Prognostic value of geriatric nutritional risk index and prognostic nutritional index in hepatocellular carcinoma.

BACKGROUND: The geriatric nutritional risk index (GNRI) and prognostic nutritional index (PNI) are considered prognostic factors for several cancers. This study aimed to investigate the relationship between the GNRI and PNI for survival outcomes in patients with hepatocellular carcinoma (HCC). METHODS: We retrospectively analyzed 1666 patients with HCC who underwent hepatectomy. Restricted cubic spline regression was used to analyze the relationship between the GNRI and PNI for recurrence and mortality. Cox proportional hazards regression analysis was used to evaluate the risk factors associated with overall survival (OS) and recurrence-free survival (RFS). Interaction analysis was performed to investigate the comprehensive effects of the GNRI, PNI, and subgroup parameters on the prognosis of patients with HCC. RESULTS: The risks of death and recurrence decreased rapidly and gradually stabilized as the GNRI and PNI scores increased. Patients with lower GNRI and PNI scores had significantly shorter OS and RFS rates than those with higher scores. Multivariate analysis showed that high GNRI [HR and 95%CI = 0.77 (0.70-0.85), P < 0.001] and PNI [HR and 95%CI = 0.77 (0.70-0.86), P < 0.001] scores were associated with decreased mortality risk. This trend was maintained by confounding variables in adjusted models despite partial interactions with clinical factors. The combined GNRI and PNI analysis showed that HCC patients with high GNRI and PNI had longer OS and RFS. CONCLUSIONS: The GNRI and PNI showed good survival predictions in patients with HCC. Combining the GNRI with PNI may help predict the prognosis of patients (age>18 years) with HCC after hepatectomy.

High-Mobility Group AT-Hook 1 Served as a Prognosis Biomarker and Associated with Immune Infiltrate in Hepatocellular Carcinoma.

BACKGROUND: The protein high-mobility group AT-hook 1 (HMGA1) has been demonstrated that modulated cellular proliferation, invasion, and apoptosis with a poor prognosis in miscellaneous carcinomas. However, the mechanism of circumstantial carcinogenesis and association with the immune microenvironment of HMGA1 in hepatocellular carcinoma (HCC) had not been extensively explored. METHODS: The gene expression, clinicopathological correlation, and prognosis analysis were performed in the data obtained from TCGA. The results were further validated by ICGC and GEO database and external validation cohort from Guangxi. The HMGA1 protein expression was further examined in the HPA database. Biological function analyses were conducted by GSEA, STRING database, and Coexpedia online tool. Using TIMER and CIBERSORT method, the relationship between immune infiltrate and HMGA1 was investigated. RESULTS: In HCC, HMGA1 had much higher transcriptional and proteomic expression than in corresponding paraneoplastic tissue. Patients with high HMGA1 expression had a poor prognosis and unpromising clinicopathological features. High HMGA1 expression was closely related to the cell cycle, tumorigenesis, substance metabolism, and immune processes by regulating complex signaling pathways. Notably, HMGA1 may be associated with TP53 mutational carcinogenesis. Moreover, increased HMGA1 expression may lead to an increase in immune infiltration and a decrease in tumor purity in HCC. CIBERSORT analysis elucidated that the amount of B cell naive, B cell memory, T cells gamma delta, macrophages M2, and mast cell resting decreased when HMGA1 expression was high, whereas T cells follicular helper, macrophages M0, and Dendritic cells resting increased. CONCLUSION: In conclusions, HMGA1 is a potent prognostic biomarker and a sign of immune infiltration in HCC, which may be a potential immunotherapy target for HCC.

Glomerular disease classification and lesion identification by machine learning.

