Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma.

Background: In contemporary study, the death of esophageal squamous cell carcinoma (ESCC) patients need precise and expedient prognostic methodologies. Objective: To develop and validate a prognostic model tailored to ESCC patients, leveraging the power of machine learning (ML) techniques and drawing insights from comprehensive datasets of laboratory-derived blood parameters. Methods: Three ML approaches, including Gradient Boosting Machine (GBM), Random Survival Forest (RSF), and the classical Cox method, were employed to develop models on a dataset of 2521 ESCC patients with 27 features. The models were evaluated by concordance index (C-index) and time receiver operating characteristics (Time ROC) curves. We used the optimal model to evaluate the correlation between features and prognosis and divide patients into low- and high-risk groups by risk stratification. Its performance was analyzed by Kaplan-Meier curve and the comparison with AJCC8 stage. We further evaluate the comprehensive effectiveness of the model in ESCC subgroup by risk score and KDE (kernel density estimation) plotting. Results: RSF's C-index (0.746) and AUC (three-year AUC 0.761, five-year AUC 0.771) had slight advantage over GBM and the classical Cox method. Subsequently, 14 features such as N stage, T stage, surgical margin, tumor length, age, Dissected LN number, MCH, Na, FIB, DBIL, CL, treatment, vascular invasion, and tumor grade were selected to build the model. Based on these, we found significant difference for survival rate between low-(3-year OS 81.8%, 5-year OS 69.8%) and high-risk (3-year OS 25.1%, 5-year OS 11.5%) patients in training set, which was also verified in test set (all P < 0.0001). Compared with the AJCC8th stage system, it showed a greater discriminative ability which is also in good agreement with its staging ability. Conclusion: We developed an ESCC prognostic model with good performance by clinical features and laboratory blood parameters.

Decision-Making Error in Platelet Transfusion Caused by EDTA-Dependent Pseudothrombocytopenia: a Case Report and Literature Review.

BACKGROUND: Ethylenediaminetetraacetic acid-dependent pseudothrombocytopenia (EDTA-PTCP) is a rare phenomenon characterized by pseudo low platelet counts when using EDTA as anticoagulant and can result in false decision making of platelet transfusion. METHODS: An application for platelet transfusion from a patient who planned to undergo spinal surgery was received by the Department of Transfusion service. The preoperative laboratory test results showed thrombocytopenia (platelet counts: 27 x 109/L). The surgeon planned to transfuse platelets before the operation to avoid bleeding in operation due to thrombocytopenia. However, the lab technologist found that there was aggregation of platelets under the microscope. Samples used with sodium citrate and heparin as anticoagulants were rechecked. RESULTS: The platelet count of the patient was normal in sodium citrate and heparin anticoagulant tubes. The patient had no history and clinical symptoms of thrombocytopenia. Therefore, the doctor canceled the platelet order. We also reviewed the relevant literature of EDTA-PTCP. CONCLUSIONS: EDTA-PTCP is rare and may result of a wrong decision of platelet transfusion. Correct understanding and treatment of this situation can avoid unnecessary platelet transfusion.

A prognostic nomogram that includes MPV in esophageal squamous cell carcinoma.

BACKGROUND: Mean platelet volume (MPV), as a marker of platelet activity, has been shown to be an efficient prognostic biomarker in several types of cancer. Using MPV, this study aimed to create and validate a prognostic nomogram to the overall survival in esophageal squamous cell carcinoma (ESCC) patients. METHODS: The nomogram was constructed and tested using data from a retrospective study of 1893 patients who were randomly assigned to the training and testing cohorts with a 7:3 randomization. In order to screen out the optimal predictors for overall survival (OS), we conducted the LASSO-cox regression, univariate, and multivariate cox regression analyses. Subsequently, the predictive accuracy of the nomogram was validated in both the training and the testing cohorts. Finally, decision curve analysis (DCA) was used to confirm clinical validity. RESULTS: Age, MPV, nerve invasion, T stage, and N stage were found as independent prognostic variables for OS and were further developed into a nomogram. The nomogram's prediction accuracy for 1-, 3-, and 5-year OS was 0.736, 0.749, 0.774, and 0.724, 0.719, 0.704 in the training and testing cohorts, respectively. Furthermore, DCA results indicated that nomograms outperformed the AJCC 8th and conventional T, N staging systems in both the training and testing cohorts. CONCLUSIONS: The nomogram, in conjunction with MPV and standard clinicopathological markers, could improve the accuracy of prediction of OS in ESCC patients.

Impact of fresh frozen plasma transfusion on mortality in extracorporeal membrane oxygenation.

