Prognostic value of creatine kinase (CK)-MB to total-CK ratio in colorectal cancer patients after curative resection.

OBJECTIVES: This study aimed to evaluate the prognostic significance of postoperative Creatine Kinase type M and B (CK-MB) to total Creatine Kinase (CK) ratio (CK-MB/CK) in colorectal cancer (CRC) patients after radical resection. METHODS: This was a single-center retrospective cohort analysis. Subjects were stage I-III CRC patients hospitalized in Sichuan Cancer Hospital from January 2017 to May 2021. Patients were divided into abnormal group and normal group according to whether the CK-MB/CK ratio was abnormal after surgery. Through a comparative analysis of clinical data, laboratory test results, and prognosis differences between the two groups, we aimed to uncover the potential relationship between abnormal CK-MB > CK results and CRC patients. To gauge the impact of CK-MB/CK on overall survival (OS) and disease-free survival (DFS), we employed the multivariable COX regression and LASSO regression analysis. Additionally, Spearman correlation analysis, logistic regression, and receiver-operating characteristic (ROC) curve analysis were conducted to assess the predictive value of the CK-MB/CK ratio for postoperative liver metastasis. RESULTS: Cox regression analysis revealed that the CK-MB/CK ratio was a stable risk factors for OS (HR = 3.82, p < 0.001) and DFS (HR = 2.31, p < 0.001). To distinguish hepatic metastases after surgery, the ROC area under the curve of CK-MB/CK was 0.697 (p < 0.001), and the optimal cut-off value determined by the Youden index was 0.347. CONCLUSIONS: Postoperative abnormal CK-MB/CK ratio predicts worse prognosis in CRC patients after radical resection and serves as a useful biomarker for detecting postoperative liver metastasis.

A Molecular Typing Method for Invasive Breast Cancer by Serum Raman Spectroscopy.

BACKGROUND: The incidence of breast cancer ranks highest among cancers and is exceedingly heterogeneous. Immunohistochemical staining is commonly used clinically to identify the molecular subtype for subsequent treatment and prognosis. PURPOSE: Raman spectroscopy and support vector machine (SVM) learning algorithm were utilized to identify blood samples from breast cancer patients in order to investigate a novel molecular typing approach. METHOD: Tumor tissue coarse needle aspiration biopsy samples, and peripheral venous blood samples were gathered from 459 invasive breast cancer patients admitted to the breast department of Sichuan Cancer Hospital between June 2021 and September 2022. Immunohistochemical staining and in situ hybridization were performed on the coarse needle aspiration biopsy tissues to obtain their molecular typing pathological labels, including: 70 cases of Luminal A, 167 cases of Luminal B (HER2-positive), 57 cases of Luminal B (HER2-negative), 84 cases of HER2-positive, and 81 cases of triple-negative. Blood samples were processed to obtained Raman spectra taken for SVM classification models establishment with machine algorithms (using 80% of the sample data as the training set), and then the performance of the SVM classification models was evaluated by the independent validation set (20% of the sample data). RESULTS: The AUC values of SVM classification models remained above 0.85, demonstrating outstanding model performance and excellent subtype discrimination of breast cancer molecular subtypes. CONCLUSION: Raman spectroscopy of serum samples can promptly and precisely detect the molecular subtype of invasive breast cancer, which has the potential for clinical value.

MCSdb, a database of proteins residing in membrane contact sites.

Organelles do not act as autonomous discrete units but rather as interconnected hubs that engage in extensive communication by forming close contacts called "membrane contact sites (MCSs)". And many proteins have been identified as residing in MCS and playing important roles in maintaining and fulfilling specific functions within these microdomains. However, a comprehensive compilation of these MCS proteins is still lacking. Therefore, we developed MCSdb, a manually curated resource of MCS proteins and complexes from publications. MCSdb documents 7010 MCS protein entries and 263 complexes, involving 24 organelles and 44 MCSs across 11 species. Additionally, MCSdb orchestrates all data into different categories with multitudinous information for presenting MCS proteins. In summary, MCSdb provides a valuable resource for accelerating MCS functional interpretation and interorganelle communication deciphering.

Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types.

Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available. Metabolomics has recently emerged as a promising field for developing novel tumor biomarkers. In this paper, we introduce metabolic pathways, instrument platforms, and a wide variety of sample types for lung cancer metabolomics. Specifically, we explore the strengths, limitations, and distinguishing features of various sample types employed in lung cancer metabolomics research. Additionally, we present the latest advances in lung cancer metabolomics research that utilize diverse sample types. We summarize and enumerate research studies that have investigated lung cancer metabolomics using different metabolomic sample types. Finally, we provide a perspective on the future of metabolomics research in lung cancer. Our discussion of the potential of metabolomics in developing new tumor biomarkers may inspire further study and innovation in this dynamic field.

Usefulness of baseline immature reticulocyte fraction to mature reticulocyte fraction ratio (IMR) as A prognostic predictor for patients with small cell lung cancer.

Background: Small cell lung cancer (SCLC) has a strong invasive ability and a high degree of malignancy, so accurate prognosis prediction is crucial for making the most favorable treatment decision.Unfortunately, there is a scarcity of prognostic indicators specific to SCLC. Reticulocyte levels in blood parameters have been linked to the prognosis of various malignancies. Given SCLC's aggressive characteristics, identifying reliable prognostic markers, such as reticulocyte counts, becomes pivotal in enhancing prognostic accuracy and guiding effective therapeutic strategies. Objective: This study aimed to evaluate the predictive power of the immature reticulocyte fraction (IRF) to mature reticulocyte fraction (MRF) ratio (IMR) for survival outcomes in patients with SCLC. Materials and methods: A retrospective analysis was conducted on 192 patients with small cell lung cancer (SCLC). The median values of various prognostic indicators, such as IMR, IRF, MRF, reticulocyte count (RET), SII (systemic immune-inflammatory index), were utilized as cutoff points, categorizing patients into high and low groups. The Kaplan-Meier method, univariate, multivariate analyses Cox regression, and C-index were used to analyze the prognostic factors for overall survival (OS). Results: In our cohort, 138 (71.9 %) were male, 119 (62 %) were smokers, and 82 (57.3 %) were older than 60 years old. The median survival time was 18.15 months.Higher mortality was observed in the high IMR and high IRF groups, while the high MRF group exhibited lower mortality. At the same time, mortality was lower in the high MRF group. Univariate analysis showed that smoking history (P = 0.006), tumor stage (P = 0.002), chemotherapy cycle (P = 0.014), IMR (P = 0.01), and many other factors significantly affected the prognosis of SCLC. Multivariate analysis demonstrated that elevated IMR was an independent adverse predictor of OS (P = 0.039, HR = 0.330). Spearman test confirmed that the prognostic indicators IRF, IMR, and SII were positively correlated with the overall survival rate of patients with SCLC. Kaplan-Meier analysis showed that the OS rate of patients with high IMR was significantly worse (P = 0.0096). In addition, we found that IMR was superior to IRF in distinguishing patients with different outcomes in the low and high groups (P < 0.05). Our novel integration index, combining IMR with the TNM stage system and SII index, exhibited superior prognostic value compared to the original index. Additionally, the combination of prognostic indicators IMR and SII significantly stratified stage I-II SCLC patients (P ＜0.05). Conclusions: The prognostic index based on peripheral blood IMR stands out as an independent predictor for SCLC patients pre-treatment. Its accessibility through routine blood analysis facilitates immediate clinical application without requiring prolonged scientific research validation. The integration of IMR with the TNM score enhances survival prediction and risk stratification. Notably, when combined with the SII score, the new IMR index demonstrates significant improvements in prognostication for stage I-II small cell lung cancer.

Convenient determination of serum HER-2 status in breast cancer patients using Raman spectroscopy.