BACKGROUND: Classification of glomerular diseases and identification of glomerular lesions require careful morphological examination by experienced nephropathologists, which is labor-intensive, time-consuming, and prone to interobserver variability. In this regard, recent advance in machine learning-based image analysis is promising. METHODS: We combined Mask Region-based Convolutional Neural Networks (Mask R-CNN) with an additional classification step to build a glomerulus detection model using human kidney biopsy samples. A Long Short-Term Memory (LSTM) recurrent neural network was applied for glomerular disease classification, and another two-stage model using ResNeXt-101 was constructed for glomerular lesion identification in cases of lupus nephritis. RESULTS: The detection model showed state-of-the-art performance on variedly stained slides with F1 scores up to 0.944. The disease classification model showed good accuracies up to 0.940 on recognizing different glomerular diseases based on H&E whole slide images. The lesion identification model demonstrated high discriminating power with area under the receiver operating characteristic curve up to 0.947 for various glomerular lesions. Models showed good generalization on external testing datasets. CONCLUSION: This study is the first-of-its-kind showing how each step of kidney biopsy interpretation carried out by nephropathologists can be captured and simulated by machine learning models. The models were integrated into a whole slide image viewing and annotating platform to enable nephropathologists to review, correct, and confirm the inference results. Further improvement on model performances and incorporating inputs from immunofluorescence, electron microscopy, and clinical data might realize actual clinical use.

SOX4 as biomarker in hepatitis B virus-associated hepatocellular carcinoma.

Background: Hepatitis B virus infection is associated with liver disease, including cancers. In this study, we assessed the power of sex-determining region Y (SRY)-related high-mobility group (HMG)-box 4(SOX4) gene to predict the clinical course of hepatocellular carcinoma (HCC). Methods: To evaluate the differential expression of SOX4 and its diagnostic and prognostic potential in HCC, we analyzed the GSE14520 dataset. Stratified analysis and joint-effect analysis were done using SOX4 and clinical factor. We then designed a nomogram for predicting the clinical course of HCC. Differential SOX4 expression and its correlation with tumor stage as well as its diagnostic and prognostic value were analyzed on the oncomine and GEPIA websites. Gene set enrichment analysis was explored as well as candidate gene ontology and metabolic pathways modulated by in SOX4 HCC. Results: Our analysis revealed that the level of SOX4 was significantly upregulated in tumor issue (P <0.001). This observation was validated through oncomine dataset and MERAV analysis (all P <0.05). Diagnostic receiver operating characteristic (ROC) analysis of SOX4 suggested it has diagnostic potential in HCC (GSE14520 dataset: P <0.001, area under curve (AUC) = 0.782; Oncomine: (Wurmbach dataset) P = 0.002, AUC = 0.831 and (Mas dataset) P <0.001, AUC = 0.947). In addition, SOX4 exhibited high correlation with overall survival of HBV-associated HCC (adjusted P = 0.004, hazard ratio (HR) (95% confidence interval (CI)) = 2.055 (1.261-3.349) and recurrence-free survival (adjusted P = 0.008, HR (95% CI) = 1.721 (1.151-2.574). These observations which were verified by GEPIA analysis for overall survival (P = 0.007) and recurrence-free survival (P= 0.096). Gene enrichment analysis revealed that affected processes included lymphocyte differentiation, pancreatic endocrine pathways, and insulin signaling pathway. SOX4 prognostic value was evaluated using nomogram analysis for HCC 1, 3, and 5-year, survival. Conclusion: Differential SOX4 expression presents an avenue of diagnosing and predicting clinical course of HCC. In HCC, SOX4 may affect TP53 metabolic processes, lymphocyte differentiation and the insulin signaling pathway.

Predicting aggressive histopathological features in esophageal cancer with positron emission tomography using a deep convolutional neural network.

BACKGROUND: The presence of lymphovascular invasion (LVI) and perineural invasion (PNI) are of great prognostic importance in esophageal squamous cell carcinoma. Currently, positron emission tomography (PET) scans are the only means of functional assessment prior to treatment. We aimed to predict the presence of LVI and PNI in esophageal squamous cell carcinoma using PET imaging data by training a three-dimensional convolution neural network (3D-CNN). METHODS: Seven hundred and ninety-eight PET scans of patients with esophageal squamous cell carcinoma and 309 PET scans of patients with stage I lung cancer were collected. In the first part of this study, we built a 3D-CNN based on a residual network, ResNet, for a task to classify the scans into esophageal cancer or lung cancer. In the second stage, we collected the PET scans of 278 patients undergoing esophagectomy for a task to classify and predict the presence of LVI/PNI. RESULTS: In the first part, the model performance attained an area under the receiver operating characteristic curve (AUC) of 0.860. In the second part, we randomly split 80%, 10%, and 10% of our dataset into training set, validation set and testing set, respectively, for a task to classify the scans into the presence of LVI/PNI and evaluated the model performance on the testing set. Our 3D-CNN model attained an AUC of 0.668 in the testing set, which shows a better discriminative ability than random guessing. CONCLUSIONS: A 3D-CNN can be trained, using PET imaging datasets, to predict LNV/PNI in esophageal cancer with acceptable accuracy.

