Letter to the editor: Suspected discrepancies due to inadequate quality of an in vitro analysis revealed by an in silico analysis.

The author's in vitro analysis results were compared with those of an in silico structure analysis of the relevant sequences obtained from the author's entire genome sequence data. It was speculated that the in silico results together with author's phylogenetic results demonstrate problems in the authors' published in vitro data, which were presumably due to an inadequate accuracy of an in vitro assay. It was fortuitously suggested that alignment images to an excess repeat structure of the same locus provide a improved speculation of a number of repeats and that accurate in vitro assays are expected to complementarily provide reliable insights in the era of whole genome sequencing.

Development of a transformer model for predicting the prognosis of patients with hepatocellular carcinoma after radiofrequency ablation.

INTRODUCTION: Radiofrequency ablation (RFA) is a widely accepted, minimally invasive treatment modality for patients with hepatocellular carcinoma (HCC). Accurate prognosis prediction is important to identify patients at high risk for cancer progression/recurrence after RFA. Recently, state-of-the-art transformer models showing improved performance over existing deep learning-based models have been developed in several fields. This study was aimed at developing and validating a transformer model to predict the overall survival in HCC patients with treated by RFA. METHODS: We enrolled a total of 1778 treatment-naïve HCC patients treated by RFA as the first-line treatment. We developed a transformer-based machine learning model to predict the overall survival in the HCC patients treated by RFA and compared its predictive performance with that of a deep learning-based model. Model performance was evaluated by determining the Harrel's c-index and validated externally by the split-sample method. RESULTS: The Harrel's c-index of the transformer-based model was 0.69, indicating its better discrimination performance than that of the deep learning model (Harrel's c-index, 0.60) in the external validation cohort. The transformer model showed a high discriminative ability for stratifying the external validation cohort into two or three different risk groups (p < 0.001 for both risk groupings). The model also enabled output of a personalized cumulative recurrence prediction curve for each patient. CONCLUSIONS: We developed a novel transformer model for personalized prediction of the overall survival in HCC patients after RFA treatment. The current model may offer a personalized survival prediction schema for patients with HCC undergoing RFA treatment.

Clinical utility of postablation liver tumor biopsy and possibility of gene mutation analysis.

AIM: Radiofrequency ablation (RFA) is regarded as a first-line treatment for hepatocellular carcinoma (HCC) at an early stage. When treated with RFA, tumor biopsy may not be performed due to the risk of neoplastic seeding. We previously revealed that the risk of neoplastic seeding is significantly reduced by performing biopsies after RFA. In this study, we investigated the possibility of pathological evaluation and gene mutation analysis of post-RFA tumor specimens. METHODS: Radiofrequency ablation was undertaken on diethylnitrosamine-induced mouse liver tumor, and tumor samples with or without RFA were subjected to whole exome sequencing. Post-RFA human liver tumor specimens were used for detection of TERT promoter mutations and pathological assessment. RESULTS: The average somatic mutation rate, sites of mutation, and small indels and base transition patterns were comparable between the nontreated and post-RFA tumors. We identified 684 sites of nonsynonymous somatic substitutions in the nontreated tumor and 704 sites of nonsynonymous somatic substitutions in the post-RFA tumor, with approximately 85% in common. In the human post-RFA samples, the TERT promoter mutations were successfully detected in 40% of the cases. Pathological evaluation was possible with post-RFA specimens, and in one case, the diagnosis of adenocarcinoma was made. CONCLUSION: Our findings suggest that post-RFA liver tumor biopsy is a useful and safe method for obtaining tumor samples that can be used for gene mutation analysis and for pathological assessment.

Steroid administration for ischemic complications after radiofrequency ablation: A retrospective study.

AIM: The aim of this study was to evaluate the effects of steroids on ischemic complications after radiofrequency ablation. METHODS: A total of 58 patients with ischemic complications were divided into two groups according to corticosteroid use or non-use. RESULTS: A total of 13 patients who were administered steroids had a shorter duration of fever than those who were not administered steroids (median 6.0 vs. 2.0 days; p < 0.001). Linear regression analysis showed that steroid administration was associated with a reduction of 3.9 days in the duration of fever (p = 0.008). CONCLUSIONS: Steroid administration for ischemic complications after radiofrequency ablation may reduce the risk of fatal outcomes by blocking systemic inflammatory reactions.

