Survival prediction optimization of acute myeloid leukaemia based on T-cell function-related genes and plasma proteins.

Interactions between acute myeloid leukaemia (AML) cells and immune cells are postulated to corelate with outcomes of AML patients. However, data on T-cell function-related signature are not included in current AML survival prognosis models. We examined data of RNA matrices from 1611 persons with AML extracted from public databases arrayed in a training and three validation cohorts. We developed an eight-gene T-cell function-related signature using the random survival forest variable hunting algorithm. Accuracy of gene identification was tested in a real-world cohort by quantifying cognate plasma protein concentrations. The model had robust prognostic accuracy in the training and validation cohorts with five-year areas under receiver-operator characteristic curve (AUROC) of 0.67-0.76. The signature was divided into high- and low-risk scores using an optimum cut-off value. Five-year survival in the high-risk groups was 6%-23% compared with 42%-58% in the low-risk groups in all the cohorts (all p values <0.001). In multivariable analyses, a high-risk score independently predicted briefer survival with hazard ratios of death in the range 1.28-2.59. Gene set enrichment analyses indicated significant enrichment for genes involved in immune suppression pathways in the high-risk groups. Accuracy of the gene signature was validated in a real-world cohort with 88 pretherapy plasma samples. In scRNA-seq analyses most genes in the signature were transcribed in leukaemia cells. Combining the gene expression signature with the 2017 European LeukemiaNet classification significantly increased survival prediction accuracy with a five-year AUROC of 0.82 compared with 0.76 (p < 0.001). Our T-cell function-related risk score complements current AML prognosis models.

Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome.

Myelodysplastic syndrome (MDS) is clonal disease featured by ineffective haematopoiesis and potential progression into acute myeloid leukaemia (AML). At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis for MDS patients based on the identified metabolic gene panel in training cohort, followed by external validation in an independent cohort. The patients with lower risk had better prognosis than patients with higher risk. The constructed model was verified as an independent prognostic factor for MDS patients with hazard ratios of 3.721 (1.814-7.630) and 2.047 (1.013-4.138) in the training cohort and validation cohort, respectively. The AUC of 3-year overall survival was 0.846 and 0.743 in the training cohort and validation cohort, respectively. The high-risk score was significantly related to other clinical prognostic characteristics, including higher bone marrow blast cells and lower absolute neutrophil count. Moreover, gene set enrichment analyses (GSEA) showed several significantly enriched pathways, with potential indication of the pathogenesis. In this study, we identified a novel stable metabolic panel, which might not only reveal the dysregulated metabolic microenvironment, but can be used to predict the prognosis of MDS.

Long noncoding RNAs and their epigenetic function in hematological diseases.

Recent discoveries demonstrate the importance of long noncoding RNA (lncRNA) in the regulation of multiple major processes impacting development, differentiation, and metastasis of hematological diseases through epigenetic mechanisms. In contrast to genetic changes, epigenetic modification does not modify genes but is frequently reversible, thus providing opportunities for targeted treatment using specific inhibitors. In this review, we will summarize the function and epigenetic mechanism of lncRNA in malignant hematologic diseases.

Cytidine triphosphate synthase 1-mediated metabolic reprogramming promotes proliferation and drug resistance in multiple myeloma.

Upregulation of metabolism-related gene cytidine triphosphate synthase 1 (CTPS1) is associated with poor prognosis in multiple myeloma (MM). However, its role in MM remains unclear. In this study, bioinformatics analysis revealed significant differences in CTPS1 expression levels among various plasma cell malignancies. The patients with high CTPS1 expression had poor overall survival, progression-free survival, and event-free survival. CTPS1 was significantly correlated with sex, albumin, ß2 microglobulin, lactate dehydrogenase, and advanced disease. In vitro experiments demonstrated that CTPS1-overexpressing (CTPS1-OE) cells proliferated faster than CTPS1-short hairpin RNA (CTPS1-sh) cells. NRG-SGM3 mice showed significantly accelerated tumor growth in the CTPS1-OE group. CTPS1-OE decreased sensitivity to bortezomib, whereas CTPS1-sh increased sensitivity to bortezomib in MM cell lines. Mechanistically, CTPS1 was primarily involved in metabolism processes. Additionally, CTPS1 was closely related to several co-expressed genes such as MYC and the bone marrow immune microenvironment. In conclusion, CTPS1 is a significant prognostic biomarker for patients with MM, suggesting a potential therapeutic target.

