Predictive value of a nomogram for hepatocellular carcinoma with brain metastasis at initial diagnosis: A population-based study

Comparison of metastasis and prognosis between early-onset and late-onset hepatocellular carcinoma: A population-based study

Background

While hepatocellular carcinoma (HCC) represents a highly heterogeneous disease with variable oncogenesis mechanisms and biological features, little is understood about differences in distant metastasis (DM) and prognosis between early-onset and late-onset HCC. This study defined early-onset disease as cancer diagnosed at age younger than 50 years and aimed to present a comprehensive analysis to characterize these disparities based on age.

Methods

Information of HCC patients was retrospectively collected from the SEER database and our hospital. Patient demographics, tumor characteristics, and survival were compared between the two groups. A 1:1 propensity score matching (PSM) was adopted to adjust confounding factors. Logistic and cox analysis were utilized to explore risk factors of DM and prognosis, respectively. Besides, the survival differences were assessed by the Kaplan-Meier curve and log-rank test.

Results

In total, 19187 HCC patients obtained from the SEER database and 129 HCC patients obtained from our own center were enrolled. Among 19187 patients with HCC, 3376 were identified in the matched cohort, including 1688 early-onset patients and 1688 late-onset patients. Compared with late-onset HCC, early-onset HCC was more likely to occur in female (25.2% vs. 22.9%, P = 0.030), have large tumors (>10.0 cm, 24.1% vs. 14.6%, P = 0.000), harbor poorly differentiated/undifferentiated cancers (17.0% vs. 14.0%, P = 0.003), present advanced clinical stage (T3+T4, 33.7% vs. 28.5%; N1, 9.2% vs. 6.7%; P = 0.000), and develop DM (13.0% vs. 9.5%, P = 0.000). After adjustment for confounders by PSM, we discovered that early-onset HCC remained an independent risk factor for DM. However, combined with Kaplan-Meier curve and cox analysis, early-onset HCC was an independent favorable predictor of survival. We validated these data on an independent cohort from our hospital.

Conclusion

In this population-based study, despite developing DM more frequently, early-onset HCC exhibited a superior prognosis than late-onset HCC. Nevertheless, further research is warranted to understand the underlying aetiologic basis for the disparities.

Repeated surgery for hemorrhagic brain metastases from hepatocellular carcinoma: palliation or effective part of a multimodal treatment? A case-based approach

Brain metastases from hepatocellular carcinoma (HCCBM) are encountered very rarely in clinical practice, especially in western countries. Only a minority of patients undergoes resective surgery, as clinical picture is usually complex and presentation is often catastrophic with intra-cerebral hemorrhage (ICH). Neurosurgical intervention can be not only life-saving but may also alleviate significantly the burden of symptoms. We present the case of a patient with six metachronous hemorrhagic HCCBM in which emergent surgery extended survival by 9 months, of which seven spent in near-normal life quality, stressing the role of neurosurgery in the evaluation of HCCBM patients.

Prognostic factors and predictive nomogram models for early death in elderly patients with hepatocellular carcinoma: a population-based study