BACKGROUND: Patients who receive extracorporeal membrane oxygenation (ECMO) support require substantial transfusions. Red blood cell (RBC) and platelet (PLT) transfusions have been reported to be associated with adverse outcomes in ECMO patients. However, little is known about whether the transfusion of fresh frozen plasma (FFP) is associated with mortality and morbidity among patients receiving ECMO. The aim of this study was to examine the relationship between FFP transfusion and mortality in ECMO patients and assess risk factors for the transfusion of FFP. METHODS: The clinical parameters of 116 ECMO patients were collected. The machine learning approach of the Boruta algorithm was employed to select the variables associated with ECMO patients' in-hospital mortality. Univariate and multivariate logistic regression analyses were applied to identify the association between the selected variables and in-hospital mortality. Spearman correlation and backwards stepwise multiple linear regression analyses were used to examine parameters contributing to FFP transfusion. RESULTS: Among the 116 patients who received ECMO support, the in-hospital mortality was 32.8%. The median FFP (mL/kg/d) transfusion was higher in dead patients (5.07, IQR 1.78-8.90) when compared to alive patients (2.16, IQR 0.79-4.66) (p = 0.007). After adjustment for confounders, FFP transfusion (mL/kg/d) was associated with in-hospital mortality (OR 1.09, 95% CI, 1.01-1.18; p = 0.035). Further analysis found that higher activated partial thromboplastin time (APTT), higher levels of uric acid (UA) and lower PLT counts were significant risk factors for FFP transfusion, with estimated values of 0.06 (95% CI, 0.02-0.11; p = 0.009), 0.01 (95% CI, 0.00-0.02; p = 0.003) and -0.03 (95% CI, -0.05--0.01; p = 0.007), respectively. CONCLUSION: FFP transfusion is markedly associated with in-hospital mortality among patients receiving ECMO, and higher APTT, higher levels of UA and lower PLT counts are risk factors for FFP transfusion. This suggests that better management of patients' coagulation system and kidney function may reduce the utilization of FFP, thus improving ECMO patient outcomes.

Detection and analysis of circulating large intergenic non-coding RNA regulator of reprogramming in plasma for breast cancer.

BACKGROUND: Previous studies have indicated that large intergenic non-coding RNA regulator of reprogramming (lincRNA-ROR) plays an important role in regulating tumor carcinogenesis and metastasis; however, whether circulating lincRNA-ROR could function as a potential biomarker for breast cancer (BC) diagnosis and monitoring is unknown. This study was conducted to investigate circulating lincRNA-ROR in plasma as a potential biomarker for BC diagnosis and monitoring. METHODS: We performed reverse transcription-quantitative-PCR to examine lincRNA-ROR expression levels in cell lines, 24 pairs of BC tissue samples, and 94 plasma samples from BC patients. Potential correlations between plasma lincRNA-ROR levels and clinicopathological characteristics were analyzed. A receiver operating characteristic curve was calculated to evaluate the diagnostic values for BC. Pearson correlation analysis of lincRNA-ROR in plasma samples and the corresponding tissues of the same patients was performed to explore tumor monitoring values. RESULTS: LincRNA-ROR expression was significantly increased in BC cell lines, tissues, and plasma (all P < 0.01). Plasma lincRNA-ROR levels were associated with estrogen receptors (P = 0.042) and lymph node metastasis (P = 0.046). The area under the receiver operating characteristic curve of plasma lincRNA-ROR was 0.844 (sensitivity 80.0%, specificity 56.7%), which was higher than carcinoembryonic and carbohydrate antigen 15-3 values. Moreover, plasma lincRNA-ROR levels were decreased in postoperative compared to preoperative samples (P < 0.0001). Plasma lincRNA-ROR levels moderately correlated with the corresponding tissue level in the same patients (r2 = 0.638, P < 0.0001). CONCLUSION: Plasma lincRNA-ROR may be a potential biomarker for BC diagnosis and a dynamic monitor.

Long non-coding RNAs as novel biomarkers for breast cancer invasion and metastasis.

Breast cancer (BC) is now the most common malignancy worldwide, with high prevalence and lethality among women. Invasion and metastasis are the major reasons for breast cancer-associated mortality. However, the underlying mechanism of invasion and metastasis has not been entirely elucidated. Long non-coding RNAs (lncRNAs) are a large class of non-coding transcripts that are >200 bases in length and cannot encode proteins. Evidence has indicated that lncRNAs regulate gene expression at the levels of epigenetic modification, transcription and post-transcription. In addition, they are involved in diverse tumor biological processes, including cell proliferation, apoptosis, invasion, metastasis and angiogenesis. The present review focuses on the recent progress of lncRNAs in breast cancer invasion and metastasis, aiming to provide novel strategies for the clinical prevention, diagnosis and treatment of breast cancer.

Circulating long non-coding HOX transcript antisense intergenic ribonucleic acid in plasma as a potential biomarker for diagnosis of breast cancer.

