Molecular Characterization and Prognostication of Large Cell Neuroendocrine Carcinoma and Large Cell Carcinoma

PurposeLarge cell neuroendocrine carcinoma (LCNEC) and classic large cell carcinoma (LCC) are two distinct entities with different histological and biological characteristics. However, the mutational profiles and the clinical behavior of the two subtypes of lung cancer remain to be explored.Patients and MethodsPathological diagnoses of all screened patients were finally confirmed by three or four experienced pathologists. Patients with uncertain pathological diagnoses were excluded. Finally, we genetically profiled ten patients with LCNEC and seven with LCC. ALL patients were subjected to next-generation sequencing (NGS) test, which included nine patients sequenced with a 139-gene panel and eight patients with a 425-gene panel. Including only intersected mutations from these two panels, survival analysis was further conducted.ResultsBoth LCNEC and LCC showed high prevalence in male patients, with no clear association with smoking history. Potential targetable mutations in KRAS and RET were detected in the study cohort. However, LCNEC and LCC showed distinct mutational profiles with an enrichment of RB1/TP53 co-mutations in a subset of LCNEC patients. SMARCA4 and KEAP1 mutations were exclusively found in LCC patients, and RICTOR, BRAF, ROS1 and TET2 mutations were only detected in LCNEC. LCC patients in the cohort had shorter survival compared to LCNEC patients (p=0.006). Survival analysis revealed an association between SMARCA4 mutations and poor outcome in the study cohort and in the LCC subset. Mutations in BRAF were associated with a trend of increased survival in the study cohort, as well as in the LCNEC subset. Finally, TET2 mutations were associated with poor outcome in the LCNEC cohort.ConclusionLCC and LCNEC were both heterogeneous diseases with limited treatment options. Our study identified potential targetable mutations and prognostic biomarkers that might provide more therapeutic options and improve individualized patient care.

What are the risk factors of hospital length of stay in the novel coronavirus pneumonia (COVID-19) patients? A survival analysis in southwest China

Background The global epidemic of novel coronavirus pneumonia (COVID-19) has resulted in substantial healthcare resource consumption. Since patients’ hospital length of stay (LoS) is at stake in the process, an investigation of COVID-19 patients’ LoS and its risk factors becomes urgent for a better understanding of regional capabilities to cope with COVID-19 outbreaks. Methods First, we obtained retrospective data of confirmed COVID-19 patients in Sichuan province via National Notifiable Diseases Reporting System (NNDRS) and field surveys, including their demographic, epidemiological, clinical characteristics and LoS. Then we estimated the relationship between LoS and the possibly determinant factors, including demographic characteristics of confirmed patients, individual treatment behavior, local medical resources and hospital grade. The Kaplan-Meier method and the Cox Proportional Hazards Model were applied for single factor and multi-factor survival analysis. Results From January 16, 2020 to March 4, 2020, 538 human cases of COVID-19 infection were laboratory-confirmed, and were hospitalized for treatment, including 271 (50%) patients aged ≥ 45, 285 (53%) males, and 450 patients (84%) with mild symptoms. The median LoS was 19 (interquartile range (IQR): 14–23, range: 3–41) days. Univariate analysis showed that age and clinical grade were strongly related to LoS (P<0.01). Adjusted multivariate analysis showed that the longer LoS was associated with those aged ≥ 45 (Hazard ratio (HR): 0.74, 95% confidence interval (CI): 0.60–0.91), admission to provincial hospital (HR: 0.73, 95% CI: 0.54–0.99), and severe illness (HR: 0.66, 95% CI: 0.48–0.90). By contrast, the shorter LoS was linked with residential areas with more than 5.5 healthcare workers per 1,000 population (HR: 1.32, 95% CI: 1.05–1.65). Neither gender factor nor time interval from illness onset to diagnosis showed significant impact on LoS. Conclusions Understanding COVID-19 patients’ hospital LoS and its risk factors is critical for governments’ efficient allocation of resources in respective regions. In areas with older and more vulnerable population and in want of primary medical resources, early reserving and strengthening of the construction of multi-level medical institutions are strongly suggested to cope with COVID-19 outbreaks.

Survival Analysis to Predict How Color Influences the Shelf Life of Strawberry Leather

Color change of fruit-based products during storage is an important quality parameter to determine their shelf life. In this study, a combination of relative humidity (RH) and illumination was evaluated on the stability of strawberry leathers. Samples were conditioned at 25 °C, in chambers with RH of 22.5% and 52.3% and under two levels of illumination (no illumination and with a light-emitting diode (LED) illumination at 1010 lx). Samples were analyzed during storage by instrumental color measurements, total anthocyanin content, and consumers’ acceptance/rejection of the product color. Current-status survival analysis was performed to estimate the sensory-based shelf-life of the strawberry leather. The chromatic parameters (a* and ΔE* values) and anthocyanin content changed with increasing storage time and RH, fitting a first-order fractional conversion model. Samples conditioned at the higher RH showed a higher reduction of a* values and anthocyanins losses when stored under LED illumination than those without illumination. The increase of RH resulted in a faster increase of the consumer rejection probability and a shorter shelf life of the strawberry leather. For 50% of consumers’ rejection, the sensory shelf life of the strawberry leather equilibrated at 22.5% RH was estimated as at least 54 days, while it was reduced to approximately 2 days at 52.3% RH. The red chromatic parameter (a* value) strongly correlated to the percentage of consumer rejection in all storage conditions, suggesting that this analytical parameter can be useful as a predictor of strawberry leather’s shelf life. Therefore, the results of this study show the applicability of an approach that integrates instrumental and sensory data to acquire faster information on color changes during the storage of strawberry leather and product shelf-life prediction.