Background: Owing to an aging society, there has been an observed increase in the average age of patients diagnosed with hepatocellular carcinoma (HCC). Consequently, this study is centered on identifying the prognostic factors linked with early death among this elderly demographic diagnosed with HCC. Additionally, our focus extends to developing nomograms capable of predicting such outcomes. Methods: The Surveillance, Epidemiology and End Results (SEER) database underpinned this study, showcasing participants aged 75 and above diagnosed with HCC within the timeframe from 2010 to 2015. These participants were divided randomly, at a 7:3 ratio, into training and validation cohorts. Univariable and multivariable logistic regressions were applied to the training cohort in the identification of prognostic indicators of early death, forming the basis for nomogram development. To measure the efficacy of these nomograms within both cohorts, we resorted to Receiver Operating Characteristic (ROC) curves, along with GiViTI calibration belt and Decision Curve Analysis (DCA). Results: The study involved 1,163 elderly individuals diagnosed with HCC, having reported instances of 397 all-cause early deaths and 356 HCC-specific early deaths. The sample group was divided into two cohorts: a training group consisting of 815 individuals, and a validation cohort, comprised of 348 individuals. Multifactorial analysis identified grade, T-stage, surgery, radiation, chemotherapy, bone and lung metastasis as significant predictors of mortality from all causes. Meanwhile, race, grade, T-stage, surgery, radiation, chemotherapy, and bone metastasis were revealed to be estimative factors for cancer-specific mortality. Subsequently, these factors were used to develop nomograms for prediction. GiViTI calibration belt corroborated the acceptable coherence of the nomograms, DCA confirmed their valuable clinical applicability, and ROC curves evidenced satisfactory discriminative capacity within both training and validation cohorts. Conclusion: The nomograms utilized in this study proved instrumental in detecting early death among elderly individuals afflicted with HCC. This tool could potentially assist physicians in formulating individualized treatment strategies.

Short-term prognosis for hepatocellular carcinoma patients with lung metastasis: A retrospective cohort study based on the SEER database

Our study aimed to develop a prediction model to predict the short-term mortality of hepatocellular carcinoma (HCC) patients with lung metastasis. The retrospective data of HCC patients with lung metastasis was from the Surveillance, Epidemiology, and End Results registration database between 2010 and 2015. 1905 patients were randomly divided into training set (n = 1333) and validation set (n = 572). There were 1092 patients extracted from the Surveillance, Epidemiology, and End Results database 2015 to 2019 as the validation set. The variable importance was calculated to screen predictors. The constructed prediction models of logistic regression, random forest, broad learning system, deep neural network, support vector machine, and naïve Bayes were compared through the predictive performance. The mortality of HCC patients with lung metastasis was 51.65% within 1 month. The screened prognostic factors (age, N stage, T stage, tumor size, surgery, grade, radiation, and chemotherapy) and gender were used to construct prediction models. The area under curve (0.853 vs. 0.771) of random forest model was more optimized than that of logistic regression model in the training set. But, there were no significant differences in testing and validation sets between random forest and logistic regression models. The value of area under curve in the logistic regression model was significantly higher than that of the broad learning system model (0.763 vs. 0.745), support vector machine model (0.763 vs. 0.689) in the validation set, and higher than that of the naïve Bayes model (0.775 vs. 0.744) in the testing model. We further chose the logistic regression prediction model and built the prognostic nomogram. We have developed a prediction model for predicting short-term mortality with 9 easily acquired predictors of HCC patients with lung metastasis, which performed well in the internal and external validation. It could assist clinicians to adjust treatment strategies in time to improve the prognosis.

Association of alpha-fetoprotein and metastasis for small hepatocellular carcinoma: A propensity-matched analysis

Metastasis is crucial for the prognosis of hepatocellular carcinoma (HCC). Distinguishing the potential risk factors for distant metastasis in small HCC (diameter ≤ 5 cm) is of great significance for improving the prognosis. HCC patients in the Surveillance, Epidemiology and End Results (SEER) registry with tumors ≤ 5 cm in diameter between January 2010 and December 2015 were retrieved. Demographic and clinicopathological metrics were extracted, including age, sex, race, marital status, tumor size, histological grade, T stage, N stage, M stage, alpha-fetoprotein (AFP), and liver fibrosis score. Univariate and multivariate logistic regression analyses were used to identify independent risk factors correlated with extrahepatic metastasis in small HCC. Propensity score matching (PSM) analysis was performed to balance the confounding factors in baseline characteristics. A total of 4176 eligible patients were divided into a non-metastasis group (n = 4033) and a metastasis group (n = 143) based on metastasis status. In multivariate analysis, larger tumor size, poor histological differentiation, regional lymph node metastasis, and elevated serum AFP levels were identified as independent risk factors for distant metastasis (P < 0.05), while age, sex, race, marital status, and liver fibrosis score were not associated with extrahepatic metastasis. After propensity score analysis, the AFP level was no longer associated with metastatic risk. The present study provided no evidence for a correlation between the clinical threshold of AFP and metastasis in small hepatocellular carcinoma.