Given the significant therapeutic efficacy of anti-HER-2 treatment, the HER-2 status is a crucial piece of information that must be obtained in breast cancer patients. Currently, as per guidelines, HER-2 status is typically acquired from breast tissue of patients. However, there is growing interest in obtaining HER-2 status from serum and other samples due to the convenience and potential for dynamic monitoring. In this study, we have developed a serum Raman spectroscopy technique that allows for the rapid acquisition of HER-2 status in a convenient manner. The established HER-2 negative and positive classification model achieved an area under the curve of 0.8334. To further validate the reliability of our method, we replicated the process using immunohistochemistry and in situ hybridization. The results demonstrate that serum Raman spectroscopy, coupled with artificial intelligence algorithms, is an effective technical approach for obtaining HER-2 status.

Application of serum Raman spectroscopy combined with classification model for rapid breast cancer screening.

Introduction: This study aimed to evaluate the feasibility of using general Raman spectroscopy as a method to screen for breast cancer. The objective was to develop a machine learning model that utilizes Raman spectroscopy to detect serum samples from breast cancer patients, benign cases, and healthy subjects, with puncture biopsy as the gold standard for comparison. The goal was to explore the value of Raman spectroscopy in the differential diagnosis of breast cancer, benign lesions, and healthy individuals. Methods: In this study, blood serum samples were collected from a total of 333 participants. Among them, there were 129 cases of tumors (pathologically diagnosed as breast cancer and labeled as cancer), 91 cases of benign lesions (pathologically diagnosed as benign and labeled as benign), and 113 cases of healthy controls (labeled as normal). Raman spectra of the serum samples from each group were collected. To classify the normal, benign, and cancer sample groups, principal component analysis (PCA) combined with support vector machine (SVM) was used. The SVM model was evaluated using a cross-validation method. Results: The results of the study revealed significant differences in the mean Raman spectra of the serum samples between the normal and tumor/benign groups. Although the mean Raman spectra showed slight variations between the cancer and benign groups, the SVM model achieved a remarkable prediction accuracy of up to 98% for classifying cancer, benign, and normal groups. Discussion: In conclusion, this exploratory study has demonstrated the tremendous potential of general Raman spectroscopy as a clinical adjunctive diagnostic and rapid screening tool for breast cancer.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer.

BACKGROUND: Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis. METHODS: Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve. RESULTS: From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients. CONCLUSIONS: This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Serum laser Raman spectroscopy as a potential diagnostic tool to discriminate the benignancy or malignancy of pulmonary nodules.

It has been proved that Raman spectral intensities could be used to diagnose lung cancer patients. However, the application of Raman spectroscopy in identifying the patients with pulmonary nodules was barely studied. In this study, we revealed that Raman spectra of serum samples from healthy participants and patients with benign and malignant pulmonary nodules were significantly different. A support vector machine (SVM) model was developed for the classification of Raman spectra with wave points, according to ANOVA test results. It got a good performance with a median area under the curve (AUC) of 0.89, when the SVM model was applied in discriminating benign from malignant individuals. Compared with three common clinical models, the SVM model showed a better discriminative ability and added more net benefits to participants, which were also excellent in the small-size nodules. Thus, the Raman spectroscopy could be a less-invasive and low-costly liquid biopsy.

Research progress on the interaction between oxidative stress and platelets: Another avenue for cancer?