Intertwined Flexoelectricity and Stacking Ferroelectricity in Marginally Twisted hBN Moiré Superlattice.

Moiré superlattices in twisted van der Waals homo/heterostructures present a fascinating interplay between electronic and atomic structures, with potential applications in electronic and optoelectronic devices. Flexoelectricity, an electromechanical coupling between electric polarization and strain gradient, is intrinsic to these superlattices because of the lattice misfit strain at the atomic scale. However, due to its weak magnitude, the effect of flexoelectricity on moiré ferroelectricity has remained underexplored. Here, the role of flexoelectricity in shaping and modulating the moiré ferroelectric patterns in twisted hBN homojunction is unveiled. Enhanced flexoelectric effects induce unique stacking ferroelectric domains with hollow triangular structures. Interlayer bubbles influence domain shape and periodicity through local electric field modulation, and tip-stress enables the reversible manipulation of domain area and polarization direction. These findings highlight the impact of flexoelectric effects on moiré ferroelectricity, offering a new tuning knob for its manipulation.

TSC22D3 as an immune-related prognostic biomarker for acute myeloid leukemia.

Acute myeloid leukemia (AML) is the type of hematologic neoplasm most common in adults. Glucocorticoid-induced gene TSC22D3 regulates cell proliferation through its function as a transcription factor. However, there is no consensus on the prognostic and immunoregulatory significance of TSC22D3 in AML. In the present study, we evaluated the correlation between TSC22D3 expression, immunoinfiltration, and prognostic significance in AML. Knockdown of TSC22D3 significantly attenuated the proliferation of Hel cells and increased sensitivity to cytarabine (Ara-c) drugs. Furthermore, TSC22D3 reduced the release of interleukin-1ß (IL-1ß) by inhibiting the NF-κB/NLRP3 signaling pathway, thereby inhibiting macrophage polarization to M1 subtype, and attenuating the pro-inflammatory tumor microenvironment. In conclusion, this study identified TSC22D3 as an immune-related prognostic biomarker for AML patients and suggested that therapeutic targeting of TSC22D3 may be a potential treatment option for AML through tumor immune escape.

Predicting Survival of Patients with Nonmetastatic Breast Cancer Based on Fibrinogen-to-Albumin Ratio and Lymphocyte-to-Monocyte Ratio: A Nomogram-Based Assessment.

Background: Parameters of systemic inflammation have received attention as prognostic surrogates in various malignant tumors. Fibrinogen-to-albumin ratio (FAR) and lymphocyte-to-monocyte ratio (LMR) correlate with tumor growth and dissemination. We aimed to bring the combination of FAR and LMR (FAR-LMR) together to establish novel nomograms for survival and recurrence in nonmetastatic breast cancer patients. Methods: We retrospectively recruited 461 female patients with nonmetastatic breast cancer from January 2011 to December 2013 in our hospital and randomly assigned them into the training cohort (N = 318) and the validation cohort (N = 143). The potential predictive factors for overall survival (OS), locoregional recurrence-free survival (LRFS), and distant metastasis-free survival (DMFS) were assessed by Cox proportional hazards models and log-rank test. Results: Elevated FAR was associated with poor OS (p < 0.001) and DMFS (p = 0.02), whereas increased LMR was associated with satisfactory OS (p = 0.01) and LRFS (p = 0.01). High FAR combined with low LMR was associated with less favorable OS (p = 0.001), LRFS (p = 0.005), and DMFS (p = 0.003) Based on multivariate analysis, FAR-LMR, tumor size, lymph node metastasis, age, and pathologic status contributed to prognostic nomograms of OS, DMFS, and LRFS. Nomograms presented exceptional performance for 3-, 5-, and 8-year OS, DMFS, and LRFS prediction compared with clinical TNM stage. The C-index was significantly higher than that of TNM stage, either of FAR or LMR (3-year: 0.709 vs. 0.621 vs. 0.544 vs. 0.641, 5-year: 0.761 vs. 0.597 vs. 0.605 vs. 0.677, 8-year: 0.84 vs. 0.62 vs. 0.539 vs. 0.623). Conclusions: We developed and validated a convenient predictive model for the survival outcomes of patients with nonmetastatic breast cancer. The nomograms can be utilized as auxiliary tools to provide prognostic information.

