Transhepatectomy combined with arterial chemoembolization and transcatheter arterial chemoembolization in the treatment of hepatocellular carcinoma: a clinical prognostic analysis.

BACKGROUND: The prognosis of patients undergoing hepatectomy combined with transarterial chemoembolization (TACE) and TACE alone was examined in order to better understand the role of hepatectomy in the treatment of hepatocellular carcinoma (HCC). In this work, we also created a model and investigated the variables influencing overall survival (OS) in HCC patients. METHODS: Retrospective analysis of 1083 patients who received TACE alone as the control group and 188 patients who received TACE after surgery in a total of 1271 HCC patients treated with LR + TACE or TACE at three third-class hospitals in China. It was done using the Propensity Score Matching (PSM) technique. The differences in OS between the two groups were compared, and OS-influencing factors were looked at. The main endpoint is overall survival. In this study, the COX regression model was used to establish the nomogram. RESULTS: The median OS of the LR + TACE group was not attained after PSM. The median OS for the TACE group was 28.8 months (95% CI: 18.9-38.7). The median OS of the LR + TACE group was higher than that of the TACE group alone, indicating a significant difference between the two groups (χ2 = 16.75, P < 0.001). While it was not achieved in the LR + TACE group, the median OS for patients with lymph node metastases in the TACE group alone was 18.8 months. The two groups differed significantly from one another (χ2 = 4.105, P = 0.043). In patients with distant metastases, the median OS of the LR + TACE treatment group was not achieved, and the median OS of the TACE group alone was 12.0 months. The difference between the two groups was sizable (χ2 = 5.266, P = 0.022). The median OS for patients with PVTT following PSM was 30.1 months in the LR + TACE treatment group and 18.7 months in the TACE alone group, respectively. The two groups differed significantly from one another (χ2 = 5.178, P = 0.023); There was no discernible difference between the two groups in terms of median overall survival (OS), which was 30.1 months for patients with lymph node metastasis and 19.2 months for those without (P > 0.05); Regarding the median OS for patients with distant metastases, which was not achieved and 8.5 months, respectively, there was a significant difference between the two groups (χ2 = 5.759, P = 0.016). We created a new nomogram to predict 1-, 2-, and 3-year survival rates based on multiple independent predictors in COX multivariate analysis. The cohort's C-index is 0.705. The area under the curve (AUC value) for predicting 1-, 2-, and 3-year survival rates were shown by the subject operating characteristic (ROC) curve linked to the nomogram to be 0.730, 0.728, and 0.691, respectively. CONCLUSIONS: LR + TACE can increase OS, delay tumor recurrence, and improve prognosis in HCC patients when compared to TACE alone. Additionally, the nomogram we created does a good job of forecasting the 1-year survival rate of hepatocellular carcinoma.

A glycosylation-related signature predicts survival in pancreatic cancer.

BACKGROUND: Tumor initiation and progression are closely associated with glycosylation. However, glycosylated molecules have not been the subject of extensive studies as prognostic markers for pancreatic cancer. The objectives of this study were to identify glycosylation-related genes in pancreatic cancer and use them to construct reliable prognostic models. MATERIALS AND METHODS: The Cancer Genome Atlas and Gene Expression Omnibus databases were used to assess the differential expression of glycosylation-related genes; four clusters were identified based on consistent clustering analysis. Kaplan-Meier analyses identified three glycosylation-related genes associated with overall survival. LASSO analysis was then performed on The Cancer Genome Atlas and International Cancer Genome Consortium databases to identify glycosylation-related signatures. We identified 12 GRGs differently expressed in pancreatic cancer and selected three genes (SEL1L, TUBA1C, and SDC1) to build a prognostic model. Thereafter, patients were divided into high and low-risk groups. Eventually, we performed Quantitative real-time PCR (qRT-PCR) to validate the signature. RESULTS: Clinical outcomes were significantly poorer in the high-risk group than in the low-risk group. There were also significant correlations between the high-risk group and several risk factors, including no-smoking history, drinking history, radiotherapy history, and lower tumor grade. Furthermore, the high-risk group had a higher proportion of immune cells. Eventually, three glycosylation-related genes were validated in human PC cell lines. CONCLUSION: This study identified the glycosylation-related signature for pancreatic cancer. It is an effective predictor of survival and can guide treatment decisions.