Improved prognosis of hepatitis C-related hepatocellular carcinoma in the era of direct-acting antivirals.

The prognostic impact of direct-acting antivirals (DAAs) on patients with hepatitis C-related hepatocellular carcinoma (C-HCC) is still unclear. This study aimed to evaluate the prognosis of C-HCC in the DAA era. We enrolled 1237 consecutive patients with treatment-naive C-HCC who underwent radical radiofrequency ablation between 1999 and 2019. We also enrolled 350 patients with nonviral HCC as controls. We divided these patients into three groups according to the year of initial treatment: 1999-2005 (cohort 1), 2006-2013 (cohort 2), and 2014-2019 (cohort 3). The use of antiviral agents and their effect in patients with C-HCC was investigated. Overall survival was evaluated for each cohort using the Kaplan-Meier method and a multivariable Cox proportional hazards regression model. Sustained virologic response (SVR) was achieved in 52 (10%), 157 (26%), and 102 (74%) patients with C-HCC in cohorts 1-3, respectively. The 3- and 5-year survival rates of patients with C-HCC were 82% and 59% in cohort 1; 80% and 64% in cohort 2; and 86% and 78% in cohort 3, respectively (p = 0.003). Multivariable analysis adjusted for age, liver function, and tumor extension showed that the prognosis of C-HCC improved in cohort 3 compared to cohort 1 (adjusted hazard ratio [aHR], 0.49; 95% confidence interval [CI], 0.32-0.73; p < 0.001), whereas the prognosis of nonviral HCC did not improve significantly (aHR, 0.96; 95% CI, 0.59-1.57; p = 0.88). The prognosis of C-HCC drastically improved with the advent of DAAs.

Machine Learning-Based Personalized Prediction of Hepatocellular Carcinoma Recurrence After Radiofrequency Ablation.

Background and Aims: Radiofrequency ablation (RFA) is a widely accepted, minimally invasive treatment for hepatocellular carcinoma (HCC). This study aimed to develop a machine learning (ML) model to predict the risk of HCC recurrence after RFA treatment for individual patients. Methods: We included a total of 1778 patients with treatment-naïve HCC who underwent RFA. The cumulative probability of overall recurrence after the initial RFA treatment was 78.9% and 88.0% at 5 and 10 years, respectively. We developed a conventional Cox proportional hazard model and 6 ML models-including the deep learning-based DeepSurv model. Model performance was evaluated using Harrel's c-index and was validated externally using the split-sample method. Results: The gradient boosting decision tree (GBDT) model achieved the best performance with a c-index of 0.67 from external validation, and it showed a high discriminative ability in stratifying the external validation sample into 2, 3, and 4 different risk groups (P < .001 among all risk groups). The c-index of DeepSurv was 0.64. In order of significance, the tumor number, serum albumin level, and des-gamma-carboxyprothrombin level were the most important variables for the prediction of HCC recurrence in the GBDT model. Also, the current GBDT model enabled the output of a personalized cumulative recurrence prediction curve for each patient. Conclusion: We developed a novel ML model for the personalized risk prediction of HCC recurrence after RFA treatment. The current model may lead to the personalization of effective follow-up strategies after RFA treatment according to the risk stratification of HCC recurrence.

Clinical study of modulated electro-hyperthermia for advanced metastatic breast cancer.

Modulated electro-hyperthermia (mEHT) is a new treatment modality developed to overcome the problems associated with traditional hyperthermia; mEHT uses a precise impedance-matched system and modulated radiofrequency current flow to malignant tumors. It selects the malignant cells based on their biophysical differences, due to their high metabolic rate, individual (autonomic) behavior and membrane status. The aim of the present study was to report the outcomes of mEHT in the treatment of advanced breast cancer. mEHT was examined in 10 patients with advanced metastatic breast cancer and recurrent disease, who were considered incurable by standard therapy protocols. Of the 10 patients, partial response was achieved in 3, disease stability in 3, and progressive disease in 4; however, their quality of life was improved based on their subjective reports. No adverse effects were observed in any of the 10 patients. The present study demonstrated the feasibility of mEHT as a possible therapy for advanced breast cancer cases when standard therapies fail. Moreover, mEHT had no side effects and may be combined with various treatments for long-term therapy.