Oxidative stress (OS) is a chemical imbalance between an oxidant and an antioxidant, causing damage to redox signaling and control or causing molecular damage. Unbalanced oxidative metabolism can produce excessive reactive oxygen species (ROS). These excess ROS can cause drastic changes in platelet metabolism and further affect platelet function. It will also lead to an increase in platelet procoagulant phenotype and cell apoptosis, which will increase the risk of thrombosis. The creation of ROS and subsequent platelet activation, adhesion, and recruitment are then further encouraged in an auto-amplifying loop by ROS produced from platelets. Meanwhile, cancer cells produce a higher concentration of ROS due to their fast metabolism and high proliferation rate. However, excessive ROS can result in damage to and modification of cellular macromolecules. The formation of cancer and its progression is strongly associated with oxidative stress and the resulting oxidative damage. In addition, platelets are an important part of the tumor microenvironment, and there is a significant cross-communication between platelets and cancer cells. Cancer cells alter the activation status of platelets, their RNA spectrum, proteome, and other properties. The "cloaking" of cancer cells by platelets providing physical protection，avoiding destruction from shear stress and the attack of immune cells, promoting tumor cell invasion.We explored the vicious circle interaction between ROS, platelets, and cancer in this review, and we believe that ROS can play a stimulative role in tumor growth and metastasis through platelets.

Factors influencing platelet isolation: a prospective multicenter study from Western China.

Tumor-educated platelets (TEPs) have been widely reported to have promising application potential; nonetheless, platelet isolation from peripheral blood is an important but neglected step in TEPs research for platelet-based liquid biopsy. In this article, we discussed some common influence factors for platelet isolation. To investigate the factors involved in platelet isolation, a prospective multicenter study was conducted on healthy Han Chinese adults (18 to 79 years of age). A total of 208 individuals were included in the final statistical analysis out of the 226 healthy volunteers who were prospectively enrolled from four hospitals. The primary study metric was the platelet recovery rate (PRR). The similar pattern was observed in the four hospitals, The PRR at room temperature (23°C±2°C) was slightly higher than the PRR at cold temperature (4°C±2°C). Moreover, the PRR gradually decreased as the storage time increased. The PRR for samples within 2 hours of storage is significantly higher than for samples beyond 2 hours (p < .05). Additionally, PRR was also affected by the equipment used in different centers. This study confirmed several factors that influence platelet isolation. In our study, we indicated that platelet isolation should be performed within two hours of peripheral blood draw and held at room temperature until isolation, and that centrifuge models should be fixed during the extraction process, which will further improve the research progress of platelet-based liquid biopsy in cancer.

What is the context? Globally, cancer is one of the leading cause of premature death. Early screening is important for cancer diagnosis and treatment and can even significantly lower cancer mortalityGlobally, cancer is one of the leading cause of premature death. Early screening is important for cancer diagnosis and treatment and can even significantly lower cancer mortalityFor the liquid biopsy, isolation is an important step. Early studies have explored the influencing factors of exosome, circulating tumor cells (CTCs), and other components extraction in liquid biopsy.Despite platelet also being an excellent source of liquid biopsy, few studies have explored the factors that influence platelet isolation.Considering the importance of platelet isolation in tumor-based platelet liquid biopsy, our aim is to optimize platelet isolation conditions as much as possible to obtain a high platelet recovery rate.What is new? In this study, we conducted a prospective multicenter study ofhealthy adults from four centers, combining whole blood with platelet-richplasma to investigate factors influencing platelet recovery rate (PRR) during platelet isolation.In our study, we indicated that platelet isolation should be performed within two hours at room temperature, and that centrifuge models should be fixed during the extraction process, which will further improve the research progress of platelet-based liquid biopsy in cancer.What is the impact? In future platelet-related studies, we should fix the sample storage temperature, storage time and centrifuge model in the process of platelet extraction, so as to reduce the variables affecting platelet extraction as much as possible and ensure the stable recovery rate of platelet extraction.

