Metastases account for the overwhelming majority of cancer-associated deaths. The dissemination of cancer cells from the primary tumor to distant organs involves a complex process known as the invasion-metastasis cascade. The underlying biological mechanisms of metastasis, however, remain largely elusive. Recently, the discovery and characterization of non-coding RNAs (ncRNAs) have revealed the diversity of their regulatory roles, especially as key contributors throughout the metastatic cascade. Here, we review recent progress in how three major types of ncRNAs (microRNAs, long non-coding RNAs, and circular RNAs) are involved in the multistep procedure of metastasis. We further examine interactions among the three ncRNAs as well as current progress in their regulatory mechanisms. We also propose the prevention of metastasis in the early stages of cancer progression and discuss current translational studies using ncRNAs as targets for metastasis diagnosis and treatments. These studies provide insights into developing more effective strategies to target metastatic relapse.
Gene Expression Regulation, Neoplastic/genetics* ; RNA, Untranslated/genetics* ; MicroRNAs ; RNA, Long Noncoding ; RNA, Circular/genetics*

Objective:To screen out the differentially regulated metabolites by the analysis of serum metabolic fingerprints, and to provide potential biomarkers for diagnosis of lung cancer.Methods:A total of 228 subjects were enrolled in Changhai Hospital from January 27, 2021 to June 4, 2021, including 97 newly diagnosed lung cancer patients and 131 healthy individuals. Serum samples were collected from the enrolled cohort according to a standard procedure, and the enrolled cohort was divided into a training set and a completely independent validation set by stratified random sampling. The metabolic fingerprints of serum samples were collected by previously developed nano-assisted laser desorption/ionization mass spectrometry (nano-LDI MS). After age and gender matching of the training set, a diagnostic model based on serum metabolic fingerprints was established by machine learning algorithm, and the classification performance of the model was evaluated by receiver operating characteristic (ROC) curve.Results:Serum metabolic fingerprint for each sample was obtained in 1 minute using a novel nano-LDI MS, with consumption of only 1 μl original serum sample. For the training set, the area under ROC curve (AUC) of the constructed classifier for diagnosis of lung cancer was 0.92 (95% CI 0.87-0.97), with a sensitivity of 89% and specificity of 89%. For the independent validation set, the AUC reached 0.96 (95% CI 0.90-1.00) with a sensitivity of 91% and specificity of 94%, which showed no significant decrease compared to training set. We also identified a biomarker panel of 5 metabolites, demonstrating a unique metabolic fingerprint of lung cancer patients. Conclusion:Serum metabolic fingerprints and machine learning were combined to establish a diagnostic model, which can be used to distinguish between lung cancer patients and healthy controls. This work sheds lights on the rapid metabolic analysis for clinical application towards in vitro diagnosis.

Objective:To evaluate the feasibility of a predictire model composed of non-specific test indexes in early diagnosis of gastric cancer.Methods:From the database of electronic medical record system of Shanghai Changhai Hospital, a total of 24 615 case records were included from January 1, 2010 to April 30, 2019, including 10 497 cases of gastric cancer, 5 198 cases of precancerous diseases, and 8 920 cases of health examination. Through stratified random sampling, the study population was divided into validation set, training set and test set. After data processing and quality control for all laboratory variables, the optimal machine learning algorithm and diagnostic efficiency grouping were selected through four machine learning algorithms, induding the gradient boosting decision tree, random forest, support vector machine, and artificial neural network, and the data were trained by backward stepwise regression method to build the best feature model.Result:In this study, a diagnostic model V22 consisting of 22 routine testing parameters was established. V22 could distinguish early gastric cancer from control group composed of healthy group and precancerous disease, AUC was 0.808, the sensitivity was 85.7%, and the specificity was 91.9%. For CEA negative gastric cancer, V22 also showed high diagnostic accuracy, AUC was 0.801.Conclusion:V22 was a valuable model for the diagnosis of gastric cancer. V22 was an auxiliary diagnostic model of gastric cancer with clinical application value, which could well distinguish early gastric cancer from the control group composed of healthy group and precancerous disease, and the detection rate of early gastric cancer was better than the traditional tumor marker CEA.

Research on the application of artificial intelligence in laboratory medicine has become an important direction for laboratory development. However, there were still problems in the process of product application research and development of artificial intelligence technology, such as lack of interpretability of machine learning models, lack of talent teams, and many potential safety hazards. The reason for this may include low quality of data sets, deviations in research design, imperfect talent training mechanism, and inadequate legislation and supervision. In response to these reasons, the article proposes countermeasures, including establishing data entry and collection standards, formulating data labeling management standards, making model risk analysis, strengthening compound talent training, and improving supervision and management systems. Ensuring artificial intelligence products applied in the field of laboratory medicine could effectively improve the quality of medical services on the premise of improving the efficiency of diagnosis and reducing the rate of misdiagnosis and missed diagnosis.