Development and validation of a tumor immune cell infiltration-related gene signature for recurrence prediction by weighted gene co-expression network analysis in prostate cancer.

Introduction: Prostate cancer (PCa) is the second most common malignancy in men. Despite multidisciplinary treatments, patients with PCa continue to experience poor prognoses and high rates of tumor recurrence. Recent studies have shown that tumor-infiltrating immune cells (TIICs) are associated with PCa tumorigenesis. Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to derive multi-omics data for prostate adenocarcinoma (PRAD) samples. The CIBERSORT algorithm was used to calculate the landscape of TIICs. Weighted gene co-expression network analysis (WGCNA) was performed to determine the candidate module most significantly associated with TIICs. LASSO Cox regression was applied to screen a minimal set of genes and construct a TIIC-related prognostic gene signature for PCa. Then, 78 PCa samples with CIBERSORT output p-values of less than 0.05 were selected for analysis. WGCNA identified 13 modules, and the MEblue module with the most significant enrichment result was selected. A total of 1143 candidate genes were cross-examined between the MEblue module and active dendritic cell-related genes. Results: According to LASSO Cox regression analysis, a risk model was constructed with six genes (STX4, UBE2S, EMC6, EMD, NUCB1 and GCAT), which exhibited strong correlations with clinicopathological variables, tumor microenvironment context, antitumor therapies, and tumor mutation burden (TMB) in TCGA-PRAD. Further validation showed that the UBE2S had the highest expression level among the six genes in five different PCa cell lines. Discussion: In conclusion, our risk-score model contributes to better predicting PCa patient prognosis and understanding the underlying mechanisms of immune responses and antitumor therapies in PCa.

One-step homogeneous micro-orifice resistance immunoassay for detection of chlorpyrifos in orange samples.

In this work, a one-step homogeneous micro-orifice resistance immunoassay has been proposed for chlorpyrifos detection by integrating functionalized polystyrene (PS) microsphere probes with particle counting technology. The particle counter is highly sensitive and accurate for detecting the state of PS microspheres, where the particles of different states exhibit significant differences in resistance. The state of the functionalized PS microspheres is altered from dispersed to aggregated during the antigen-antibody recognition. Based on the degree of aggregation of the functionalized PS microsphere probes, chlorpyrifos can be quantitatively detected through the competitive immune response between PS antibodies and PS complete antigens. This one-step homogeneous micro-orifice resistance immunoassay simplified the procedures and greatly increased the sensitivity of detection, which has been successfully applied to detect chlorpyrifos in orange samples within 0.5 h, with the detection limit of 0.058 ng/mL.

A survival prediction model and nomogram based on immune-related gene expression in chronic lymphocytic leukemia cells.

Introduction: There are many different chronic lymphoblastic leukemia (CLL) survival prediction models and scores. But none provide information on expression of immune-related genes in the CLL cells. Methods: We interrogated data from the Gene Expression Omnibus database (GEO, GSE22762; Number = 151; training) and International Cancer Genome Consortium database (ICGC, CLLE-ES; Number = 491; validation) to develop an immune risk score (IRS) using Least absolute shrinkage and selection operator (LASSO) Cox regression analyses based on expression of immune-related genes in CLL cells. The accuracy of the predicted nomogram we developed using the IRS, Binet stage, and del(17p) cytogenetic data was subsequently assessed using calibration curves. Results: A survival model based on expression of 5 immune-related genes was constructed. Areas under the curve (AUC) for 1-year survivals were 0.90 (95% confidence interval, 0.78, 0.99) and 0.75 (0.54, 0.87) in the training and validation datasets, respectively. 5-year survivals of low- and high-risk subjects were 89% (83, 95%) vs. 6% (0, 17%; p < 0.001) and 98% (95, 100%) vs. 92% (88, 96%; p < 0.001) in two datasets. The IRS was an independent survival predictor of both datasets. A calibration curve showed good performance of the nomogram. In vitro, the high expression of CDKN2A and SREBF2 in the bone marrow of patients with CLL was verified by immunohistochemistry analysis (IHC), which were associated with poor prognosis and may play an important role in the complex bone marrow immune environment. Conclusion: The IRS is an accurate independent survival predictor with a high C-statistic. A combined nomogram had good survival prediction accuracy in calibration curves. These data demonstrate the potential impact of immune related genes on survival in CLL.