Effect of Dietary Ramie Powder at Various Levels on the Growth Performance, Meat Quality, Serum Biochemical Indices and Antioxidative Capacity of Yanling White Geese.

To investigate the effects of different levels of ramie powder (Boehmeria nivea (L.) Gaudich.) (i.e., 0%, 6%, 12% and 24%) on the production performance, serum biochemical indices, antioxidative capacity and intestinal development of Yanling white geese, a total of 256 geese at 56 days of age were randomly divided into four groups and fed a control diet and the control diet supplemented with 6%, 12% and 24% ramie powder, respectively, for 42 days. The results show that dietary supplementation with 12% ramie powder significantly increased the average final weight (p < 0.05) and tended to improve the average daily gain (ADG) and feed/gain ratio (F/G) of the test geese (0.05 < p < 0.10). Moreover, the dietary inclusion of 12 and 24% ramie powder improved meat qualities by reducing the L* value (p < 0.05) and cooking loss (0.05 < p < 0.10) of thigh muscle. Compared with the control group, the ramie powder supplementation at different levels increased the serum activities of glutathione peroxidase and glutathione, promoting the antioxidative capacity of the body (0.05 < p < 0.10). This study demonstrates that moderate ramie powder is beneficial to the production performance of Yanling white geese and has the potential to be used as a poultry feed ingredient. In conclusion, 12% was the proper supplementation rate of ramie powder in Yanling white geese feed.

A novel anoikis-related gene signature predicts prognosis in patients with head and neck squamous cell carcinoma and reveals immune infiltration.

Background: Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive disease with a poor prognosis for advanced tumors. Anoikis play a key role in cancer metastasis, facilitating the detachment and survival of cancer cells from the primary tumor site. However, few studies have focused on the role of anoikis in HNSC, especially on the prognosis. Methods: Anoikis-related genes (ANRGs) integrated from Genecards and Harmonizome portals were used to identify HNSCC subtypes and to construct a prognostic model for HNSCC patients. Also, we explored the immune microenvironment and enrichment pathways between different subtypes. Finally, we provide clinical experts with a novel nomogram based on ANRGs, with DCA curves indicating the potential clinical benefit of the model for clinical strategies. Results: We identified 69 survival-related HNSCC anoikis-related DEGs, from which 7 genes were selected to construct prognostic models. The prognostic risk score was identified as an independent prognostic factor. Functional analysis showed that these high and low risk groups had different immune status and drug sensitivity. Next risk scores were combined with HNSCC clinicopathological features together to construct a nomogram, and DCA analysis showed that the model could benefit patients from clinical treatment strategies. Conclusion: The predictive seven-gene signature and nomogram established in this study can assist clinicians in selecting personalized treatment for patients with HNSCC.

SLC22A8: An indicator for tumor immune microenvironment and prognosis of ccRCC from a comprehensive analysis of bioinformatics.

Clear cell renal cell carcinoma (ccRCC) is one of the most common renal malignancies worldwide. SLC22A8 plays a key role in renal excretion of organic anions. However, its role in ccRCC remains unclear; therefore, this study aimed to elucidate the relationship between SLC22A8 and ccRCC. The The Cancer Genome Atlas-kidney renal clear cell carcinoma cohort was included in this study. The Wilcoxon signed-rank test and logistic regression were used to analyze the relationship between SLC22A8 expression and clinicopathological characteristics. Multifactorial analysis and Kaplan-Meier survival curves were adopted for correlation between SLC22A8 expression and clinicopathological parameters and overall survival. Utilizing the UALCAN database, the correlation of the expression levels of SLC22A8 DNA methylation in ccRCC was explored. Immunological characterization of SLC22A8 regarding the ccRCC tumor microenvironment was carried out by the single sample Gene Set Enrichment Analysis algorithm and the CIBERSORT algorithm. With the CellMiner database, the analysis of the association between SLC22A8 gene expression and drug sensitivity was further performed. Eventually, gene ontology and Kyoto Encyclopedia of Gene and Genome enrichment analyses were applied to identify the functional and signaling pathways involved in SLC22A8. SLC22A8 expression is associated with age, grade, stage, and tumor status. SLC22A8 protein expression levels, phosphorylated protein levels, and DNA methylation expression levels were lower in ccRCC tissues than in normal tissues, and low methylation levels predicted poor overall survival. Comprehensive analysis of tumor immune infiltration and the tumor microenvironment indicated a higher level of overall immunity in the SLC22A8 low expression group. Gene Enrichment Analysis results showed that low expression of SLC22A8 was associated with immune pathways, such as phagocytosis recognition and humoral immune response. SLC22A8 expression was significantly correlated with survival and immune infiltration in ccRCC and can be used as a prognostic biomarker for ccRCC.

