Background: To develop an effective model of predicting fatal outcomes in the severe coronavirus disease 2019 (COVID-19) patients. Methods: Between February 20, 2020 and April 4, 2020, consecutive confirmed 2541 COVID-19 patients from three designated hospitals were enrolled in this study. All patients received chest computed tomography (CT) and serological examinations at admission. Laboratory tests included routine blood tests, liver function, renal function, coagulation profile, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and arterial blood gas. The SaO ₂ was measured using pulse oxygen saturation in room air at resting status. Independent high-risk factors associated with death were analyzed using Cox proportional hazard model. A prognostic nomogram was constructed to predict the survival of severe COVID-19 patients. Results: There were 124 severe patients in the training cohort, and there were 71 and 76 severe patients in the two independent validation cohorts, respectively. Multivariate Cox analysis indicated that age ≥ 70 years ( HR = 1.184, 95% CI 1.061–1.321), panting (breathing rate ≥ 30/min) ( HR = 3.300, 95% CI 2.509–6.286), lymphocyte count < 1.0 × 10 ⁹ /L ( HR = 2.283, 95% CI 1.779–3.267), and interleukin-6 (IL-6) > 10 pg/ml ( HR = 3.029, 95% CI 1.567–7.116) were independent high-risk factors associated with fatal outcome. We developed the nomogram for identifying survival of severe COVID-19 patients in the training cohort (AUC = 0.900, 95% CI 0.841–0.960, sensitivity 95.5%, specificity 77.5%); in validation cohort 1 (AUC = 0.811, 95% CI 0.763–0.961, sensitivity 77.3%, specificity 73.5%); in validation cohort 2 (AUC = 0.862, 95% CI 0.698–0.924, sensitivity 92.9%, specificity 64.5%). The calibration curve for probability of death indicated a good consistence between prediction by the nomogram and the actual observation. The prognosis of severe COVID-19 patients with high levels of IL-6 receiving tocilizumab were better than that of those patients without tocilizumab both in the training and validation cohorts, but without difference ( P = 0.105 for training cohort, P = 0.133 for validation cohort 1, and P = 0.210 for validation cohort 2). Conclusions: This nomogram could help clinicians to identify severe patients who have high risk of death, and to develop more appropriate treatment strategies to reduce the mortality of severe patients. Tocilizumab may improve the prognosis of severe COVID-19 patients with high levels of IL-6.

Background: Integration of HBV DNA into the human genome could progressively contribute to hepatocarcinogenesis. Both intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular-cholangiocarcinoma (CHC) are known to be associated with HBV infection. However, the integration of HBV and mechanism of HBV-induced carcinogenesis in ICC and CHC remains unclear. Methods: 41 patients with ICC and 20 patients with CHC were recruited in the study. We conducted HIVID analysis on these 61 samples to identify HBV integration sites in both the tumor tissues and adjacent non-tumor liver tissues. To further explore the effect of HBV integration on gene alteration, we selected paired tumors and adjacent non-tumor liver tissues from 3 ICC and 4 CHC patients for RNA-seq and WGS. Results: We detected 493 HBV integration sites in ICC patients, of which 417 were from tumor samples and 76 were from non-tumor samples. And 246 HBV integration sites were detected in CHC patients, of which 156 were located in the genome of tumor samples and 90 were in non-tumor samples. Recurrent HBV integration events were detected in ICC including TERT, ZMAT4, MET, ANKFN1, PLXNB2, and in CHC like TERT, ALKBH5. Together with our established data of HBV-infected hepatocellular carcinoma, we found that HBV preferentially integrates into the specific regions which may affect the gene expression and regulation in cells and involved in carcinogenesis. We further performed genomic and transcriptomic sequencing of three ICC and four CHC patients, and found that HBV fragments could integrate near some important oncogene like TERT, causing large-scale genome variations on nearby genomic sequences, and at the same time changing the expression level of the oncogenes. Conclusion: Comparative analysis demonstrates numerous newly discovered mutational events in ICC and CHC resulting from HBV insertions in the host genome. Our study provides an in-depth biological and clinical insights into HBV-induced ICC and CHC.

Virus integration in the host genome is frequently reported to be closely associated with many human diseases, and the detection of virus integration is a critically challenging task. However, most existing tools show limited specificity and sensitivity. Therefore, the objective of this study is to develop a method for accurate detection of virus integration into host genomes. Herein, we report a novel method termed HIVID2 that is a significant upgrade of HIVID. HIVID2 performs a paired-end combination (PE-combination) for potentially integrated reads. The resulting sequences are then remapped onto the reference genomes, and both split and discordant chimeric reads are used to identify accurate integration breakpoints with high confidence. HIVID2 represents a great improvement in specificity and sensitivity, and predicts breakpoints closer to the real integrations, compared with existing methods. The advantage of our method was demonstrated using both simulated and real datasets. HIVID2 uncovered novel integration breakpoints in well-known cervical cancer-related genes, including FHIT and LRP1B, which was verified using protein expression data. In addition, HIVID2 allows the user to decide whether to automatically perform advanced analysis using the identified virus integrations. By analyzing the simulated data and real data tests, we demonstrated that HIVID2 is not only more accurate than HIVID but also better than other existing programs with respect to both sensitivity and specificity. We believe that HIVID2 will help in enhancing future research associated with virus integration. HIVID2 can be accessed at https://github.com/zengxi-hada/HIVID2/. Supplementary data are available at Bioinformatics online.

