Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study.

BACKGROUND: Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening. METHODS: Raman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls. RESULTS: The diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly. CONCLUSION: Therefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the SVM algorithm has great potential for the noninvasive detection of lung cancer.

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.

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.

Aspartate Transaminase-to-Albumin Ratio (ATAR), a Novel Prognostic Index, Predicts Outcomes in Patients with Small-Cell Lung Cancer.

BACKGROUND: Small cell lung cancer (SCLC) is characterized by high invasion rates, rapid progression, and poor prognoses. Thus, identifying SCLC patients at high risk of progression and death is critical to improve long-term survival. In this study, the aspartate transaminase-to-albumin ratio (ATAR) was examined as a prognostic factor for SCLC patients. METHODS: We screened 196 SCLC patients from December 2013 to September 2022 at the Sichuan Cancer Hospital. The data was collected from patients' medical information as well as from their blood results during diagnosis. Using the Youden index as a cutoff value, patients were divided into high-risk（> 0.54) and low-risk (≤ 0.54) ATAR groups. We analyzed the prognostic factors for overall survival (OS) using the Kaplan-Meier method, univariate and multivariate analyses, Cox regression, and the C-index. RESULTS: There were 109 (55.6%) smokers among the patients, and the median OS was 17.55 months. The Kaplan-Meier analysis indicated that patients with high-risk ATAR had significantly lower OS (p < 0.0001). A multivariate analysis demonstrated that elevated ATAR is an independent adverse predictor of OS (p < 0.001, HR = 1.907). Our study found that ATAR is an independent predictor of survival outcomes in SCLC, which was superior to ALB, PNI, and SII in predicting outcomes in low-risk and high-risk groups (all p < 0.05). Models combining ATAR with ALB, PNI, and SII showed more powerful prognostic value than their corresponding original models. Moreover, the prognostic indicator ATAR can significantly stratify stage I - II and III - IV SCLC patients (p ＜ 0.05). CONCLUSIONS: Peripheral blood ATAR prognostic index can be used as an independent predictor of SCLC patients before treatment.

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.

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.

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.

CircSEC24A promotes tumor progression through sequestering miR-455-3p in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) ranks third in the cause of death due to cancer. Circular RNA circSEC24 Homolog A (circSEC24A) has been uncovered to be upregulated in liver cancer. However, the function of circSEC24A in HCC is indistinct. We analyzed the microarray datasets GSE78520 and GSE94508 to search for differentially expressed circRNAs associated with HCC. Expression of circSEC24A, microRNA (miR)-455-3p, and protein phosphatase, Mg2+/Mn2+ dependent 1F (PPM1F) mRNA was detected by quantitative real-time polymerase chain reaction (RT-qPCR). Loss-of-function experiments were conducted to validate the biological function of circSEC24A in HCC cells in vitro and in vivo. Protein levels were evaluated by western blotting and immunohistochemistry (IHC). The relationship between circSEC24A or PPM1F and miR-455-3p was verified by a dual-luciferase reporter and/or RNA immunoprecipitation (RIP) assays. circSEC24A was overexpressed in HCC. circSEC24A silencing decreased xenograft tumor growth in vivo and repressed proliferation, metastasis, invasion, epithelial-to-mesenchymal transition (EMT), induced cell cycle arrest, and apoptosis of HCC cells in vitro. circSEC24A acted as a molecular sponge to sequester miR-455-3p, resulting in elevating the expression of PPM1F. miR-455-3p inhibitor reversed the suppressive impact of circSEC24A silencing on malignant behaviors of HCC cells. PPM1F overexpression offsets the inhibitory effect of miR-455-3p mimic on malignant behaviors of HCC cells. circSEC24A sponged miR-455-3p to elevate the PPM1F expression, resulting in accelerating malignant behaviors of HCC cells. The study provided a potential therapeutic target for patients with HCC.

Normal reference intervals of prognostic nutritional index in healthy adults: A large multi-center observational study from Western China.

BACKGROUND: It has been widely reported that the prognostic nutritional index (PNI) played a pivotal role in nutritional assessment of surgical patients and tumor prognosis. In order to improve the accuracy of evaluation in Western China, we established reference intervals (RIs) of PNI in healthy controls. METHODS: A retrospective cohort study on healthy ethnic Han adults (18-79 years) was conducted to explore the influences of age, gender, study centers, and instruments on PNI and to establish RIs. The data came from a healthy routine examination center database and laboratory information system (LIS) of four centers in Western China, and there were 200 persons selected randomly for verification of RIs. RESULTS: Five thousand eight hundred and thirty-nine healthy candidates were enrolled. PNI showed a marked gender dependence, and males had significantly higher PNI than females across all ages (p < 0.01). We found that PNI is significantly different between age groups (p < 0.01), the value of PNI tended to decrease with age increasing. There is also an obvious influence of centers and instruments on PNI (p < 0.01). CONCLUSIONS: We established reference intervals of PNI in healthy Han Chinese population in Western China and validated successfully. Further established RIs will lead to better standardizations of PNI for clinical applications.

[Clinical Characteristics and Risk Factors of ESKAPE Pathogens Bloodstream Infection in Cancer Patients].