Gamma knife radiosurgery versus transcatheter arterial chemoembolization for hepatocellular carcinoma with portal vein tumor thrombus: a propensity score matching study.

BACKGROUND: The optimal locoregional treatment for hepatocellular carcinoma (HCC) patients with portal vein tumor thrombus (PVTT) is unclear. This study aimed to investigate the efficacy of Gamma knife radiosurgery (GKR) versus transcatheter arterial chemoembolization (TACE) in HCC patients with PVTT. METHODS: This retrospective study included 544 HCC patients with PVTT (GKR, 202; TACE, 342). Propensity score matching (PSM) analysis identified 171 matched pairs of patients. The primary endpoint was overall survival (OS). RESULTS: Before PSM, the GKR group exhibited longer median OS (mOS) than the TACE group (17.2 vs. 8.0 months, p < 0.001). We followed the Cheng's classification for PVTT. In the subgroup analysis, GKR was associated with significantly longer mOS for patients with PVTT II-IV (17.5 vs. 8.7 months, p < 0.001; 17.2 vs. 7.8 months, p = 0.001; 14.5 vs. 6.5 months, p = 0.001, respectively) and comparable OS for patients with PVTT I. After PSM, the GKR group had also a longer mOS than the TACE group (15.8 vs. 10.4 months, p < 0.001). In the subgroup analysis, the GKR group demonstrated superior mOS for patients with PVTT II-IV (all p < 0.05) and comparable OS for patients with PVTT I. CONCLUSIONS: GKR was associated better OS than TACE in HCC patients with PVTT, especially for patients with PVTT II-IV. CLINICAL TRIALS REGISTRATION: The study was registered in the Chinese Clinical Trials Registry under the registration number ChiCTR2100051057.

Evaluating DNA Methylation, Gene Expression, Somatic Mutation, and Their Combinations in Inferring Tumor Tissue-of-Origin.

Carcinoma of unknown primary (CUP) is a type of metastatic cancer, the primary tumor site of which cannot be identified. CUP occupies approximately 5% of cancer incidences in the United States with usually unfavorable prognosis, making it a big threat to public health. Traditional methods to identify the tissue-of-origin (TOO) of CUP like immunohistochemistry can only deal with around 20% CUP patients. In recent years, more and more studies suggest that it is promising to solve the problem by integrating machine learning techniques with big biomedical data involving multiple types of biomarkers including epigenetic, genetic, and gene expression profiles, such as DNA methylation. Different biomarkers play different roles in cancer research; for example, genomic mutations in a patient's tumor could lead to specific anticancer drugs for treatment; DNA methylation and copy number variation could reveal tumor tissue of origin and molecular classification. However, there is no systematic comparison on which biomarker is better at identifying the cancer type and site of origin. In addition, it might also be possible to further improve the inference accuracy by integrating multiple types of biomarkers. In this study, we used primary tumor data rather than metastatic tumor data. Although the use of primary tumors may lead to some biases in our classification model, their tumor-of-origins are known. In addition, previous studies have suggested that the CUP prediction model built from primary tumors could efficiently predict TOO of metastatic cancers (Lal et al., 2013; Brachtel et al., 2016). We systematically compared the performances of three types of biomarkers including DNA methylation, gene expression profile, and somatic mutation as well as their combinations in inferring the TOO of CUP patients. First, we downloaded the gene expression profile, somatic mutation and DNA methylation data of 7,224 tumor samples across 21 common cancer types from the cancer genome atlas (TCGA) and generated seven different feature matrices through various combinations. Second, we performed feature selection by the Pearson correlation method. The selected features for each matrix were used to build up an XGBoost multi-label classification model to infer cancer TOO, an algorithm proven to be effective in a few previous studies. The performance of each biomarker and combination was compared by the 10-fold cross-validation process. Our results showed that the TOO tracing accuracy using gene expression profile was the highest, followed by DNA methylation, while somatic mutation performed the worst. Meanwhile, we found that simply combining multiple biomarkers does not have much effect in improving prediction accuracy.