Prognostic value of Glypican family genes in early-stage pancreatic ductal adenocarcinoma after pancreaticoduodenectomy and possible mechanisms.

BACKGROUND: This study explored the prognostic significance of Glypican (GPC) family genes in patients with pancreatic ductal adenocarcinoma (PDAC) after pancreaticoduodenectomy using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). METHODS: A total of 112 PDAC patients from TCGA and 48 patients from GEO were included in the analysis. The relationship between overall survival and the expression of GPC family genes as well as basic clinical characteristics was analyzed using the Kaplan-Meier method with the log-rank test. Joint effects survival analysis was performed to further examine the relationship between GPC genes and prognosis. A prognosis nomogram was established based on clinical characteristics and prognosis-related genes. Prognosis-related genes were investigated by genome-wide co-expression analysis and gene set enrichment analysis (GSEA) was carried out to identify potential mechanisms of these genes affecting prognosis. RESULTS: In TCGA database, high expression of GPC2, GPC3, and GPC5 was significantly associated with favorable survival (log-rank P = 0.031, 0.021, and 0.028, respectively; adjusted P value = 0.005, 0.022, and 0.020, respectively), and joint effects analysis of these genes was effective for prognosis prediction. The prognosis nomogram was applied to predict the survival probability using the total scores calculated. Genome-wide co-expression and GSEA analysis suggested that the GPC2 may affect prognosis through sequence-specific DNA binding, protein transport, cell differentiation and oncogenic signatures (KRAS, RAF, STK33, and VEGFA). GPC3 may be related to cell adhesion, angiogenesis, inflammatory response, signaling pathways like Ras, Rap1, PI3K-Akt, chemokine, GPCR, and signatures like cyclin D1, p53, PTEN. GPC5 may be involved in transcription factor complex, TFRC1, oncogenic signatures (HOXA9 and BMI1), gene methylation, phospholipid metabolic process, glycerophospholipid metabolism, cell cycle, and EGFR pathway. CONCLUSION: GPC2, GPC3, and GPC5 expression may serve as prognostic indicators in PDAC, and combination of these genes showed a higher efficiency for prognosis prediction.

Genome-wide RNA-sequencing dataset reveals the prognostic value and potential molecular mechanisms of lncRNA in non-homologous end joining pathway 1 in early stage Pancreatic Ductal Adenocarcinoma.

Objective: Our current study is to explore the prognostic value and molecular mechanisms underlying the role of lncRNA in non-homologous end joining pathway 1 (LINP1) in early stage pancreatic ductal adenocarcinoma (PDAC). Methods: Genome-wide RNA-seq datasets of 112 early stage PDAC patients were got from The Cancer Genome Atlas and analyzed using multiple online tools. Results: Overall survival in high LINP1 expression patients was shorter than those with low expression (high-LINP1 vs. low-LINP1=481 vs. 592 days, log-rank P=0.0432). The multivariate Cox proportional hazard regression model suggested that high-LINP1 patients had a markedly higher risk of death than low-LINP1 patients (adjusted P=0.004, hazard ratio=2.214, 95% confidence interval=1.283-3.820). Analysis of genome-wide co-expressed genes, screening of differentially expressed genes, and gene set enrichment analysis indicated that LINP1 may be involved in the regulation of cell proliferation-, cell adhesion- and cell cycle-related biological processes in PDAC. Six small-molecule compounds including STOCK1N-35874, fenofibrate, exisulind, NU-1025, vinburnine, and doxylamine were identified as potential LINP1-targeted drugs for the treatment of PDAC. Conclusions: Our study indicated that LINP1 may serve as a prognostic biomarker of early stage PDAC. Analysis of genome-wide datasets led to the elucidation of the underlying mechanisms and identified six potential targeted drugs for the treatment of early PDAC.

