Prediction of chronic kidney disease after orthotopic liver transplantation: development and validation of a nomogram model

Background We aimed to develop and validate a nomogram model for predicting CKD after orthotopic liver transplantation (OLT). Methods The retrospective data of 399 patients who underwent transplantation and were followed in our centre were collected. They were randomly assigned to the training set (n = 293) and validation set (n = 106). Multivariable Cox regression analysis was performed in the training set to identify predictors of CKD. According to the Cox regression analysis results, a nomogram model was developed and validated. The renal function of recipients was monitored, and the long-term survival prognosis was assessed. Results The incidence of CKD at 5 years after OLT was 25.6%. Cox regression analysis identified several predictors of post-OLT CKD, including recipient age at surgery (HR 1.036, 95% CI 1.006-1.068; p = 0.018), female sex (HR 2.867, 95% CI 1.709-4.810; p < 0.001), preoperative hypertension (HR 1.670, 95% CI 0.962-2.898; p = 0.068), preoperative eGFR (HR 0.996, 95% CI 0.991-1.001; p = 0.143), uric acid at 3 months (HR 1.002, 95% CI 1.001-1.004; p = 0.028), haemoglobin at 3 months (HR 0.970, 95% CI 0.956-0.983; p < 0.001), and average concentration of cyclosporine A at 3 months (HR 1.002, 95% CI 1.001-1.003; p < 0.001). According to these parameters, a nomogram model for predicting CKD after OLT was constructed and validated. The C-indices were 0.75 and 0.80 in the training and validation sets. The calibration curve of the nomogram showed that the CKD probabilities predicted by the nomogram agreed with the observed probabilities at 1, 3, and 5 years after OLT (p > 0.05). Renal function declined slowly year by year, and there were significant differences between patients divided by these predictors. Kaplan-Meier survival analysis showed that the survival prognosis of recipients decreased significantly with the progression of renal function. Conclusions With excellent predictive abilities, the nomogram may be a simple and reliable tool to identify patients at high risk for CKD and poor long-term prognosis after OLT.

Network Pharmacology Prediction: The Possible Mechanisms of Cinobufotalin against Osteosarcoma

Objective. To explore the active compounds and targets of cinobufotalin (huachansu) compared with the osteosarcoma genes to obtain the potential therapeutic targets and pharmacological mechanisms of action of cinobufotalin on osteosarcoma through network pharmacology. Methods. The composition of cinobufotalin was searched by literature retrieval, and the target was selected from the CTD and TCMSP databases. The osteosarcoma genes, found from the GeneCards, OMIM, and other databases, were compared with the cinobufotalin targets to obtain potential therapeutic targets. The protein-protein interaction (PPI) network of potential therapeutic targets, constructed through the STRING database, was inputted into Cytoscape software to calculate the hub genes, using the NetworkAnalyzer. The hub genes were inputted into the Kaplan-Meier Plotter online database for exploring the survival curve. Functional enrichment analysis was identified using the DAVID database. Results. 28 main active compounds of cinobufotalin were explored, including bufalin, adenosine, oleic acid, and cinobufagin. 128 potential therapeutic targets on osteosarcoma are confirmed among 184 therapeutic targets form cinobufotalin. The hub genes included TP53, ACTB, AKT1, MYC, CASP3, JUN, TNF, VEGFA, HSP90AA1, and STAT3. Among the hub genes, TP53, ACTB, MYC, TNF, VEGFA, and STAT3 affect the patient survival prognosis of sarcoma. Through function enrichment analysis, it is found that the main mechanisms of cinobufotalin on osteosarcoma include promoting sarcoma apoptosis, regulating the cell cycle, and inhibiting proliferation and differentiation. Conclusion. The possible mechanisms of cinobufotalin against osteosarcoma are preliminarily predicted through network pharmacology, and further experiments are needed to prove these predictions.

An 11-Gene Signature Based on Treatment Responsiveness Predicts Radiation Therapy Survival Benefit Among Breast Cancer Patients

PurposeWe developed a strategy of building prognosis gene signature based on clinical treatment responsiveness to predict radiotherapy survival benefit in breast cancer patients.Methods and MaterialsAnalyzed data came from the public database. PFS was used as an indicator of clinical treatment responsiveness. WGCNA was used to identify the most relevant modules to radiotherapy response. Based on the module genes, Cox regression model was used to build survival prognosis signature to distinguish the benefit group of radiotherapy. An external validation was also performed.ResultsIn the developed dataset, MEbrown module with 534 genes was identified by WGCNA, which was most correlated to the radiotherapy response of patients. A number of 11 hub genes were selected to build the survival prognosis signature. Patients that were divided into radio-sensitivity group and radio-resistant group based on the signature risk score had varied survival benefit. In developed dataset, the 3-, 5-, and 10-year AUC of the signature were 0.814 (CI95%: 0.742–0.905), 0.781 (CI95%: 0.682–0.880), and 0.762 (CI95%: 0.626–0.897), respectively. In validation dataset, the 3- and 5-year AUC of the signature were 0.706 (CI95%: 0.523–0.889) and 0.743 (CI95%: 0.595–0.891). The signature had higher predictive power than clinical factors alone and had more clinical prognosis efficiency. Functional enrichment analysis revealed that the identified genes were mainly enriched in immune-related processes. Further immune estimated analysis showed the difference in distribution of immune micro-environment between radio-sensitivity group and radio-resistant group.ConclusionsThe 11-gene signature may reflect differences in tumor immune micro-environment that underlie the differential response to radiation therapy and could guide clinical-decision making related to radiation in breast cancer patients.