Diagnostic and prognostic nomograms for newly diagnosed intrahepatic cholangiocarcinoma with brain metastasis: A population-based analysis

Brain metastasis (BM) is one of the rare metastatic sites of intrahepatic cholangiocarcinoma (ICC). ICC with BM can seriously affect the quality of life of patients and lead to a poor prognosis. The aim of this study was to establish two nomograms to estimate the risk of BM in ICC patients and the prognosis of ICC patients with BM. Data on 19,166 individuals diagnosed with ICC were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database. Independent risk factors and prognostic factors were identified by the logistic and the Cox regression, respectively. Next, two nomograms were developed, and their discrimination was estimated by concordance index (C-index) and calibration plots, while the clinical benefits of the prognostic nomogram were evaluated using the receiver operating characteristic (ROC) curves, the decision curve analysis (DCA), and the Kaplan-Meier analyses. The independent risk factors for BM were T stage, N stage, surgery, alpha-fetoprotein (AFP) level, and tumor size. T stage, surgery, radiotherapy, and bone metastasis were prognostic factors for overall survival (OS). For the prognostic nomogram, the C-index was 0.759 (95% confidence interval (CI) = 0.745-0.773) and 0.764 (95% CI = 0.747-0.781) in the training and the validation cohort, respectively. The calibration curves revealed a robust agreement between predictions and actual observations probability. The area under curves (AUCs) for the 3-, 6-, and 9-month OS were 0.721, 0.727, and 0.790 in the training cohort and 0.702, 0.777, and 0.853 in the validation cohort, respectively. The DCA curves yielded remarkable positive net benefits over a wide range of threshold probabilities. The Kaplan-Meier analysis illustrated that the nomogram could significantly distinguish the population with different survival risks. We successfully established the two nomograms for predicting the incidence of BM and the prognosis of ICC patients with BM, which may assist clinicians in choosing more effective treatment strategies.

Risk factors and predictive nomograms for early death of patients with advanced hepatocellular carcinoma: a large retrospective study based on the SEER database

BACKGROUND

Hepatocellular carcinoma (HCC) is a kind of tumor with high invasiveness, and patients with advanced HCC have a higher risk of early death. The aim of the present study was to identify the risk factors of early death in patients with advanced HCC and establish predictive nomograms.

METHODS

Death that occurred within 3 months of initial diagnosis is defined as early death. Patients diagnosed with stage IV HCC between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database for model establishment and verification. Univariable and multivariable logistic regression analyses were used to identify the risk factors. Predictive nomograms were constructed and an internal validation was performed. Decision curve analysis (DCA) was used to verify the true clinical application value of the models.

RESULTS

Of 6603 patients (57% age > 60, 81% male, 70% white, 46% married), 21% and 79% had stage IVA and IVB, respectively. On the multivariable analyses, risk factors for early deaths in patients with stage IVA were age, tumor size, histological grade, alpha-fetoprotein (AFP), fibrosis score, tumor stage (T stage), surgery, radiotherapy, and chemotherapy, and that in stage IVB were age, histological grade, AFP, T stage, node stage (N stage), bone metastasis, lung metastasis, surgery, radiotherapy, and chemotherapy. The areas under the curves (AUCs) were 0.830 (95% CI 0.809-0.851) and 0.789 (95% CI 0.768-0.810) in stage IVA and IVB, respectively. Nomograms comprising risk factors with the concordance indexes (C-indexes) were 0.820 (95% CI 0.799-0.841) in stage IVA and 0.785 (95% CI 0.764-0.0.806) in stage IVB for internal validation (Bootstrapping, 1000re-samplings). The calibration plots of the nomograms show that the predicted early death was consistent with the actual value. The results of the DCA analysis show that the nomograms had a good clinical application.