Clinical significance of long non-coding RNA DUXAP8 and its protein coding genes in hepatocellular carcinoma.

Backgrounds: Hepatocellular carcinoma (HCC) is a lethal malignancy worldwide that is difficult to diagnose during the early stages and its tumors are recurrent. Long non-coding RNAs (lncRNAs) have increasingly been associated with tumor biomarkers for diagnosis and prognosis. This study attempts to explore the potential clinical significance of lncRNA DUXAP8 and its co-expression related protein coding genes (PCGs) for HCC. Method: Data from a total of 370 HCC patients from The Cancer Genome Atlas were utilized for the analysis. DUXAP8 and its top 10 PCGs were explored for their diagnostic and prognostic implications for HCC. A risk score model and nomogram were constructed for prognosis prediction using prognosis-related genes and DUXAP8. Molecular mechanisms of DUXAP8 and its PCGs involved in HCC initiation and progression were investigated. Then, potential target drugs were identified using genome-wide DUXAP8-related differentially expressed genes in a Connectivity Map database. Results: The top 10 PCGs were identified as: RNF2, MAGEA1, GABRA3, MKRN3, FAM133A, MAGEA3, CNTNAP4, MAGEA6, MALRD1, and DGKI. Diagnostic analysis indicated that DUXAP8, MEGEA1, MKRN3, and DGKI show diagnostic implications (all area under curves ≥0.7, p≤0.05). Prognostic analysis indicated that DUXAP8 and RNF2 had prognostic implications for HCC (adjusted p=0.014 and 0.008, respectively). The risk score model and nomogram showed an advantage for prognosis prediction. A total of 3 target drugs were determined: cinchonine, bumetanide and amiprilose and they may serve as potential therapeutic targets for HCC. Conclusion: Functioning as an oncogene, DUXAP8 is overexpressed in tumor tissue and may serve as both a diagnostic and prognosis biomarker for HCC. MEGEA1, MKRN3, and DGKI maybe potential diagnostic biomarkers and DGKI may also be potentially prognostic biomarkers for HCC.

Investigation of the association between C-X-C motif chemokine receptor subunits and tumor infiltration levels and prognosis in patients with early-stage pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the malignancies with the highest morality rate due to postoperative local invasion and distant metastasis. Although C-X-C motif chemokine receptor (CXCR) subunits have been reported as prognostic indicators in gastric cancer, the prognostic value of CXCR subunits in PDAC remains poorly understood. In the present study, the expression levels and biological functions of CXCR subunits were investigated using multiple publicly accessible bioinformatic platforms and databases. Survival analysis was used to evaluate the prognostic value of CXCR subunits in 112 early-stage PDAC cases by setting the median expression levels as the cut-off values. A nomogram was constructed to combine CXCR subunit expression levels and clinical data for prognosis prediction. Moreover, the association between CXCR subunit expression levels and tumor infiltration levels were detected in PDAC. The expression levels of CXCR subunits were elevated in PDAC tumor tissues. In the multivariate Cox proportional risk regression model, high CXCR2, CXCR4 and CXCR6 expression levels in early-stage PDAC were associated with a more favorable prognosis. Further, it was demonstrated that the differential expression levels of CXCR subunits in PDAC for combined survival analysis could contribute to risk stratification. The nomogram model demonstrated the contribution of CXCR subunits and clinical features in the prognosis of PDAC. Gene Set Enrichment Analysis suggested that CXCR subunits serve a role in immunomodulatory functions. The expression levels and somatic copy number alterations of CXCR subunits were associated with tumor infiltration levels in PDAC. CXCR subunits were associated with prognosis in patients with early-stage PDAC and may be potential drug targets for the treatment of pancreatic cancer.