Background: The lack of effective biomarkers for the treatment of postoperative recurrence in hepatocellular carcinoma (HCC) persists despite lenvatinib therapy. This study aims to identify beta-actin (ACTB) as a predictive biomarker for lenvatinib that can facilitate individualized treatment for HCC. Methods: This retrospective study included a subset of patients with HCC who underwent partial hepatectomy, with some receiving postoperative lenvatinib treatment and others not receiving lenvatinib treatment. A propensity score matching (PSM) analysis of patients who underwent treatment with or without lenvatinib following HCC partial hepatectomy was performed. Immunohistochemistry was employed to determine the levels of ACTB expression in HCC samples obtained from matched patients (n=225) enrolled in this study. The X-Tile was employed to determine the optimal cut-off point of ACTB levels for predicting time to recurrence (TTR). To assess the correlation between ACTB levels and lenvatinib efficacy, a subgroup analysis of TTR was conducted. A Cox regression model with an interaction term was utilized to assess the predictive significance of the model. Subsequently, a nomogram was developed and its discriminative ability and predictive accuracy were assessed using the concordance index (C-index) and calibration curve. For the investigation of the ACTB expression, HCC and para-tumoral normal tissues were employed. The patient-derived xenograft (PDX) model was utilized to validate the correlation between ACTB levels and lenvatinib responsiveness. Results: After PSM, a total of 76 patients who underwent postoperative lenvatinib treatment were included in the analysis, with a median TTR of 24.35 months. Early-stage HCC patients with lower levels of ACTB exhibited a more favorable response to lenvatinib therapy compared to those with higher levels. The reduced expression of ACTB was indicative of the benefits of lenvatinib, as opposed to higher levels {hazard ratio (HR) =0.243 [95% confidence interval (CI): 0.096–0.619], P<0.001, P value for interaction =0.014}. In approximately 81.8% of cases involving HCC patients, there was an observed increase in the expression of ACTB. Multivariate analysis of the lenvatinib cohort revealed Child-Pugh [HR =5.416 (95% CI: 1.390–21.104), P=0.015], Barcelona Clinic Liver Cancer (BCLC) stage [HR =2.508 (95% CI: 1.116–5.639), P=0.026], and ACTB [HR =5.879 (95% CI: 2.424–14.259), P<0.001] score as independent factors for TTR, and all were included in the nomogram. The survival probability based on the calibration curve showed that the prediction of the nomogram was in good agreement with the actual observation. The C-index of the nomogram for predicting survival was 0.76 (95% CI: 0.71–0.84). Moreover, the PDXs derived from tumors exhibiting low levels of ACTB expression demonstrated a heightened sensitivity to lenvatinib treatment. Conclusions: In patients with tumors treated with lenvatinib, low ACTB expression can predict a lower risk of recurrence. The validation of this potential biomarker in independent cohorts is necessary prior to its implementation for precision treatment stratification in patients undergoing partial hepatectomy for early-stage HCC.

Background:Integration of HBV DNA into the human genome could progressively contributes to hepatocarcinogenesis. Both Intrahepatic cholangiocarcinoma (ICC) and Combined hepatocellular-cholangiocarcinoma (CHC) are known to be associated with HBV infection. However, the integration of HBV and mechanism of HBV-induced carcinogenesis in ICC and CHC remains unclear.Methods:41 patients with ICC and 20 patients with CHC were recruited in the study. We conducted HIVID analysis on these 61 samples to identify HBV integration sites in both the tumor tissues and adjacent non-tumor liver tissues. To further explore the effect of HBV integration on gene expression, we selected paired tumors and adjacent non-tumor liver tissues from 3 ICC and 4 CHC patients for RNA-seq and WGS.Results:We detected 493 HBV integration sites in ICC patients, of which 417 were from tumor samples, 76 were from non-tumor samples. And 246 HBV integration sites were detected in CHC patients, of which 156 were located in the genome of tumor samples and 90 were in non-tumor samples. Recurrent HBV integration events were detected in ICC including TERT, ZMAT4, MET, ANKFN1, PLXNB2, and in CHC like TERT, ALKBH5. Together with our established data of HBV-infected hepatocellular carcinoma, we found HBV preferentially integrates into the specific regions which may affect the gene expression and regulation in cells and induce carcinogenesis. We further performed genomic and transcriptomic sequencing of three ICC and four CHC patients and found that HBV fragments could integrate near some important oncogene like TERT, causing large-scale genome variations on nearby genomic sequences, and at the same time changing the expression level of the oncogene genes.Conclusion:Comparative analysis demonstrates numerous newly discovered mutational events in ICC and CHC resulting from HBV insertions in the host genome. Our study provides an in-depth biological and clinical insights into HBV-induced ICC and CHC.