CONCLUSION

The nomograms can be beneficial for clinicians in identifying the risk factors for early death of patients with advanced HCC and predicting the probability of early death, so as to allow for individualized treatment plans to be accurately selected.

Bioinformatic analysis and in vitro validation of a five-microRNA signature as a prognostic biomarker of hepatocellular carcinoma

Background

Existing research has identified correlations between numerous microRNAs (miRNAs) and the prognosis of hepatocellular carcinoma (HCC). However, the role of a combination of miRNAs in predicting HCC survival requires further elucidation.

Methods

miRNA expression profiles and clinical data from HCC patients were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed (DE) miRNAs in tumor versus normal samples were identified. All HCC patients were randomly assigned to a training cohort or a validation cohort at a ratio of 1 to 1. A least absolute shrinkage and selection operator (LASSO) Cox regression model was subsequently employed to establish the miRNA signature. The constructed miRNA signature was then developed and validated.

Results

In total, 127 DE miRNAs were detected between HCC and paracancerous tissue using HCC RNA sequencing (RNA-Seq) data extracted from TCGA database. LASSO Cox regression generated a five-miRNA signature consisting of has-mir-105-2, has-mir-9-3, has-mir-137, has-mir-548f-1, and has-mir-561 in the training cohort. This risk model was significantly related to survival (P=5.682e-6). Log-rank tests and multivariate Cox regression analyses revealed the five-miRNA signature as an independent prognostic indicator [HR =3.285, 95% confidence interval (CI): 1.737–6.213], with the area under curve (AUC) of the miRNA signature being 0.728. The effects of the miRNA signature were further confirmed in the validation cohort and in the OncomiR Cancer Database and Gene Expression Omnibus (GEO) dataset. Functional enrichment analysis revealed the potential effects of the five-miRNA signature in tumor-related biological pathways and processes. Cell Counting Kit-8, Transwell, and wound healing assays, were used to evaluate the role of has-mir-137 in HCC cell proliferation and migration in vitro.

Conclusions

We established a novel five-miRNA signature which reliably predicted prognosis in HCC patients and which could be used to assist in both strategic counseling and personalized management in HCC.

Profiles of m6A RNA methylation regulators for the prognosis of hepatocellular carcinoma

N6-methyladenosine (m⁶A) RNA methylation, which is related to cancer initiation and progression, is dynamically regulated by the m⁶A RNA methylation regulators (including ‘writers’, ‘erasers’ and ‘readers’). However, the prognostic value of m⁶A RNA methylation regulators involved in hepatocellular carcinoma (HCC) carcinogenesis and progression remains to be elucidated. The aim of the present study was to determine the prognostic score in predicting the prognosis of HCC patients based on these regulators. In The Cancer Genome Atlas, most of the 13 major m⁶A RNA methylation regulators were found to be differentially expressed between HCC and normal samples (P<0.001). In addition, two subgroups (clusters 1/2) had also been identified by applying consensus clustering in the m⁶A RNA methylation regulators. As compared with the cluster 1 subgroup, the cluster 2 subgroup was correlated with a poorer prognosis, as shown by the Kaplan-Meier method (P=6.197e-4). A risk signature was constructed based on these findings using six m⁶A RNA methylation regulators, which could not only predict the clinicopathological features of HCCs, but also serve as an independent prognostic marker, as shown by Cox regression analysis (hazard ratio=1.219, 95% confidence interval: 1.143–1.299; P<0.001). Data from the International Cancer Genome Consortium were used for external validation. In addition, gene set enrichment analysis identified several pathways that m⁶A RNA methylation regulators were closely associated with. In conclusion, the m⁶A RNA methylation regulators are the crucial participants in the malignant progression of HCCs, which are potentially useful for prognosis stratification and therapeutic strategy development for HCC.

