the 3-year and 5-year probabilities of LC-SM and OCSM, and a competing risk model was built. Based on the model, we developed a competing risk nomogram to predict the 3-year and 5-year cumulative probabilities of LC-SM and the corresponding concordance indexes (C-indexes) and calibration curves were derived to assess the model performance. To evaluate the clinical usefulness of the nomogram, decision curve analysis (DCA) was conducted. Furthermore, patients were categorized into three groups according to the tertile values of the nomogram-based scores, and their survival differences were assessed using CIF curves.

Results

The 3-year and 5-year cumulative mortalities were 49.6 and 55.8% for LC-SM and 8.2 and 11.8% for OCSM, respectively. In multivariate analysis, increasing age, male sex, no surgery, and advanced T, N and M stages were related to a significantly higher likelihood of LC-SM. The nomogram showed good calibration, and the 3-year and 5-year C-indexes for predicting the probabilities of LC-SM in the validation cohort were both 0.79, which were almost equal to those of the ten-fold cross validation. DCA demonstrated that using the nomogram gained more benefit when the threshold probabilities were set within the ranges of 0.24–0.89 and 0.25–0.91 for 3-year and 5-year LCSM, respectively. In both the training and validation cohorts, the high-risk group had the highest probabilities of LC-SM, followed by the medium-risk and low-risk groups (both P < 0.0001).

Conclusions

The competing risk nomogram displayed excellent discrimination and calibration for predicting LC-SM. With the aid of this individualized predictive tool, clinicians can more expediently devise appropriate treatment protocols and follow-up schedules.

Disparities in utilization of services for racial and ethnic minorities with hepatocellular carcinoma associated with hepatitis C

BACKGROUND

Hepatitis C affects racial minorities disproportionately and is greatest among the black population. The incidence of hepatocellular carcinoma has increased with the largest increase observed in black and Hispanic populations, but limited data remain on whether hepatitis C hepatocellular carcinoma in racial-ethnic minorities have the same utilization of services compared with the white population.

METHODS

We used the database of the National Inpatient Sample to identify hepatitis C-hepatocellular carcinoma patients (N = 200,163) who underwent liver transplantation (n = 11,491), liver resection (n = 4,896), or ablation of liver lesions (n = 6,933) from 2005 to 2015. We estimated utilization over time and assessed differences in utilization and inpatient mortality across patient characteristics.

RESULTS

In multivariate analysis, factors associated with utilization of services included treatment year, sex, race, insurance status, hospital type, and comorbidity burden, with black and Hispanic patients having statistically significantly decreased utilization. Factors associated with inpatient mortality included treatment year, sex, race, insurance status, hospital type, hospital region, and comorbidity burden, with black patients having a statistically significantly greater risk of inpatient mortality.

CONCLUSION

We identified racial and socioeconomic factors which were associated with utilization of services and inpatient mortality for patients with hepatitis C hepatocellular carcinoma. Blacks were especially disadvantaged in the receipt of care. Further work to abrogate these findings is imperative to ensure equitable provision of surgical therapies.

Astragaloside IV enhances cisplatin chemosensitivity in hepatocellular carcinoma by suppressing MRP2

Decreased chemosensitivity among tumor cells is often an obstacle in cisplatin (Cis) chemotherapy. Overexpression of multidrug resistance-associated protein 2 (MRP2) is a key mechanism underlying decreased Cis chemosensitivity and resistance. Astragaloside IV (AS IV) is an important component derived from the well-known traditional Chinese herb Astragalus membranaceus. The aim of this study was to explore the role of AS IV in enhancing the antitumor effect of Cis by suppressing MRP2 expression in HepG2 cells and H22 tumor-bearing mice. After co-treatment of HepG2 cells with Cis and AS IV, we assessed the effects on cell proliferation and apoptosis. Tumor growth and apoptosis assessment were performed to assess chemosensitivity in H22 tumor-bearing mice. We used western blotting, immunofluorescence assays, and immunohistochemistry assays to detect MRP2 expression in HepG2 cells, H22 tumor tissues and mouse kidney tissues. AS IV enhanced Cis chemosensitivity by increasing tumor cell apoptosis and slowing tumor growth in vitro and in vivo. MRP2 overexpression in tumor cells was induced by Cis, which contributes to decreased chemosensitivity and Cis resistance. Co-administration of AS IV suppressed MRP2 expression in tumor tissues, which might be an important mechanism for enhancing Cis chemosensitivity in hepatocellular carcinoma. Moreover, AS IV alleviated Cis-induced kidney injury in mice without changing MRP2 expression. In total, AS IV enhanced the antitumor effect of Cis against hepatocellular carcinoma by suppressing MRP2 expression in tumor cells. The results provide a new insight into the combined use of a chemotherapy drug and natural ingredients to treat cancer.