Objective:We aimed to explore a colorectal cancer risk prediction model through machine learning algorithm based on the big data in laboratory medicine.Methods:According to the labeling of colonoscopy combined with pathology or referring to the ICD-10 code, the colonoscopy patients in Shanghai Changhai Hospital from 2013.1.1 to 2019.6.30 and the outpatients and inpatients from 2010.1.1 to 2019.6.30 were divided into colorectal cancer groups and non-colorectal cancer group. Four machine learning algorithms, Extreme gradient boosting(Xgboost),Artificial Neural Network(ANN),Support Vector Machine(SVM),Random Forest(RF), are used to mine all routine laboratory test item data of the enrolled patients, select model features and establish a classification model for colorectal cancer. And the effectiveness of the model was prospectively verified in patients in the whole hospital of Changhai Hospital from 2019.7.1 to 2020.8.31.Result:A colorectal cancer risk prediction model (CRC-Lab7) including 7 characteristics of fecal occult blood, carcinoembryonic antigen, red blood cell distribution width, lymphocyte count, albumin/globulin, high-density lipoprotein cholesterol and hepatitis B virus core antibody was constructed by the XgBoost algorithm. The AUC of the model in the validation set and prospective validation set were 0.799 and 0.816, respectively, which was significantly higher than that of fecal occult blood (AUC was 0.68 and 0.706, respectively). It also has high diagnostic accuracy for colorectal cancer with negative fecal occult blood or under 50 years old.Conclusion:In this study, a colorectal cancer risk prediction model was established by mining routine laboratory big data. The model′s performance is better than fecal occult blood, and it has high diagnostic accuracy for colorectal cancer in patients with negative fecal occult blood and younger than 50 years old.

Real world study has attracted more and more researchers' attention because of the evidence obtained from real clinical practice. With the development of big data in laboratory medicine and the needs for translational studies for diagnostic markers, the concept of real world study has also been introduced in laboratory medicine. This article presents a preliminary discussion on the opportunities and requirements for the real world study in the development of laboratory medicine, and on how the researchers conduct real world study.

OBJECTIVE： To investigate and analyze the current status and existing problems of Chinese patent medicine instructions in outpatient department of our hospital， and to provide suggestions for the improvement of Chinese patent medicine instructions. METHODS： A total of 435 copies of Chinese patent medicines instructions using in the outpatient pharmacy of our hospital were collected in 2018. The labeling of usage and dosage of the instructions and other items were not clear and missing items were analyzed statistically. RESULTS： In 435 copies of drug instructions， unclear usage and dosage included usage and dosage were marked only in grams or milliliters （54 kinds， 12.4％）； daily dosage was not clear （165 kinds， 37.9％）； the words “or follow the doctor’s advice” were involved in drug instructions （86 kinds， 19.8％）； the labeling of usage and dosage for special population were not clear （34 kinds， 7.8％）； medication time was not labeled （365 kinds， 83.9％）. Unclear labeling of other items included unclear drug interaction （121 kinds， 27.8％）， unclear matters needing attention （12 kinds， 2.8％）， unclear ADR （307 kinds， 70.6％）， unclear contraindications （257 kinds， 59.1％） and unclear indications （1 kind， 0.2％）. The missing items included that drug dosage for special population （41 kinds， 94.5％）， pharmacological and toxicological items （305 kinds， 70.1％）， clinical trial data （395 kinds， 90.8％）， storage temperature label （377 kinds， 86.7％）. CONCLUSIONS： Missing items and unclear information on safe medication are common in Chinese patent medicine instructions， which need to be standardized and perfected in order to provide reference for rational drug use and guarantee the safety of drug use in patients.

The modern laboratory developed tests (LDTs) plays a very important role in the development of precision medicine and clinical laboratory diagnosis. The perfect LDTs inspection platform not only requires strict quality control system and management standards, but also requires technical support from high-end professional teams. By combing the development process of LDTs, this paper discusses the significance of developing LDTs in the context of precision medicine, the positive role played by the development of laboratory medicine and the cultivation of laboratory talents, and how to effectively develop LDTs.

There's a very complicated association between gut microbiota and diseases. With the development of metagenomics and bioinformatics, people have a new recognition of its role in pathogenesis, diagnosis and treatments. This review focuses on the close relationship between gut microbiota and liver diseases, analyzes and discusses the current research status, value of clinical applications and development directions. It is suggested that investigating the interactions between gut microbiota and body to discover new pathogenic pathway, maybe helpful in seeking for relevant drug targets and developing more sensitive early detection markers, then undoubtedly facilitate the clinical diagnosis and treatment of liver diseases.

Precision medicine is an emerging approach for disease treatment and prevention , which attempts to explore the effective means for protecting human health by synthetical consideration of individual variability in genes , environment and life style.Precision medicine has the prominent property of multidisciplinary intercrossing and fusion , the development of which claims rapid clinical application of advanced technology in the research of basic medical science.What kind of development opportunities are laboratory medicine confronted with under the novel medical mode? How can laboratory medicine and its researchers do to seize these opportunities and meet the challenges of precision medicine ? These questions are preliminary discussed in this paper.