OBJECTIVE: To identify the risk factors of ESKAPE pathogens infection and related death in cancer patients, and to supply evidence for clinical precaution and diagnosis. METHODS: A retrospective study of clinical and experimental data of cancer patients with bloodstream infection were carried out in Sichuan Cancer Hospital from 2013 to 2018. The clinical feature, predisposing factors and risk factors of death in ESKAPE group and non-ESKAPE group were analyzed by univariate analysis and multivariate logistic regression. RESULTS: A total of 753 patients were enrolled in the study. Totally 795 pathogenic bacteria strains were isolated from blood culture and there were 278 ESKAPE strains, which took up 34.97% of isolated strains. Univariate analysis and multivariate logistic regression analysis showed that gender of male, multiple pathogens, history of exposure to enzyme inhibitors and agranulocytosis were independent risk factors of ESKAPE pathogens bloodstream infection. Peritoneal infection and combined fungal infection were independent risk factors of ESKAPE bloodstream infection related death. CONCLUSION: The bloodstream infection of ESKAPE pathogens is a problem worthy of clinical attention for cancer patients with neutrophil deficiency, previous antibiotic exposure, and fungal infection and peritoneal infection.

Normal Reference Intervals of Neutrophil-To-Lymphocyte Ratio, Platelet-To-Lymphocyte Ratio, Lymphocyte-To-Monocyte Ratio, and Systemic Immune Inflammation Index in Healthy Adults: a Large Multi-Center Study from Western China.

BACKGROUND: Numerous studies have shown that the hematological components of the systemic inflammatory response, including the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR), the lymphocyte-to-monocyte ratio (LMR), and the systemic immune inflammation index (SII) are efficient prognostic indicators in patients with cancers. Most of the studies did not investigate the reference intervals (RIs) of these parameters in healthy controls. METHODS: A retrospective cohort study was performed on healthy ethnic Han population aged between 18 and 79 years of age by retrieving the data from a healthy routine examination center database and laboratory infor-mation system of four participating centers in western China. By following the Clinical and Laboratory Standards Institute (CLSI), RIs of each parameter was established and validated. RESULTS: The analysis included 5,969 healthy subjects. We found that the individual's gender can significantly influence PLR, LMR, and SII (all p < 0.05), but not NLR (p > 0.05). Surprisingly, we also found that with an increase in age, the PLR, LMR, and SII tend to decrease, while NLR remained stable. PLR, LMR, and SII values were significantly higher in the young adults (18 - 64 years) than in old adults (65 - 79 years) (p < 0.001). The RIs of NLR, PLR (adults), PLR (old adults), LMR and SII were 0.88 - 4.0, 49 - 198, 42 - 187, 2.63 - 9.9, 142 x 109/L - 804 x 109/L, respectively. CONCLUSIONS: Our study addresses possible variations and establishes consensus for the NLR, PLR, LMR, and SII RIs for healthy Han Chinese adults in western China. Further, established RIs can standardize clinical applications and promote the use of these indicators into the routine complete blood count report.

Dexamethasone Stimulates Hepatitis B Virus (HBV) Replication Through Autophagy.

BACKGROUND Reactivation of hepatitis B virus (HBV) is a fatal complication of chemotherapy. Occult HBV infection might be reactivated in patients undergoing chemotherapy or immunosuppression. However, the mechanism of HBV reactivation induced by chemotherapy or immunosuppression remains unclear. MATERIAL AND METHODS HepG2.2.15 cells were treated with an autophagy inducer (rapamycin), an inhibitor (3-methyladenine, 3-MA), and dexamethasone. Autophagosomes were observed by a transmission electron microscope (TEM). LC3-I, LC3-II, and P62 were analyzed by western blot. HBV replicative intermediates were detected by southern blot. HBV DNA expression was quantitated with real-time polymerase chain reaction (PCR). The level of HBV surface antigen (HBsAg) in culture medium was examined by ELISA. RESULTS In this study, we find that dexamethasone stimulates HBV replication and protein expression by inducing autophagy in HepG2.2.15 cells. In contrast, autophagy inhibitor (3-MA) abrogates HBsAg secretion stimulated by dexamethasone. CONCLUSIONS Our results suggest that dexamethasone stimulates HBV replication through autophagy. This might provide a novel insight into the mechanism of glucocorticoid-mediated HBV reactivation through autophagy, which might be a new therapeutic target.

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.

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.

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.

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.

A clinical-information-free method for early diagnosis of lung cancer from the patients with pulmonary nodules based on backpropagation neural network model.

Lung cancer is the main cause of cancer-related deaths worldwide. Due to lack of obvious clinical symptoms in the early stage of the lung cancer, it is hard to distinguish between malignancy and pulmonary nodules. Understanding the immune responses in the early stage of malignant lung cancer patients may provide new insights for diagnosis. Here, using high-through-put sequencing, we obtained the TCRß repertoires in the peripheral blood of 100 patients with Stage I lung cancer and 99 patients with benign pulmonary nodules. Our analysis revealed that the usage frequencies of TRBV, TRBJ genes, and V-J pairs and TCR diversities indicated by D50s, Shannon indexes, Simpson indexes, and the frequencies of the largest TCR clone in the malignant samples were significantly different from those in the benign samples. Furthermore, reduced TCR diversities were correlated with the size of pulmonary nodules. Moreover, we built a backpropagation neural network model with no clinical information to identify lung cancer cases from patients with pulmonary nodules using 15 characteristic TCR clones. Based on the model, we have created a web server named "Lung Cancer Prediction" (LCP), which can be accessed at http://i.uestc.edu.cn/LCP/index.html.

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.

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.