BACKGROUND: It is well known that HOX transcript antisense intergenic ribonucleic acid (HOTAIR) plays an important role in breast cancer (BC). However, whether circulating HOTAIR in plasma could be used for BC diagnosis and dynamic monitoring are unclear. METHODS: We tested the expression levels of HOTAIR in 30 pairs of tissue samples and 148 plasma samples from BC patients by quantitative real time-polymerase chain reaction, and the correlation between plasma HOTAIR levels and clinical features were analyzed. Receiver operating characteristic curve (ROC) was used to assess the diagnostic power of plasma HOTAIR for BC. Furthermore, we explored the monitoring values of plasma HOTAIR for BC and analyzed the correlation of HOTAIR levels between plasma and corresponding tissues of the same patients. RESULTS: The expression levels of HOTAIR were significantly higher in BC tissues and plasma than in the control ( P < 0.05). The expression levels of plasma HOTAIR were correlated with lymph node metastasis ( P = 0.018), estrogen receptor (ER) ( P = 0.012), c-erbB-2 (P = 0.006) and triple positive ( P = 0.015). The area under the ROC curve of plasma HOTAIR was 0.80 (sensitivity 69.2%; specificity 93.3%), which was higher than the carcinoembryonic antigen and carbohydrate antigen 15-3 values obtained. Moreover, plasma HOTAIR expression levels in postoperative patients were lower than those in preoperative patients ( P = 0.029) and were moderately correlated with the corresponding tissue levels of the same patients (r = 0.68, P < 0.0001). CONCLUSION: These results indicated that HOTAIR may be a potential biomarker for the diagnosis of BC.

Circulating lncRNA H19 in plasma as a novel biomarker for breast cancer.

BACKGROUND: Long non-coding RNA (lncRNA) H19 has been well studied playing an important role in breast cancer (BC) progress and the expression of H19 may service as a diagnostic target for BC. However, it is unclear if circulating lncRNA H19 could as a potential biomarker for BC diagnosis and monitor. OBJECTIVE: The objective of our study was to determine whether plasma lncRNA H19 could be used as biomarkers for the screening and early diagnosis of breast cancer. METHODS: In this study, we carried out a quantitative real-time PCR (qRT-PCR) method to examine the expression levels of lncRNA H19 in 24 pairs of BC tissues and 20 pairs of BC plasma. The differentially expressed of plasma H19 was further validated in another 102 BC patients and 96 healthy controls. The potential correlations between plasma H19 levels and clinicopathological characteristics were analyzed. Receiver operating characteristic (ROC) curve was performed to evaluate the diagnostic values of plasma H19 between 30 early stage BC patients and 30 healthy controls. 24 paired pre- and postoperative plasma samples were detected to assess the tumor monitoring values. RESULTS: The results revealed that the expression of H19 was significantly increased in BC tissues and plasma compared with healthy controls (P< 0.05), and plasma H19 levels were significantly correlated with estrogen receptor (ER) (P= 0.008), progesterone receptor (PR) (P= 0.025), c-erbB-2 (P= 0.043) and lymph node metastasis (P= 0.006). The area under the ROC curve (AUC) of plasma H19 was 0.81(sensitivity, 56.7%; specificity, 86.7%; P< 0.0001), which was higher than the values of carcinoembryonic antigen (CEA) and carbohydrate antigen 153 (CA153). Furthermore, plasma H19 levels were significantly decreased in postoperative samples than preoperative samples (P= 0.0006). CONCLUSION: Plasma H19 may serve as a potential biomarker for BC early screening and prognosis monitor.

[Long non-coding RNA HOTAIR in plasma as a potential biomarker for breast cancer diagnosis].

OBJECTIVE: To investigate the expression of long non-coding RNA HOTAIR in the plasma of breast cancer patients and its value in the diagnosis of breast cancer. METHODS: HOTAIR levels were measured in 24 tumor tissues and 70 plasma samples from breast cancer patients using quantitative real-time PCR. The correlations of plasma HOTAIR level with the clinicopathological features of the patients were analyzed. A multivariate logistic regression model was established to analyze the value of plasma HOTAIR in comparison with plasma CA153 and CEA levels for breast cancer diagnosis. We further detected HOTAIR levels in the plasma and breast cancer tissues of 24 patients before and after operation and investigated their correlation. RESULTS: Breast cancer patients had increased expressions of HOTAIR in the tumor tissues and plasma, and plasma HOTAIR level was significantly correlated with estrogen receptor (ER) level (P=0.004) and lymph node metastasis (P=0.010). Receiver operating characteristic (ROC) curve and the multivariable logistic regression model showed that the area under ROC curve (AUC) of plasma HOTAIR was 0.82 (P<0.001) for breast cancer diagnosis with a diagnostic sensitivity and a specificity of 73.3% and 93.3%, respectively. The diagnostic power and specificity of plasma HOTAIR was much higher than those of CA153 (AUC=0.66, P=0.030) and CEA (AUC=0.52, P=0.001), and the combination of the 3 markers further enhanced the diagnostic power (AUC=0.84) and specificity (96.7%). Plasma HOTAIR level was significantly reduced in the patients after the operation (P<0.0001) and showed a moderate correlation with its expression in tumor tissues (r=0.62, P<0.0001). CONCLUSION: Plasma HOTAIR may serve as a potential biomarker for breast cancer diagnosis.