90 K increased delivery efficiency of extracellular vesicles through mediating internalization.

Using mass spectrometry-based high-throughput proteomics, we identified a membrane protein on extracellular vesicles (EVs), 90 K, which predicts poor overall survival of patients with head and neck cancer. 90 K levels in serum EVs could serve as an independent factor for poor prognosis of patients with head and neck cancer. Pre-treatment of immune competent mice with tumor-derived EVs (TDEs) elicited an immune-suppressive microenvironment for tumor cells, which was regulated by 90 K. The immunosuppressive function of TDE-90 K depends on the presence of myeloid derived suppressor cells (MDSCs) rather than regulatory T cells. The immune regulatory role of TDEs on MDSCs depends on miR-21 which is encapsulated in TDEs. Moreover, 90 K is required for the internalization of TDE cargo though interacting with integrin-ß1 and anti-siglec-9 rather than directly affecting the immune function of MDSCs. 90 K modification of γδT cell-derived EVs (γδTEVs) could increase the delivery efficiency and therapeutic effect of PD-L1 siRNA by γδTEVs. We concluded that as a secreted protein modulating cell-cell and cell-matrix interactions, 90 K can be carried by TDEs to mediate the internalization and delivery of TDEs cargo by recipient cells. This function of 90 K could be utilized to improve the efficiency of EV-based drug delivery.

Prognostic value of nutritional and inflammatory indicators in females with esophageal squamous cell cancer: A propensity score matching study.

Background: Esophageal squamous cell cancer (ESCC) is a disease with a male predominance. Accordingly, the applicability of prognostic indicators values previously set for the general population with ESCC has not been reported for determining the physical state in females. Methods: Patients with ESCC were pooled from 2009 to 2017 at Sichuan Cancer Hospital. We determined the differences in the nutritional and inflammatory indicators between gender by sex-stratified survival analysis in all cohorts (n = 2,660) and matching cohorts (n = 483 pairs) separately. Propensity score matching (PSM) was employed to eliminate selection bias between genders. We further performed the prognostic value of total cholesterol (TC) by subgroup analysis in the female cohort. The area ROC curve was used to assess the predictive performance of TC in females. Results: There were a total of 2,660 patients with ESCC, of whom 2,173 (81.7%) were male and 487 (18.3%) were female. Before PSM, the prognostic nutritional index was an independent factor for OS in males but not in females. For cohort with or without matching, TC was an independent prognostic factor in females not for males. Furthermore, female patients with high TC level had significant poor OS in stages III and IV. The AUCs of TC were 0.63 and 0.70 for predicting 3- and 5-year OS, respectively. Conclusion: Based on a much larger cohort, we confirmed that gender was a significant prognostic factor for ESCC patients. Interestingly, we found a significant difference in TC related to ESCC prognosis between genders. Collectively, TC might be an independent prognostic factor in females with ESCC.

Comprehensive Analysis of the Molecular Characteristics and Prognosis value of AT II-associated Genes in Non-small Cell Lung Cancer.

Alveolar type II (AT II) is a key structure of the distal lung epithelium and essential to maintain normal lung homeostasis. Dedifferentiation of AT II cells is significantly correlated with lung tumor progression. However, the potential molecular mechanism and clinical significance of AT II-associated genes for lung cancer has not yet been fully elucidated. In this study, we comprehensively analyzed the gene expression, prognosis value, genetic alteration, and immune cell infiltration of eight AT II-associated genes (AQP4, SFTPB, SFTPC, SFTPD, CLDN18, FOXA2, NKX2-1, and PGC) in Nonsmall Cell Lung Cancer (NSCLC). The results have shown that the expression of eight genes were remarkably reduced in cancer tissues and observably relating to clinical cancer stages. Survival analysis of the eight genes revealed that low-expression of CLDN18, FOXA2, NKX2-1, PGC, SFTPB, SFTPC, and SFTPD were significantly related to a reduced progression-free survival (FP), and low CLDN18, FOXA2, and SFTPD mRNA expression led to a short postprogression survival (PPS). Meanwhile, the alteration of 8 AT II-associated genes covered 273 out of 1053 NSCLC samples (26%). Additionally, the expression level of eight genes were significantly correlated with the infiltration of diverse immune cells, including six types of CD4+T cells, macrophages, neutrophils, B cells, CD8+ T cells, and dendritic cells. In summary, this study provided clues of the values of eight AT II-associated genes as clinical biomarkers and therapeutic targets in NSCLC and might provide some new inspirations to assist the design of new immunotherapies.