Novel candidate biomarkers of origin recognition complex 1, 5 and 6 for survival surveillance in patients with hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) has high morbidity and mortality and lacks effective biomarkers for early diagnosis and survival surveillance. Origin recognition complex (ORC), consisting of ORC1-6 isoforms, was examined to assess the potential significance of ORC isoforms for HCC prognosis. Methods: Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA) databases were used to examine differential isoform expression, stage-specific expression, calculate Pearson correlations and perform survival analysis. A human protein atlas database was utilized to evaluate the protein expression of ORCs in liver tissue. The cBioPortal database was used to assess isoform mutations and the survival significance of ORCs in HCC. Cytoscape software was employed to construct gene ontologies, metabolic pathways and gene-gene interaction networks. Results: Differential expression analysis indicated that ORC1 and ORC3-6 were highly expressed in tumor tissues in the Oncomine and GEPIA databases, while ORC2 was not. All the ORCs were showed positive and statistically significant correlations with each other (all P<0.001). ORC1-2 and ORC4-6 expressions were associated with disease stages I-IV (all P<0.05), but ORC3 was not. Survival analysis found that ORC1 and ORC4-6 expressions were associated with overall survival (OS), and ORC1-3 and ORC5-6 expression were associated with recurrence-free survival (RFS; all P<0.05). In addition, low expression of these ORC genes consistently indicated better prognosis compared with high expression. Protein expression analysis revealed that ORC1 and ORC3-6 were expressed in normal liver tissues, whereas ORC2 was not. Enrichment analysis indicated that ORCs were associated with DNA metabolic process, sequence-specific DNA binding and were involved in DNA replication, cell cycle, E2F-enabled inhibition of pre-replication complex formation and G1/S transition. Conclusions: Differentially expressed ORC1, 5 and 6 are candidate biomarkers for survival prediction and recurrence surveillance in HCC.

Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning.

Pathologic diagnosis of nasopharyngeal carcinoma (NPC) can be challenging since most cases are nonkeratinizing carcinoma with little differentiation and many admixed lymphocytes. Our aim was to evaluate the possibility to identify NPC in nasopharyngeal biopsies using deep learning. A total of 726 nasopharyngeal biopsies were included. Among them, 100 cases were randomly selected as the testing set, 20 cases as the validation set, and all other 606 cases as the training set. All three datasets had equal numbers of NPC cases and benign cases. Manual annotation was performed. Cropped square image patches of 256 × 256 pixels were used for patch-level training, validation, and testing. The final patch-level algorithm effectively identified NPC patches, with an area under the receiver operator characteristic curve (AUC) of 0.9900. Using gradient-weighted class activation mapping, we demonstrated that the identification of NPC patches was based on morphologic features of tumor cells. At the second stage, whole-slide images were sequentially cropped into patches, inferred with the patch-level algorithm, and reconstructed into images with a smaller size for training, validation, and testing. Finally, the AUC was 0.9848 for slide-level identification of NPC. Our result shows for the first time that deep learning algorithms can identify NPC.

Oncogene PLCE1 may be a diagnostic biomarker and prognostic biomarker by influencing cell cycle, proliferation, migration, and invasion ability in hepatocellular carcinoma cell lines.

Hepatocellular carcinoma (HCC) is a lethal malignancy worldwide. HCC has traits of late diagnosis and high recurrence. This study explored potential diagnosis and prognosis significance of phospholipase C epsilon 1 (PLCE1) in HCC. The messenger RNA (mRNA) levels and diagnostic value of PLCE1 were determined by real-time polymerase chain reaction and online databases GEPIA, oncomine, and GSE14520 data set. Survival analysis used the Kaplan-Meier Plotter website. Cell cycle, proliferation, migration, and invasion assays were performed with downregulated PLCE1 expression in HCC-M and HepG2 cell lines. PLCE1 was differentially expressed and highly expressed in tumors and had low expression in nontumor tissues (all p < .05). The diagnostic value of PLCE1 was validated with the datasets (all p < .01, all areas under curves > 0.7). PLCE1 mRNA expression was associated with the overall and relapse-free survival (both p < .05). Functional experiments indicated that downregulation of PLCE1 expression led to increased G1 stage in cell cycle and decreased cell proliferation, migration, and invasion compared with a negative control group (all p ≤ .05). The oncogene PLCE1 was differentially expressed in HCC and non-HCC tissues. It is a candidate for diagnosis and serves as prognosis biomarker. PLCE1 influenced survival by affecting the cell cycle, proliferation, migration, and invasion ability.