Identification of Breast Cancer Immune Subtypes by Analyzing Bulk Tumor and Single Cell Transcriptomes

Background: The histological and molecular classification of breast cancer (BC) is being used in the clinical management of this disease. However, subtyping of BC based on the tumor immune microenvironment (TIME) remains insufficiently explored, although such investigation may provide new insights into intratumor heterogeneity in BC and potential clinical implications for BC immunotherapy.Methods: Based on the enrichment scores of 28 immune cell types, we performed clustering analysis of transcriptomic data to identify immune-specific subtypes of BC using six different datasets, including five bulk tumor datasets and one single-cell dataset. We further analyzed the molecular and clinical features of these subtypes.Results: Consistently in the six datasets, we identified three BC subtypes: BC-ImH, BC-ImM, and BC-ImL, which had high, medium, and low immune signature scores, respectively. BC-ImH displayed a significantly better survival prognosis than BC-ImL. Triple-negative BC (TNBC) and human epidermal growth factor receptor-2-positive (HER2+) BC were likely to have the highest proportion in BC-ImH and the lowest proportion in BC-ImL. In contrast, hormone receptor-positive (HR+) BC had the highest proportion in BC-ImL and the lowest proportion in BC-ImH. Furthermore, BC-ImH had the highest tumor mutation burden (TMB) and predicted neoantigens, while BC-ImL had the highest somatic copy number alteration (SCNA) scores. It is consistent with that TMB and SCNA correlate positively and negatively with anti-tumor immune response, respectively. TP53 had the highest mutation rate in BC-ImH and the lowest mutation rate in BC-ImL, supporting that TP53 mutations promote anti-tumor immune response in BC. In contrast, PIK3CA displayed the highest mutation rate in BC-ImM, while GATA3 had the highest mutation rate in BC-ImL. Besides immune pathways, many oncogenic pathways were upregulated in BC-ImH, including ErbB, MAPK, VEGF, and Wnt signaling pathways; the activities of these pathways correlated positively with immune signature scores in BC.Conclusions: The tumors with the strong immune response (“hot” tumors) have better clinical outcomes than the tumors with the weak immune response (“cold” tumors) in BC. TNBC and HER2+ BC are more immunogenic, while HR + BC is less immunogenic. Certain HER2+ or HR + BC patients could be propitious to immunotherapy in addition to TNBC.

Aerial View of the Association Between m6A-Related LncRNAs and Clinicopathological Characteristics of Pancreatic Cancer

Pancreatic cancer is a highly malignant tumor with a poor survival prognosis. We attempted to establish a robust prognostic model to elucidate the clinicopathological association between lncRNA, which may lead to poor prognosis by influencing m6A modification, and pancreatic cancer. We investigated the lncRNAs expression level and the prognostic value in 440 PDAC patients and 171 normal tissues from GTEx, TCGA, and ICGC databases. The bioinformatic analysis and statistical analysis were used to illustrate the relationship. We implemented Pearson correlation analysis to explore the m6A-related lncRNAs, univariate Cox regression and Kaplan-Meier methods were performed to identify the seven prognostic lncRNAs signatures. We inputted them in the LASSO Cox regression to establish a prognostic model in the TCGA database, verified in the ICGC database. The AUC of the ROC curve of the training set is 0.887, while the validation set is 0.711. Each patient has calculated a risk score and divided it into low-risk and high-risk subgroups by the median value. Moreover, the model showed a robust prognostic ability in the stratification analysis of different risk subgroups, pathological grades, and recurrence events. We established a ceRNA network between lncRNAs and m6A regulators. Enrichment analysis indicated that malignancy-associated biological function and signaling pathways were enriched in the high-risk subgroup and m6A-related lncRNAs target mRNA. We have even identified small molecule drugs, such as Thapsigargin, Mepacrine, and Ellipticine, that may affect pancreatic cancer progression. We found that seven lncRNAs were highly expressed in tumor patients in the GTEx-TCGA database, and LncRNA CASC19/UCA1/LINC01094/LINC02323 were confirmed in both pancreatic cell lines and FISH relative quantity. We provided a comprehensive aerial view between m6A-related lncRNAs and pancreatic cancer’s clinicopathological characteristics, and performed experiments to verify the robustness of the prognostic model.