Predicting Clear Cell Renal Cell Carcinoma Survival Using Kurtosis of Cytoplasm in the Hematoxylin Channel from Histology Slides

Purpose. Grade-dependent decrease of lipid storage in clear cell renal cell carcinoma (ccRCC) leads to morphology changes in HE sections. This study investigated the role of cytoplasmic features in frozen sections of ccRCC on prognosis using the digital pathology approach. Methods. We established an automatic pipeline that performed tumor region selection, stain vector normalization, nuclei segmentation, and feature extraction based on the pathologic data from Shanghai General Hospital and The Cancer Genome Atlas database. Extracted features were subjected to survival analysis. Results. Kurtosis of the cytoplasm in the hematoxylin channel was correlated with progression-free survival (HR 0.10, 95% CI: 0.04–0.24, p = 6.52 ∗ 10 − 7 ) and overall survival (HR 0.11, 95% CI: 0.05–0.31, p = 1.72 ∗ 10 − 5 ) in ccRCC, which outperformed other texture features in this analysis. Multivariate Cox regression analysis revealed that low kurtosis of cytoplasm in the hematoxylin channel was an independent predictor for a shorter progression-free survival time ( p = 0.044 ) and overall survival time (p = 0.01). Kaplan–Meier survival analysis of progression-free survival and overall survival also showed a significantly worse prognosis in patients with low kurtosis of the cytoplasm in the hematoxylin channel (both p < 0.0001 ). Lower kurtosis of cytoplasm in the hematoxylin channel was associated with higher pathologic grade, less cholesterol ester, and more mitochondrial DNA content. Conclusion. Kurtosis of the cytoplasm in the hematoxylin channel predicts survival in clear cell renal cell carcinoma.

Clinical Significance and Potential Role of LSM4 Overexpression in Hepatocellular Carcinoma: An Integrated Analysis Based on Multiple Databases

Background: Hepatocellular carcinoma (HCC) is a solid tumor with high recurrence rate and high mortality. It is crucial to discover available biomarkers to achieve early diagnosis and improve the prognosis. The effect of LSM4 in HCC still remains unrevealed. Our study is dedicated to exploring the expression of LSM4 in HCC, demonstrating its clinical significance and potential molecular mechanisms.Methods: Clinical information and LSM4 expression values of HCC were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Survival analysis and receiver operating characteristic (ROC) curve analysis were applied to evaluate the prognostic and diagnostic significance of LSM4. Calculating pooled standardized mean difference (SMD) and performing summary receiver operating characteristic (sROC) curve analysis to further determine its expression status and diagnostic significance. LSM4-related co-expressed genes (CEGs) were obtained and explored their clinical significance in HCC. LSM4-associated pathways were identified through Gene set enrichment analysis (GSEA).Results: Up-regulated LSM4 was detected in HCC tissues (SMD = 1.56, 95% CI: 1.29–1.84) and overexpressed LSM4 had excellent distinguishing ability (AUC = 0.91, 95% CI: 0.88–0.93). LSM4 was associated with clinical stage, tumor grade, and lymph node metastasis status (p &lt; 0.05). Survival analysis showed that high LSM4 expression was related to poor overall survival (OS) of HCC patients. Cox regression analysis suggested that high LSM4 expression may be an independent risk factor for HCC. We obtained nine up-regulated CEGs of LSM4 in HCC tissues, and six CEGs had good prognostic and diagnostic significance. GSEA analysis showed that up-regulated LSM4 was closely related to the cell cycle, cell replication, focal adhesion, and several metabolism-associated pathways, including fatty acid metabolism.Conclusion: Overexpressed LSM4 may serve as a promising diagnostic and prognostic biomarker of HCC. Besides, LSM4 may play a synergistic effect with CEGs in promoting the growth and metastasis of HCC cells via regulating crucial pathways such as cell cycle, focal adhesion, and metabolism-associated pathways.

Evaluation of Log Odds of Positive Lymph Nodes in Predicting the Survival of Neoadjuvant Therapy Patients with Non-Small Cell Lung Cancer After Surgery: A SEER Cohort-Based Study

Background log odds of positive lymph nodes (LODDS) is a novel lymph node (LN) descriptor, demonstrating promising prognostic value in many tumors. However, there was limited information on LODDS in non-small cell lung cancer (NSCLC) patients, especially those receiving neoadjuvant therapy followed by lung surgery. Methods A total of 2,059 NSCLC patients who received neoadjuvant therapy and surgery were identified in the Surveillance, Epidemiology, and End Results (SEER) database. We used the X-tile software to calculate the cut-off value of LODDS. Kaplan-Meier survival analysis and receiver operating characteristics (ROC) curve were used to compare the predictive value of the American Joint Committee on Cancer (AJCC) N staging descriptor and LODDS. Univariate and multivariate Cox regression and inverse probability of treatment weighting (IPTW) analyses were conducted to construct the model predicting the prognosis. Results LODDS showed better differentiating ability in survival analysis than N staging descriptor (Log-rank test, P<0.0001 vs. P=0.031). The ROC curve demonstrated that the AUC of LODDS was significantly higher than the N staging descriptor in 1-year, 3-year, and 5-year survival analyses (All P<0.05). Univariate and multivariate Cox regression analysis showed that the LODDS was an independent risk factor for NSCLC patients receiving neoadjuvant therapy followed by surgery, both before and after IPTW (all P<0.001). A clinicopathological model with LODDS, age, gender, T, and radiotherapy could better predict the prognosis. Conclusions Compared with the AJCC N staging descriptor, LODDS exhibits better predictive ability for NSCLC patients receiving neoadjuvant therapy followed by surgery. A multivariate clinicopathological model with LODDS included demonstrates sound performance in predicting the prognosis.