Clinlabomics: leveraging clinical laboratory data by data mining strategies.

The recent global focus on big data in medicine has been associated with the rise of artificial intelligence (AI) in diagnosis and decision-making following recent advances in computer technology. Up to now, AI has been applied to various aspects of medicine, including disease diagnosis, surveillance, treatment, predicting future risk, targeted interventions and understanding of the disease. There have been plenty of successful examples in medicine of using big data, such as radiology and pathology, ophthalmology cardiology and surgery. Combining medicine and AI has become a powerful tool to change health care, and even to change the nature of disease screening in clinical diagnosis. As all we know, clinical laboratories produce large amounts of testing data every day and the clinical laboratory data combined with AI may establish a new diagnosis and treatment has attracted wide attention. At present, a new concept of radiomics has been created for imaging data combined with AI, but a new definition of clinical laboratory data combined with AI has lacked so that many studies in this field cannot be accurately classified. Therefore, we propose a new concept of clinical laboratory omics (Clinlabomics) by combining clinical laboratory medicine and AI. Clinlabomics can use high-throughput methods to extract large amounts of feature data from blood, body fluids, secretions, excreta, and cast clinical laboratory test data. Then using the data statistics, machine learning, and other methods to read more undiscovered information. In this review, we have summarized the application of clinical laboratory data combined with AI in medical fields. Undeniable, the application of Clinlabomics is a method that can assist many fields of medicine but still requires further validation in a multi-center environment and laboratory.

The Clinical Application Value of the Prognostic Nutritional Index for the Overall Survival Prognosis of Patients with Esophageal Cancer: A Robust Real-World Observational Study in China.

Esophageal cancer is a kind of cancer with high morbidity and mortality, which is accompanied by a profound poor prognosis. A prognostic nutritional index, based on serum albumin levels and peripheral lymphocyte count, has been confirmed to be significantly associated with various cancers. This study was aimed at exploring the prognostic significance of PNI in the overall survival prognosis of patients with esophageal cancer. As a real-world study based on the big database, clinical data of 2661 patients with esophageal cancer were evaluated retrospectively, and the individuals were randomly divided into training and testing cohorts. In these two cohorts, patients are classified into a high-risk group (PNI < 49) and a low-risk group (PNI ≥ 49). Univariate and multivariate analyses were performed to analyze the independent risk factors for the prognosis of esophageal cancer patients by using the Cox proportional hazards regression model. In this study, whether in the training cohort or the testing cohort, according to the univariate analysis, gender, tumor size, tumor grade, T stage, N stage, M stage, TNM stage, and PNI were significantly correlated with overall survival. Furthermore, the multivariate analysis showed that gender, T stage, N stage, M stage, TNM stage, and PNI were independent prognostic risk factors for esophageal cancer. PNI can be regarded as an independent prognostic factor combined with gender, T stage, N stage, M stage, and TNM stage, and it might be a novel reliable biomarker for esophageal cancer.

A novel prognostic model for malignant patients with Gram-negative bacteremia based on real-world research.

Gram-negative bacteremia (GNB) is a common complication in malignant patients. Identifying risk factors and developing a prognostic model for GNB might improve the survival rate. In this observational and real-world study, we retrospectively analyzed the risk factors and outcomes of GNB in malignant patients. Multivariable regression was used to identify risk factors for the incidence of GNB, while Cox regression analysis was performed to identify significant prognostic factors. A prognostic model was constructed based on Cox regression analysis and presented on a nomogram. ROC curves, calibration plots, and Kaplan-Meier analysis were used to estimate the model. It comprised 1004 malignant patients with Bloodstream infection (BSI) in the study cohort, 65.7% (N = 660) acquired GNB. Multivariate analysis showed gynecologic cancer, hepatobiliary cancer, and genitourinary cancer were independent risk factors related to the incidence of GNB. Cox regression analysis raised that shock, admission to ICU before infection, pulmonary infection, higher lymphocyte counts, and lower platelet counts were independent risk factors for overall survival (OS). The OS was significantly different between the two groups classified by optimal cut-off value (log-rank, p < 0.001). Above all, a nomogram was created based on the prognostic model, which was presented on a website freely. This real-world study was concentrated on the malignant patients with GNB and proved that shock, admission to ICU before infection, pulmonary infection, higher lymphocyte counts, and lower platelet counts were related to the death of these patients. And a prognostic model was constructed to estimate the risk score of mortality, further to reduce the risk of death.