Profiles of prognostic alternative splicing signature in hepatocellular carcinoma

Previous studies have demonstrated the role of abnormal alternative splicing (AS) in tumor progression. This study examines the prognostic index (PI) of alternative splices (ASs) in patients with hepatocellular carcinoma (HCC). The clinical features and splicing events of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed AS (DEAS) were compared between HCC and adjacent normal samples. Univariate Cox regression analysis was used to determine changes in DEAS associated with overall survival (OS). A PI was generated from OS‐associated DEASs using Kaplan‐Meier curves, receiver operating characteristic (ROC) curves, multivariate Cox regression, and cluster analysis. Then, the correlation between DEASs and splicing factors was assessed, followed by functional and pathway enrichment analysis. We identified 34 163 ASs of 8985 genes in HCC, and 153 OS‐ASs were identified using univariate Cox regression analysis. Low‐ and high‐PI groups were determined based on the median “PI‐ALL” value according to significantly different survival (P = 2.2e − 16). The ROC curve of all PI (PI‐ALL) had an area under the curve (AUC) of 0.993 for survival status in patients with HCC. A potential regulatory network associated with prognosis of patients with HCC was established. Enrichment analysis also resulted in the identification of several pathways potentially associated with carcinogenesis and progression of HCC. Four clusters were identified that were associated with clinical features and prognosis. Our study generated comprehensive profiles of ASs in HCC. The interaction network and functional connections were used to elucidate the underlying mechanisms of AS in HCC.

Predictive value of a nomogram for melanomas with brain metastases at initial diagnosis

BACKGROUND

Estimation of incidence and prognosis of melanomas with brain metastases (MBM) at initial diagnosis based on a large cohort is lacking in current research. This study aims to construct an effective prognostic nomogram for newly diagnosed MBM.

MATERIALS AND METHODS

Patients diagnosed with melanomas from Surveillance, Epidemiology, and End Results program between 2010 and 2014 were enrolled in our study. Risk factors predicting brain metastases (BM) were identified using logistic regression analysis. Cox regression analysis was performed to identify prognostic factors of overall survival (OS). Nomogram for estimating 6-, 9-, and 12-month OS was established based on Cox regression analysis. The discriminative ability and calibration of the nomogram were tested using C statistics, calibration plots, and Kaplan-Meier curves.

RESULTS

Sixty-two thousand three hundred and sixty-nine melanoma patients were enrolled, including 928 with BM. Sex, marital status, insurance status, subsite, surgery of primary sites, radiation, chemotherapy, bone metastases, liver metastases, and lung metastases were associated with MBM at initial diagnosis. On multivariable Cox regression, the following eight variables were incorporated in the prediction of OS: age, unmarried status, absence of surgery to primary sites or unknown, absence of radiation or unknown, absence of chemotherapy or unknown, with bone metastases, with liver metastases, and with lung metastases. The nomogram showed good predictive ability as indicated by discriminative ability and calibration, with the C statistics of 0.716 (95% CI, 0.695-0.737).

CONCLUSIONS

The incidence and prognosis of MBM patients were well estimated in this study based on a large cohort. The nomogram performed well and could be a useful tool to predict prognosis.

Analysis of competing endogenous RNA network identifies a poorly differentiated cancer-specific RNA signature for hepatocellular carcinoma