Pan-Immune-Inflammation Value: A New Prognostic Index in Operative Breast Cancer.

Background: To build a predictive scoring model based on simple immune and inflammatory parameters to predict postoperative survival in patients with breast cancer. Methods: We used a brand-new immuno-inflammatory index-pan-immune-inflammation value (PIV)-to retrospectively evaluate the relationship between PIV and overall survival (OS), and based on the results of Cox regression analysis, we established a simple scoring prediction model based on several independent prognostic parameters. The predictive accuracy of the model was evaluated and independently validated. Results: A total of 1,312 patients were included for analysis. PIV was calculated as follows: neutrophil count (109/L) × platelet count (109/L) × monocyte count (109/L)/lymphocyte count (109/L). According to the best cutoff value of PIV, we divided the patients into two different subgroups, high PIV (PIV > 310.2) and low PIV (PIV ≤ 310.2), associated with significantly different survival outcomes (3-year OS, 80.26% vs. 86.29%, respectively; 5-year OS, 62.5% vs. 71.55%, respectively). Six independent prognostic factors were identified and used to build the scoring system, which performed well with a concordance index (C-index) of 0.759 (95% CI: 0.715-0.802); the calibration plot showed good calibration. Conclusions: We have established and verified a simple scoring system for predicting prognosis, which can predict the survival of patients with operable breast cancer. This system can help clinicians implement targeted and individualized treatment strategies.

Comprehensive Profiling Analysis of CD209 in Malignancies Reveals the Therapeutic Implication for Tumor Patients Infected With SARS-CoV-2.

Coronavirus disease 2019 (COVID-19), which is known to be caused by the virus severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is characterized by pneumonia, cytokine storms, and lymphopenia. Patients with malignant tumors may be particularly vulnerable to SARS-CoV-2 infection and possibly more susceptible to severe complications due to immunosuppression. Recent studies have found that CD209 (DC-SIGN) might be a potential binding receptor for SARS-CoV-2 in addition to the well-known receptor ACE2. However, pan-cancer studies of CD209 remain unclear. In this study, we first comprehensively investigated the expression profiles of CD209 in malignancies in both pan-carcinomas and healthy tissues based on bioinformatic techniques. The CD209 expression declined dramatically in various cancer types infected by SARS-CoV-2. Remarkably, CD209 was linked with diverse immune checkpoint genes and infiltrating immune cells. These findings indicate that the elevation of CD209 among specific cancer patients may delineate a mechanism accounting for a higher vulnerability to infection by SARS-CoV-2, as well as giving rise to cytokine storms. Taken together, CD209 plays critical roles in both immunology and metabolism in various cancer types. Pharmacological inhibition of CD209 antigen (D-mannose), together with other anti-SARS-CoV-2 strategies, might provide beneficial therapeutic effects in specific cancer patients.

A Prognostic Survival Model of Pancreatic Adenocarcinoma Based on Metabolism-Related Gene Expression.

Accurately predicting the survival prospects of patients suffering from pancreatic adenocarcinoma (PAAD) is challenging. In this study, we analyzed RNA matrices of 182 subjects with PAAD based on public datasets obtained from The Cancer Genome Atlas (TCGA) as training datasets and those of 63 subjects obtained from the Gene Expression Omnibus (GEO) database as the validation dataset. Genes regulating the metabolism of PAAD cells correlated with survival were identified. Furthermore, LASSO Cox regression analyses were conducted to identify six genes (XDH, MBOAT2, PTGES, AK4, PAICS, and CKB) to create a metabolic risk score. The proposed scoring framework attained the robust predictive performance, with 2-year survival areas under the curve (AUCs) of 0.61 in the training cohort and 0.66 in the validation cohort. Compared with the subjects in the low-risk cohort, subjects in the high-risk training cohort presented a worse survival outcome. The metabolic risk score increased the accuracy of survival prediction in patients suffering from PAAD.