Selection and Validation of Reference Genes for Pan-Cancer in Platelets Based on RNA-Sequence Data.

Many studies in recent years have demonstrated that some messenger RNA (mRNA) in platelets can be used as biomarkers for the diagnosis of pan-cancer. The quantitative real-time polymerase chain reaction (RT-qPCR) molecular technique is most commonly used to determine mRNA expression changes in platelets. Accurate and reliable relative RT-qPCR is highly dependent on reliable reference genes. However, there is no study to validate the reference gene in platelets for pan-cancer. Given that the expression of some commonly used reference genes is altered in certain conditions, selecting and verifying the most suitable reference gene for pan-cancer in platelets is necessary to diagnose early stage cancer. This study performed bioinformatics and functional analysis from the RNA-seq of platelets data set (GSE68086). We generated 95 candidate reference genes after the primary bioinformatics step. Seven reference genes (YWHAZ, GNAS, GAPDH, OAZ1, PTMA, B2M, and ACTB) were screened out among the 95 candidate reference genes from the data set of the platelets' transcriptome of pan-cancer and 73 commonly known reference genes. These candidate reference genes were verified by another platelets expression data set (GSE89843). Then, we used RT-qPCR to confirm the expression levels of these seven genes in pan-cancer patients and healthy individuals. These RT-qPCR results were analyzed using the internal stability analysis software programs (the comparative Delta CT method, geNorm, NormFinder, and BestKeeper) to rank the candidate genes in the order of decreasing stability. By contrast, the GAPDH gene was stably and constitutively expressed at high levels in all the tested samples. Therefore, GAPDH was recommended as the most suitable reference gene for platelet transcript analysis. In conclusion, our result may play an essential part in establishing a molecular diagnostic platform based on the platelets to diagnose pan-cancer.

A new classifier constructed with platelet features for malignant and benign pulmonary nodules based on prospective real-world data.

Objectives: As the pulmonary nodules were hard to be discriminated as benignancy or malignancy only based on imageology, a prospective and observational real-world research was devoted to develop and validate a predictive model for managing the diagnostic challenge. Methods: This study started in 2018, and a predictive model was constructed using eXtreme Gradient Boosting (XGBoost) based on computed tomographic, clinical, and platelet data of all the eligible patients. And the model was evaluated and compared with other common models using ROC curves, continuous net reclassification improvement (NRI), integrated discrimination improvement (IDI), and net benefit (NB). Subsequently, the model was validated in an external cohort. Results: The development group included 419 participants, while there were 62 participants in the external validation cohort. The most accurate XGBoost model called SCHC model including age, platelet counts in platelet rich plasma samples (pPLT), plateletcrit in platelet rich plasma samples (pPCT), nodule size, and plateletcrit in whole blood samples (bPCT). In the development group, the SCHC model performed well in whole group and subgroups. Compared with VA, MC, BU model, the SCHC model had a significant improvement in reclassification as assessed by the NRI and IDI, and could bring the patients more benefits. For the external validation, the model performed not as well. The algorithm of SCHC, VA, MC, and BU model were first integrated using a web tool (http://i.uestc.edu.cn/SCHC). Conclusions: In this study, a platelet feature-based model could facilitate the discrimination of early-stage malignancy from benignancy patients, to ensure accurate diagnosis and optimal management. This research also indicated that common laboratory results also had the potential in diagnosing cancers.