Comprehensive investigation of p53, p21, nm23, and VEGF expression in hepatitis B virus-related hepatocellular carcinoma overall survival after hepatectomy.

Objective: The goal of our current study is to assess the immunohistochemical of p53, p21, nm23, and VEGF expression in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) prognosis after hepatectomy, as well as the prospective molecular mechanisms of prognostic indicator. Methods: There were 419 HBV-related HCC patients who were from southern China of Guangxi province and were used to evaluate the immunohistochemical expression for these biomarkers in prognosis. A genome-wide expression microarray dataset of HBV-related HCC were obtained from GSE14520. Results: In our study, the expression of p53, p21, and nm23 in cancer tissues of patients with hepatitis B-related hepatocellular carcinoma did not affected the clinical outcome of 2 years, 5 years or overall. Patients with high expression of VEGF had a worse overall survival after 2 years of surgery than patients with low expression (adjusted P=0.040, adjusted HR = 1.652, 95% CI = 1.024-2.665). Survival analysis of VEGF in GSE14520 cohort also demonstrated that VEGF mRNA expression also significantly associated with HBV-related HCC OS (adjusted P=0.035, adjusted HR =1.651, 95% CI =1.035-2.634). The prospective molecular mechanisms by co-expression analysis suggested that VEGF might be correlated to regulation of cell proliferation, cell growth and apoptotic process, Rap1 signaling pathway, HIF-1 signaling pathway, PPAR signaling pathway, cell cycle. Whereas the GSEA suggested that VEGF might involve in the regulation of HIF and HIF1A pathway, and TP53 regulation pathway. Conclusion: Our findings suggested that VEGF might be a prognostic indicator of HBV-related HCC, and we also identified the VEGF prospective molecular mechanisms through the whole genome co-expression and GSEA approaches.

Identification of Key Genes in Purine Metabolism as Prognostic Biomarker for Hepatocellular Carcinoma.

BACKGROUND: Deregulated purine metabolism is critical for fast-growing tumor cells by providing nucleotide building blocks and cofactors. Importantly, purine antimetabolites belong to the earliest developed anticancer drugs and are still prescribed in clinics today. However, these antimetabolites can inhibit non-tumor cells and cause undesired side effects. As liver has the highest concentration of purines, it makes liver cancer a good model to study important nodes of dysregulated purine metabolism for better patient selection and precisive cancer treatment. METHODS: By using a training dataset from TCGA, we investigated the differentially expressed genes (DEG) of purine metabolism pathway (hsa00230) in hepatocellular carcinoma (HCC) and determined their clinical correlations to patient survival. A prognosis model was established by Lasso-penalized Cox regression analysis, and then validated through multiple examinations including Cox regression analysis, stratified analysis, and nomogram using another ICGC test dataset. We next treated HCC cells using chemical drugs of the key enzymes in vitro to determine targetable candidates in HCC. RESULTS: The DEG analysis found 43 up-regulated and 2 down-regulated genes in the purine metabolism pathway. Among them, 10 were markedly associated with HCC patient survival. A prognostic correlation model including five genes (PPAT, DCK, ATIC, IMPDH1, RRM2) was established and then validated using the ICGC test dataset. Multivariate Cox regression analysis found that both prognostic risk model (HR = 4.703 or 3.977) and TNM stage (HR = 2.303 or 2.957) independently predicted HCC patient survival in the two datasets respectively. The up-regulations of the five genes were further validated by comparing between 10 pairs of HCC tissues and neighboring non-tumor tissues. In vitro cellular experiments further confirmed that inhibition of IMPDH1 significantly repressed HCC cell proliferation. CONCLUSION: In summary, this study suggests that purine metabolism is deregulated in HCC. The prognostic gene correlation model based on the five purine metabolic genes may be useful in predicting HCC prognosis and patient selection. Moreover, the deregulated genes are targetable by specific inhibitors.