Development and validation of a predictive model for peripherally inserted central catheter-related thrombosis in breast cancer patients based on artificial neural network: A prospective cohort study.

BACKGROUND: Peripherally inserted central catheters have been extensively applied in clinical practices. However, they are associated with an increased risk of thrombosis. To improve patient care, it is critical to timely identify patients at risk of developing peripherally inserted central catheter-related thrombosis. Artificial neural networks have been successfully used in many areas of clinical events prediction and affected clinical decisions and practice. OBJECTIVE: To develop and validate a novel clinical model based on artificial neural network for predicting peripherally inserted central catheter-related thrombosis in breast cancer patients who underwent chemotherapy and determine whether it may improve the prediction performance compared with the logistic regression model. DESIGN: A prospective cohort study. SETTING: A large general hospital in Fujian Province, China. PARTICIPANTS: One thousand eight hundred and forty-four breast cancer patients with peripherally inserted central catheters placement for chemotherapy were eligible for the study. METHODS: The dataset was divided into a training set (N = 1497) and an independent validation set (N = 347). The synthetic minority oversampling technique (SMOTE) was used to handle the effect of imbalance class. Both the artificial neural network and logistic regression models were then developed on the training set with and without SMOTE, respectively. The performance of each model was evaluated on the validation set using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS: Of the 1844 enrolled patients, 256 (13.9%) were diagnosed with peripherally inserted central catheter-related thrombosis. Predictive models were constructed in the training set and assessed in the validation set. Eight factors were selected as input variables to develop the artificial neural network model. Without SMOTE, the artificial neural network model (AUC = 0.725) outperformed the logistic regression model (AUC = 0.670, p = 0.039). SMOTE improved the performance of both two models based on AUC. With the SMOTE sampling, the artificial neural network model performed the best across all evaluated models, the AUC value remained statistically better than that of the logistic regression model (0.742 vs. 0.675, p = 0.004). CONCLUSION: Artificial neural network model can effectively predict peripherally inserted central catheter-related thrombosis in breast cancer patients receiving chemotherapy. Identifying high-risk groups with peripherally inserted central catheter-related thrombosis can provide close monitoring and an opportune time for intervention.

Landscape Profiling Analysis of DPP4 in Malignancies: Therapeutic Implication for Tumor Patients With Coronavirus Disease 2019.

Severe coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is characterized by pneumonia, lymphopenia, and cytokine storms. Patients with underlying conditions, and especially cancer patients with impaired immunity, are particularly vulnerable to SARS-CoV-2 infection and complications. Although angiotensin converting enzyme II (ACE2) has been identified as a cellular binding receptor for SARS-CoV-2, immunopathological changes in severe cancer patients support the investigation of additional potential receptors such as dipeptidyl peptidase 4 (DPP4), a key immunoregulator. However, a comprehensive profiling analysis of DPP4 in malignancies remains obscure. In this study, using different datasets, we demonstrated the expression of DPP4 in healthy tissues and pan-cancers, showing the risk of different cancer types towards SARS-CoV-2 infection according to DPP4 expression levels. DPP4 expression was positively correlated with infiltrating levels of various immune cells and showed strong correlations with diverse immune marker sets in pan-cancer patients analyzed by Tumor Immune Estimation Resource (TIMER). These findings suggest that increased DPP4 expression in specific cancer patients might account for the high susceptibility to SARS-CoV-2 infection and the induction of cytokine storms. Due to the critical role of DPP4 in immunometabolism, our results indicate that pharmacological inhibition of DPP4 might provide beneficial therapeutic effects for SARS-CoV-2 treatment together with other strategies in specific tumor patients.

A Model Combining Skeletal Muscle Mass and a Hematological Biomarker to Predict Survival in Patients With Nasopharyngeal Carcinoma Undergoing Concurrent Chemoradiotherapy.