Mediastinoscopic salvage esophagectomy for recurrent esophageal squamous cell carcinoma after definitive chemoradiotherapy in a previously pneumonectomized patient.

We herein report a case of mediastinoscopic salvage esophagectomy for recurrent esophageal squamous cell carcinoma after definitive chemoradiotherapy in a previously pneumonectomized patient. A 66-year-old man with a medical history of left-sided pneumonectomy for lung cancer was diagnosed with local recurrence of lower esophageal squamous cell carcinoma (cT3N0M0 cStage II) 9 years after definitive chemoradiotherapy. The mediastinoscopic cervical approach and laparoscopic transhiatal approach were combined, and the thoracic esophagus was safely mobilized to separate the esophagus from the stump of the left bronchus and to divide dense adhesions between the esophagus and fibrotic tissue at the site of the previous left mediastinal pleural resection. The esophagectomy was uneventful and followed by reconstruction with a gastric conduit via the retrosternal route. The pathological diagnosis was esophageal squamous cell carcinoma (pT3-AD, pN1, M0, pStage III), indicating R0 resection. Even as salvage surgery, mediastinoscopic esophagectomy is a safe and curative treatment strategy for esophageal cancer patients who have previously undergone pneumonectomy.

Prognostic significance of KLF4 expression in gastric cancer.

To understand the roles of pluripotent stem cell-inducing genes in gastric cancer, the expression of Krüppel-like factor 4 (KLF4), Nanog, octamer-binding transcription factor 4 (Oct4), avian myelocytomatosis viral oncogene homolog (c-Myc) and sex-determining region Y-box 2 (SOX2) was examined using the newly developed gastric carcinoma tissue microarray. The associations between the immunohistochemical expression levels of the pluripotency-inducing factors and the clinicopathological data of 108 patients with gastric cancer were analyzed. No associations were identified between the expression levels of the five pluripotency-inducing factors and the tumor-node-metastasis (TNM) classification or clinicopathological characteristics of the patients. In addition, multivariate analysis revealed no association of Nanog, Oct4, SOX2 or c-Myc with the prognosis of the gastric cancer patients; however, low expression of KLF4 was determined to be an independent negative prognostic factor (P=0.0331), particularly in patients who underwent R0 resection (TNM stages 2 and 3; P=0.0048). In summary, low KLF4 expression was found to be negatively associated with overall survival, and may therefore be a useful prognostic marker in gastric cancer patients.

KLF4 and NANOG are prognostic biomarkers for triple-negative breast cancer.

BACKGROUND: Prognosis of breast cancer patients has been reported to depend on the expression of induced pluripotent stem (iPS) cell-inducing factors: KLF4 and NANOG. However, the relationship between KLF4 or NANOG expression in each breast cancer subtype and the life prognosis has not been elucidated. METHOD: KLF4 and NANOG expression levels were evaluated in 208 patients using a newly developed tissue microarray (TMA). In vitro, siRNA against klf4 (siKLF4) was transfected in TNBC cell line MDA-MB-231, and the expression of KLF4 was inhibited. RESULTS: Triple-negative breast cancer (TNBC) patients in KLF4 high-expression (upper) group had more favorable overall survival (OS) and disease-free survival (DFS) rates than KLF4 lower group (p = 0.0453 and p = 0.0427). In contrast, patients in the NANOG upper group had significantly poorer prognosis than lower group in TNBC breast cancer subtypes (p < 0.0001). Multivariate analysis showed that KLF4 (p = 0.0313), NANOG (p = 0.0002), and TNM stage (p = 0.0001) are mutually independent prognostic factors. It was also shown that the proliferation and invasion ability of siKLF4-induced TNBC cells were up-regulated significantly. CONCLUSION: Our findings suggested that KLF4 and NANOG expression levels were favorable prognostic factors for TNBC patients. KLF4 also had an ability to inhibit the proliferation and invasion of TNBC.