Oncogene UBE2I enhances cellular invasion, migration and proliferation abilities via autophagy-related pathway resulting in poor prognosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a worldwide malignancy with high morbidity and mortality. In this study, ubiquitin conjugating enzyme E2I (UBE2I), a small ubiquitin-like modifier E2 enzyme reportedly expressed in tumors, was examined for its potential effects in HCC. Bioinformatics analysis was performed based on HCCDB, TIMER, and Kaplan-Meier plotter databases to explore the clinical implications in HCC. An siRNA kit was used to downregulate UBE2I, and in vitro experiments-including migration, invasion and proliferation assays-were performed to examine UBE2I expression in HCC. Western blot (WB) was used to determine whether downregulated UBE2I expression influenced the prognosis of HCC via autophagy pathways. Finally, RNA-sequencing was performed to explore candidate molecular mechanisms underlying the effect of UBE2I. Bioinformatics analysis including stratification by alcohol ingestion and hepatitis status in HCC showed that highly expressed UBE2I was not only correlated with poor prognosis, but was also associated with immune infiltrates. In vitro experiments showed that high expression of UBE2I was associated with increased migration, invasion and proliferation of HCC cells. WB results indicated that downregulated expression of UBE2I was associated with higher levels of autophagy-related proteins including LC3A/B, Beclin-1 and ATG16L1. Moreover, RNA-sequencing results suggested that UBE2I was involved in hepatocarcinogenesis, non-alcohol fatty liver disease, steatohepatitis, liver fibrosis, inflammation, hepatoblastoma, tumor angiogenesis, type 2 mellitus diabetes, biliary tract disease and other diseases. We conclude that oncogene UBE2I is associated with poor prognosis of HCC via autophagy pathways and may be involved in hepatocarcinogenesis, tumor angiogenesis, non-alcohol fatty liver disease and inflammation.

Diagnostic and prognostic biomarkers of Human Leukocyte Antigen complex for hepatitis B virus-related hepatocellular carcinoma.

Background: Hepatitis B virus infection had been identified its relationship with liver diseases, including liver tumors. We aimed to explore diagnostic and prognostic values between the Human Leukocyte Antigen (HLA) complex and hepatocellular carcinoma (HCC). Methods: We used the GSE14520 dataset to explore diagnostic and prognostic significance between HLA complex and HCC. A nomogram was constructed to predict survival probability of HCC prognosis. Gene set enrichment analysis was explored using gene ontologies and metabolic pathways. Validation of prognostic values of the HLA complex was performed in the Kaplan-Meier Plotter website. Results: We found that HLA-C showed the diagnostic value (P <0.0001, area under curve: 0.784, sensitivity: 93.14%, specificity: 62.26%). In addition, HLA-DQA1 and HLA-F showed prognostic values for overall survival, and HLA-A, HLA-C, HLA-DPA1 and HLA-DQA1 showed prognostic values for recurrence-free survival (all P ≤ 0.05, elevated 0.927, 0.992, 1.023, 0.918, 0.937 multiples compared to non-tumor tissues, respectively). Gene set enrichment analysis found that they were involved in antigen processing and toll like receptor signalling pathway, etc. The nomogram was evaluated for survival probability of HCC prognosis. Validation analysis indicated that HLA-C, HLA-DPA1, HLA-E, HLA-F and HLA-G were associated with HCC prognosis of overall survival (all P ≤ 0.05, elevated 0.988 and 0.997 multiples compared to non-tumor tissues, respectively). Conclusion: HLA-C might be a diagnostic and prognostic biomarker for HCC. HLA-DPA1 and HLA-F might be prognostic biomarkers for HCC.

Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome.