Survival Outcomes of Patients With Mediastinal Germ Cell Tumors: Experience of a Cancer Center in South America

PurposeMediastinal germ cell tumors (GCT) are rare neoplasms associated with poor survival prognosis. Due to their low incidence, limited information is available about this disease in South America. The objective of this study is to report the clinical characteristics and outcomes of patients with mediastinal GCT in a cancer center in Colombia.Materials and MethodsWe conducted a retrospective analysis of patients with mediastinal GCT treated at the National Cancer Institute at Bogota (Colombia) between 2008 and 2020. Survival curves were presented using the Kaplan–Meier method. Chi-square and Cox proportional hazard model tests were used for data analysis.ResultsSixty-one patients were included in the study. Of them, 60 were male and 51 (83.6%) of whom had non-seminomatous germ cell tumors (NSGCT). Twenty-nine patients (47.5%) presented with superior vena cava syndrome, and 18 (29.5%) patients had extrapulmonary metastatic involvement. The three-year overall survival (OS) of NSGCT patients was 26%. The 3-year OS of NSGCT patients who underwent surgical resection of residual mediastinal mass after chemotherapy was 59%. Non-surgical management after first-line chemotherapy was associated with a worse survival prognosis in NSGCT patients (p = 0.002). Ten patients with mediastinal seminomatous germ cell tumors (SCGT) achieved a 3-year OS of 100%.ConclusionMediastinal NSGCT had poor outcomes. Surgery of the residual mass after first-line chemotherapy seems to improve the outcome of NSGCT patients. Advanced disease at presentation may reflect inadequate access to reference cancer centers in Colombia and potentially explain poor survival outcomes in this cohort. On the other hand, mediastinal SCGT is a biologically different disease; most patients will achieve disease remission and long-term survival with first-line chemotherapy.

The Art of War: Ferroptosis and Pancreatic Cancer

Pancreatic cancer is a devastating gastrointestinal cancer, characterized by late diagnosis, low treatment success rate, and poor survival prognosis. The most common pathological type of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), which is mainly driven by the K-Ras oncogene. Ferroptosis was originally described as Ras-dependent cell death, but is now defined as lipid peroxidation-mediated regulated necrosis, accompanied by excessive activation of the autophagy degradation pathway and limited membrane repair capacity. The impaired ferroptotic pathway is involved in many types of cancer, including PDAC. On the one hand, the chronic inflammation caused by ferroptotic damage contributes to the formation of K-Ras-driven PDAC. On the other hand, drug-induced ferroptosis is an emerging strategy to suppress tumor growth in established PDAC. In this mini-review, we outline the core process of ferroptosis, discuss the regulatory mechanism of ferroptosis in PDAC, and highlight some of the challenges of targeting ferroptosis in PDAC therapy.

Quantified CIN Score From Cell-free DNA as a Novel Noninvasive Predictor of Survival in Patients With Spinal Metastasis

Purpose: Most currently available scores for survival prediction of patients with bone metastasis lack accuracy. In this study, we present a novel quantified CIN (Chromosome Instability) score modeled from cfDNA copy number variation (CNV) for survival prediction.Experimental Design: Plasma samples collected from 67 patients with bone metastases from 11 different cancer types between November 2015 and May 2016 were sent through low-coverage whole genome sequencing followed by CIN computation to make a correlation analysis between the CIN score and survival prognosis. The results were validated in an independent cohort of 213 patients.Results: During the median follow-up period of 598 (95% CI 364–832) days until December 25, 2018, 124 (44.3%) of the total 280 patients died. Analysis of the discovery dataset showed that CIN score = 12 was the optimal CIN cutoff. Validation dataset showed that CIN was elevated (score ≥12) in 87 (40.8%) patients, including 5 (5.75%) with head and neck cancer, 11 (12.6%) with liver and gallbladder cancer, 11 (12.6%) with cancer from unidentified sites, 21 (24.1%) with lung cancer, 7 (8.05%) with breast cancer, 4 (4.60%) with thyroid cancer, 6 (6.90%) with colorectal cancer, 4 (4.60%) with kidney cancer, 2 (2.30%) with prostate cancer, and 16 (18.4%) with other types of cancer. Further analysis showed that patients with elevated CIN were associated with worse survival (p &lt; 0.001). For patients with low Tokuhashi score (≤8) who had predictive survival of less than 6 months, the CIN score was able to distinguish patients with a median overall survival (OS) of 443 days (95% CI 301–585) from those with a median OS of 258 days (95% CI 184–332).Conclusion: CNV examination in bone metastatic cancer from cfDNA is superior to the traditional predictive model in that it provides a noninvasive and objective method of monitoring the survival of patients with spine metastasis.