Adaptive Kernel Value Caching for SVM Training.

Support vector machines (SVMs) can solve structured multioutput learning problems such as multilabel classification, multiclass classification, and vector regression. SVM training is expensive, especially for large and high-dimensional data sets. The bottleneck of the SVM training often lies in the kernel value computation. In many real-world problems, the same kernel values are used in many iterations during the training, which makes the caching of kernel values potentially useful. The majority of the existing studies simply adopt the least recently used (LRU) replacement strategy for caching kernel values. However, as we analyze in this article, the LRU strategy generally achieves high hit ratio near the final stage of the training but does not work well in the whole training process. Therefore, we propose a new caching strategy called EFU (less frequently used), which replaces the EFU kernel values that enhance least frequently used (LFU). Our experimental results show that EFU often has 20% higher hit ratio than LRU in the training with the Gaussian kernel. To further optimize the strategy, we propose a caching strategy called hybrid caching for the SVM training (HCST), which has a novel mechanism to automatically adapt the better caching strategy in different stages of the training. We have integrated the caching strategy into ThunderSVM, a recent SVM library on many-core processors. Our experiments show that HCST adaptively achieves high hit ratios with little runtime overhead among different problems including multilabel classification, multiclass classification, and regression problems. Compared with other existing caching strategies, HCST achieves 20% more reduction in training time on average.

A machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation.

Carcinoma of unknown primary (CUP), defined as metastatic cancers with unknown cancer origin, occurs in 3-5 per 100 cancer patients in the United States. Heterogeneity and metastasis of cancer brings great difficulties to the follow-up diagnosis and treatment for CUP. To find the tissue-of-origin (TOO) of the CUP, multiple methods have been raised. However, the accuracies for computed tomography (CT) and positron emission tomography (PET) to identify TOO were 20%-27% and 24%-40% respectively, which were not enough for determining targeted therapies. In this study, we provide a machine learning framework to trace tumor tissue origin by using gene length-normalized somatic mutation sequencing data. Somatic mutation data was downloaded from the Data Portal (Release 28) of the International Cancer Genome Consortium (ICGC), and 4909 samples for 13 cancers was used to identify primary site of cancers. Optimal results were obtained based on a 600-gene set by using the random forest algorithm with 10-fold cross-validation, and the average accuracy and F1-score were 0.8822 and 0.8886 respectively across 13 types of cancer. In conclusion, we provide an effective computational framework to infer cancer tissue-of-origin by combining DNA sequencing and machine learning techniques, which is promising in assisting clinical diagnosis of cancers.

Generalized Alternating Direction Method of Multipliers: New Theoretical Insights and Applications.

Recently, the alternating direction method of multipliers (ADMM) has received intensive attention from a broad spectrum of areas. The generalized ADMM (GADMM) proposed by Eckstein and Bertsekas is an efficient and simple acceleration scheme of ADMM. In this paper, we take a deeper look at the linearized version of GADMM where one of its subproblems is approximated by a linearization strategy. This linearized version is particularly efficient for a number of applications arising from different areas. Theoretically, we show the worst-case 𝒪(1/k) convergence rate measured by the iteration complexity (k represents the iteration counter) in both the ergodic and a nonergodic senses for the linearized version of GADMM. Numerically, we demonstrate the efficiency of this linearized version of GADMM by some rather new and core applications in statistical learning. Code packages in Matlab for these applications are also developed.