In esophageal cancer, few prediction tools can be confidently used in current clinical practice. We developed a deep convolutional neural network (CNN) with 798 positron emission tomography (PET) scans of esophageal squamous cell carcinoma and 309 PET scans of stage I lung cancer. In the first stage, we pretrained a 3D-CNN with all PET scans for a task to classify the scans into esophageal cancer or lung cancer. Overall, 548 of 798 PET scans of esophageal cancer patients were included in the second stage with an aim to classify patients who expired within or survived more than one year after diagnosis. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. In the pretrain model, the deep CNN attained an AUC of 0.738 in identifying patients who expired within one year after diagnosis. In the survival analysis, patients who were predicted to be expired but were alive at one year after diagnosis had a 5-year survival rate of 32.6%, which was significantly worse than the 5-year survival rate of the patients who were predicted to survive and were alive at one year after diagnosis (50.5%, p < 0.001). These results suggest that the prediction model could identify tumors with more aggressive behavior. In the multivariable analysis, the prediction result remained an independent prognostic factor (hazard ratio: 2.830; 95% confidence interval: 2.252-3.555, p < 0.001). We conclude that a 3D-CNN can be trained with PET image datasets to predict esophageal cancer outcome with acceptable accuracy.

Aldehyde dehydrogenase 1 (ALDH1) isoform expression and potential clinical implications in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most prevalent and life-threatening malignancies worldwide. There are few diagnostic and prognostic biomarkers and druggable targets for HCC. Aldehyde dehydrogenase 1 (ALDH1) is a marker of stem cells in a variety of cancers, but the mRNA levels and prognostic value of ALDH1 isoforms in HCC patients remain unknown. In the present study, gene ontology annotation of the ALDH1 family was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID), and the gene pathway analsis was performed using GeneMANIA software. The initial prognostic value of ALDH1 expression in 360 HCC patients was assessed using the OncoLnc database. The expression levels of ALDH1 isoforms in normal liver tissues and clinical specimens of cancer vs. normal control datasets were determined using the GTEx and Oncomine databases, respectively. We then analyzed the prognostic value of ALDH1 expression in 212 hepatitis B virus (HBV)-related HCC patients using the GEO database. We found that the ALDH1 isoform showed high aldehyde dehydrogenase activity. The ALDH1A1, ALDH1B1, and ALDH1L1 genes encoded for the ALDH1 enzyme. High ALDH1B1 expression had protective qualities in HCC patients. Moreover, HBV-related HCC patients who showed high ALDH1L1 gene expression had a better clinical outcomes. In addition, high ALDH1A1 expression was associated with a 57-month recurrence-free survival in HBV-related HCC patients. High ALDH1B1 expression was protective for HCCs with multiple nodules and high serum alpha-fetoprotein (AFP) level. Furthermore, high serum AFP levels contributed to lower ALDH1L1. ALDH1A1, ALDH1B1, and ALDH1L1, all of which were considered promising diagnostic and prognostic markers as well as potential drug targets.

Genome-Wide Association Study of MKI67 Expression and its Clinical Implications in HBV-Related Hepatocellular Carcinoma in Southern China.

BACKGROUND/AIMS: Hepatocellular carcinoma (HCC) is a common malignant tumor with a high rate of recurrence. Immunohistochemical analysis of the marker of proliferation Ki-67 (MKI67) is used to assess proliferation activity of HCC The regulation of MKI67 expression remains unclear in HCC This study aims to explore the association between MKI67 expression and gene variants. METHODS: A total of 195 hepatitis B virus (HBV)-related HCC patients were genotyped using Illumina HumanExome BeadChip-12-1_A (242,901 markers). An independent cohort (97 subjects) validated the association of polymorphism determinants and candidate genes with MKI67 expression. The relationships between MKI67 with p53 and variants of candidate genes in the clinical outcomes of HCC patients were analyzed. RESULTS: We found that MKI67 combined with p53 was associated with a 3-year recurrence-free survival and five variants near TTN and CCDC8 were associated with MKI67 expression. TTN harboring rs2288563-TT and rs2562832-AA+CA indicated a favorable outcome for HCC patients. CONCLUSION: Variants near TTN and CCDC8 were associated with MKI67 expression, and rs2288563 and rs2562832 in TTN are potential biomarkers for the prediction of clinical outcomes in HBV-related HCC patients.