BACKGROUND: Using the current tumor lymph node metastasis (TNM) staging system to make treatment decisions and predict survival in patients with nasopharyngeal carcinoma (NPC) lacks sufficient accuracy. Patients at the same stage often have different survival prognoses. METHODS: In the current study 802 NPC patients who underwent concurrent radiotherapy and chemotherapy from January 2010 to December 2014 at Sun Yat-sen University Cancer Center in China were retrospectively assessed. The optimal cut-off points for skeletal muscle index (SMI) and monocyte-to-lymphocyte ratio (MLR) were determined via receiver operating characteristic curves. SMI-MLR (S-M) grade and a nomogram were developed and used as clinical indicators in NPC patients. The consistency index (C-index) and a calibration curve were used to measure the accuracy and discriminative capacity of prediction. RESULTS: The predictive performance of S-M grade was better than that of TNM staging (C-index 0.639, range 0.578-0.701 vs. 0.605, range 0.545-0.665; p = 0.037). In multivariate analysis S-M grade, T stage, and N stage were independent prognostic factors. These three factors were then combined, yielding a nomogram with a C-index of 0.71 (range 0.64-0.77), indicating good predictive capacity. CONCLUSION: We developed and validated a prognostic parameter, S-M grade, which increased prediction accuracy significantly and can be combined with TNM staging to predict survival in patients with NPC undergoing concurrent chemoradiotherapy.

Modeling Sarcopenia to Predict Survival for Patients With Nasopharyngeal Carcinoma Receiving Concurrent Chemoradiotherapy.

BACKGROUND: The present study aimed to construct a prognostic nomogram including Epstein-Barr virus DNA (EBV-DNA) and sarcopenia in patients with nasopharyngeal carcinoma (NPC) receiving concurrent chemoradiotherapy (CCRT). METHODS: In this retrospective analysis, we studied 1,045 patients with NPC who had been treated with CCRT between 2010 and 2014. Sarcopenia was determined using routine pre-radiotherapy computed tomography scans of the third cervical vertebrae. A new S-E grade was constructed using a receiver-operating characteristic (ROC) curve analyses determined cutoff values of sarcopenia and plasma EBV-DNA. The nomogram was developed base on the sarcopenia-EBV (S-E) grade and traditional prognostic factors. A calibration curve, time-dependent ROC, decision curve analysis, and the concordance index (C-index) determined the accuracy of prediction and discrimination of the nomogram, and were compared with TNM staging system and a traditional nomogram. RESULTS: Patient survival was significantly different when sarcopenia (P < 0.001) or EBV-DNA (P = 0.001) were used and they continued to be independent prognostic factors for survival upon univariate (P < 0.001, P = 0.002, respectively) and multivariate (P < 0.001, P = 0.015, respectively) analyses. Predicting overall survival (OS) was more accurate using the S-E grade than using TNM staging and sarcopenia or EBV-DNA alone. Nomogram B (model with sarcopenia) or nomogram A (model without sarcopenia) were then developed based on the identified independent prognostic factors. Comparing nomogram prediction with actual observation showed good agreement among the calibration curves for probability of 1-, 3-, and 5-year OS. Predicted survival (C-index = 0.77) of nomogram B was statistically higher than that of nomogram A (0.676, P = 0.020) and TNM staging (0.604, P < 0.001). Risk group stratification could distinguish between survival curves within respective TNM stages (all stages, P < 0.001; stage III, P < 0.001; stage IV, P = 0.002). CONCLUSIONS: The sarcopenia-EBV DNA nomogram allowed more accurate prediction of prognosis for patients with NPC receiving CCRT.

A prognostic survival model based on metabolism-related gene expression in plasma cell myeloma.