Plenty of evidence has suggested that long noncoding RNAs (lncRNAs) play a vital role in competing endogenous RNA (ceRNA) networks. Poorly differentiated hepatocellular carcinoma (PDHCC) is a malignant phenotype. This paper aimed to explore the effect and the underlying regulatory mechanism of lncRNAs on PDHCC as a kind of ceRNA. Additionally, prognosis prediction was assessed. A total of 943 messenger RNAs (mRNAs), 86 miRNAs, and 468 lncRNAs that were differentially expressed between 137 PDHCCs and 235 well-differentiated HCCs were identified. Thereafter, a ceRNA network related to the dysregulated lncRNAs was established according to bioinformatic analysis and included 29 lncRNAs, 9 miRNAs, and 96 mRNAs. RNA-related overall survival (OS) curves were determined using the Kaplan-Meier method. The lncRNA ARHGEF7-AS2 was markedly correlated with OS in HCC (P = .041). Moreover, Cox regression analysis revealed that patients with low ARHGEF7-AS2 expression were associated with notably shorter survival time (P = .038). In addition, the area under the curve values of the lncRNA signature for 1-, 3-, and 5-year survival were 0.806, 0.741, and 0.701, respectively. Furthermore, a lncRNA nomogram was established, and the C-index of the internal validation was 0.717. In vitro experiments were performed to demonstrate that silencing ARHGEF7-AS2 expression significantly promoted HCC cell proliferation and migration. Taken together, our findings shed more light on the ceRNA network related to lncRNAs in PDHCC, and ARHGEF7-AS2 may be used as an independent biomarker to predict the prognosis of HCC.

Risk and prognostic nomograms for hepatocellular carcinoma with newly-diagnosed pulmonary metastasis using SEER data

Purpose

This research aimed to identify risk factors of pulmonary metastasis (PM) from hepatocellular carcinoma (HCC) and prognostic factors of patients with PM from HCC at initial diagnosis.

Methods

Patients diagnosed with HCC between 2010 and 2015 were reviewed retrospectively in the Surveillance, Epidemiology, and End Results (SEER) database. Patients with PM from HCC at initial diagnosis were identified from the entire cohort. Predictors for PM from HCC were identified by multivariate logistic regression analysis. Independent prognostic factors for patients with PM were determined by univariate and multivariate Cox regression analysis. Nomograms were also constructed for quantifying risk of metastasis and overall survival estimation visually.

Results

Our research included 30,641 patients diagnosed with HCC, of whom 1,732 cases were with PM from HCC at initial diagnosis. The risk factors causing PM from HCC were age (P = 0.001), race (P < 0.001), primary tumor size (P < 0.001), T stage (P < 0.001), N stage (P < 0.001), alpha-fetoprotein (P < 0.001), bone metastasis (P < 0.001), brain metastasis (P < 0.001), and intrahepatic metastasis (P < 0.001). The significantly prognostic factors for overall survival were age (P = 0.014), T stage (P = 0.009), surgical approach (P < 0.001), and chemotherapy (P < 0.001). Harrell’s C-index statistics of two nomograms were 0.768 and 0.687 respectively, indicating satisfactory predictive power.

Conclusions

This research provided evaluation of risk factors and prognosis for patients with PM from HCC. Two nomograms we developed can be convenient individualized tools to facilitate clinical decision-making.

Alternative splicing events are prognostic in hepatocellular carcinoma

Alternative splicing events (ASEs) play a role in cancer development and progression. We investigated whether ASEs are prognostic for overall survival (OS) in hepatocellular carcinoma (HCC). RNA sequencing data was obtained for 343 patients included in The Cancer Genome Atlas. Matched splicing event data for these patients was then obtained from the TCGASpliceSeq database, which includes data for seven types of ASEs. Univariate and multivariate Cox regression analysis demonstrated that 3,814 OS-associated splicing events (OS-SEs) were correlated with OS. Prognostic indices were developed based on the most significant OS-SEs. The prognostic index based on all seven types of ASEs (PI-ALL) demonstrated superior efficacy in predicting OS of HCC patients at 2,000 days compared to those based on single ASE types. Patients were stratified into two risk groups (high and low) based on the median prognostic index. Kaplan-Meier survival analysis demonstrated that PI-ALL had the greatest capacity to distinguish between patients with favorable vs. poor outcomes. Finally, univariate Cox regression analysis demonstrated that the expression of 23 splicing factors was correlated with OS-SEs in the HCC cohort. Our data indicate that a prognostic index based on ASEs is prognostic for OS in HCC.