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.

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.

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.

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.

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.

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.

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.

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 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.

Low Skeletal Muscle Mass Impairs Quality of Life in Nasopharyngeal Carcinoma Patients Treated With Concurrent Chemoradiotherapy.

Background: Nasopharyngeal carcinoma (NPC) patients receiving concurrent chemoradiotherapy (CCRT) frequently develop low skeletal muscle mass (SMM), but, little is known about the impacts of low SMM on health-related quality of life (QOL). Methods: We retrospectively assessed 56 patients with locoregionally advanced NPC enrolled in a prospective trial. Low SMM was determined on routine computed tomography simulation (CT-sim) scans taken before radiotherapy, at the third cervical (C3) vertebral level with validated sex-specific cutoffs. QOL was assessed using the World Health Organization Quality of Life Questionnaire-100 at baseline and after 3 weeks. Pain was scored every 24 h using a numerical rating scale (NRS). Characteristics related to low SMM were identified by logistic regression. The chi-square test was used to examine the association of low SMM with QOL and pain. Results: Of the 56 participants (mean age 44.20 ± 10.93 years), over half (60.71%) developed low SMM. Patients with low SMM were more likely to be older (P = 0.035), male (P = 0.066), have a lower body-mass index (BMI; P = 0.091), and have a higher pain score (P = 0.001). Older age (hazard ratio [HR] = 1.788, P = 0.016), being male (HR = 3.145, P = 0.010), lower BMI (HR = 0.761, P = 0.033), and lower prognostic nutritional index (HR = 0.186, P = 0.034) were associated with higher risk of low SMM. Low SMM was associated with poorer baseline QOL scores (P = 0.072), especially in the physical domain (P = 0.002) and its three facets: pain (P = 0.003), energy (P = 0.021), and sleep (P = 0.007). Low SMM was also associated with significantly worse QOL scores (P = 0.006) at 3 weeks, especially in the physical (P = 0.002), psychological (P = 0.046), independence (P = 0.003), social domains (P = 0.023), and in general health condition (P = 0.043). For pain score, low SMM group had worse overall changes from baseline to week 3 (P = 0.011). Conclusions: The incidence of low SMM, as evaluated using routine CT-sim scans, is high in patients receiving CCRT for locoregionally advanced NPC. Low SMM results in poorer QOL and higher pain scores, which underscores the requirement for nutritional and functional interventions to address low SMM early in the treatment course.