Accurate survival prediction of persons with plasma cell myeloma (PCM) is challenging. We interrogated clinical and laboratory co-variates and RNA matrices of 1040 subjects with PCM from public datasets in the Gene Expression Omnibus database in training (N = 1) and validation (N = 2) datasets. Genes regulating plasma cell metabolism correlated with survival were identified and seven used to build a metabolic risk score using Lasso Cox regression analyses. The score had robust predictive performance with 5-year survival area under the curve (AUCs): 0.71 (95% confidence interval, 0.65, 0.76), 0.88 (0.67, 1.00) and 0.64 (0.57, 0.70). Subjects in the high-risk training cohort (score > median) had worse 5-year survival compared with those in the low-risk cohort (62% [55, 68%] vs. 85% [80, 90%]; p < 0.001). This was also so for the validation cohorts. A nomogram combining metabolic risk score with Revised International Staging System (R-ISS) score increased survival prediction from an AUC = 0.63 [0.58, 0.69] to an AUC = 0.73 [0.66, 0.78]; p = 0.015. Modelling predictions were confirmed in in vitro tests with PCM cell lines. Our metabolic risk score increases survival prediction accuracy in PCM.

A profiling analysis on the receptor ACE2 expression reveals the potential risk of different type of cancers vulnerable to SARS-CoV-2 infection.

BACKGROUND: The new coronavirus pneumonia (NCP) is now causing a severe public health emergency. The novel coronavirus 2019 (2019-nCoV) infected individuals by binding human angiotensin converting enzyme II (ACE2) receptor. ACE2 is widely expressed in multiple organs including respiratory, cardiovascular, digestive and urinary systems in healthy individuals. These tissues with high expression level of ACE2 seemed to be more vulnerable to SARS-CoV-2 infection. Recently, it has been reported that patients with tumors were likely to be more susceptible to SARS-CoV-2 infection and indicated poor prognosis. METHODS: The tissue atlas database and the blood atlas were used to analyze the distribution of ACE2 in human tissues or organs of cancers and normal samples. Starbase dataset was applied to predict the prognosis of cancers according to expression level of ACE2. RESULTS: In this study, we demonstrated a landscape profiling analysis on expression level of ACE2 in pan-cancers and showed the risky of different type of cancers to SARS-CoV-2 according to the expression level of ACE2. In addition, we found that ACE2 was both differential expression and related to the prognosis only in liver hepatocellular carcinoma (LIHC). Relative high expression of ACE2 indicated a favorable prognosis in LIHC, but they might be more susceptible to SARS-CoV-2. CONCLUSIONS: We indeed emphasized that LIHC patients with high expression level of ACE2 should be more cautious of the virus infection. Our study might provide a potential clue for preventing infection of SARS-CoV-2 in cancers.

Sarcopenia is associated with higher toxicity and poor prognosis of nasopharyngeal carcinoma.

BACKGROUND: Given the growing evidence that sarcopenia is associated with toxicity and survival in various cancers, we investigated its significance in patients with nasopharyngeal carcinoma (NPC) receiving concurrent chemoradiotherapy (CCRT). METHODS: In this retrospective analysis, we studied 862 NPC patients who had received CCRT between 2010 and 2014. Sarcopenia was determined using routine pre-radiotherapy computed tomography (CT) simulation scans at the third cervical vertebral level. Receiver-operating characteristic curve analyses were used to determine the optimal cutoff values. Propensity score matching (PSM) was applied to develop comparable cohorts of patients with or without sarcopenia. RESULTS: A total of 862 patients were included as the primary cohort, and 308 patients were matched and regarded as the matched cohort. In the primary cohort, the 5-year overall survival (OS), locoregional recurrence-free survival, and distant metastasis-free survival (DMFS) rates for the sarcopenia group versus non-sarcopenia group were 78.2% versus 93.6% (p < 0.001), 89.4% versus 87.9% (p = 0.918), and 82.5% versus 89.0% (p = 0.007), respectively. Univariate and multivariate survival analyses revealed that sarcopenia was an independent predictor of OS (p < 0.001 and p < 0.001) and DMFS (p = 0.009, p = 0.034). Patients with sarcopenia experienced significantly higher rates of treatment-related toxicities compared with patients without sarcopenia (p = 0.032). In addition, patients with sarcopenia also experienced significantly worse treatment response than those without sarcopenia (p = 0.004). Similar results were found in a PSM cohort. CONCLUSION: The current findings support that sarcopenia is a promising indicator for predicting clinical outcomes in NPC patients receiving CCRT. A simple and rapid analysis on CT simulation images can provide information about the therapeutic toxicity and survival prognosis, consequently guiding personalized multi-modality interventions during CCRT.