Integrating Single-Cell RNA-Seq and Bulk RNA-Seq Data to Explore the Key Role of Fatty Acid Metabolism in Breast Cancer.

Cancer immune escape is associated with the metabolic reprogramming of the various infiltrating cells in the tumor microenvironment (TME), and combining metabolic targets with immunotherapy shows great promise for improving clinical outcomes. Among all metabolic processes, lipid metabolism, especially fatty acid metabolism (FAM), plays a major role in cancer cell survival, migration, and proliferation. However, the mechanisms and functions of FAM in the tumor immune microenvironment remain poorly understood. We screened 309 fatty acid metabolism-related genes (FMGs) for differential expression, identifying 121 differentially expressed genes. Univariate Cox regression models in The Cancer Genome Atlas (TCGA) database were then utilized to identify the 15 FMGs associated with overall survival. We systematically evaluated the correlation between FMGs' modification patterns and the TME, prognosis, and immunotherapy. The FMGsScore was constructed to quantify the FMG modification patterns using principal component analysis. Three clusters based on FMGs were demonstrated in breast cancer, with three patterns of distinct immune cell infiltration and biological behavior. An FMGsScore signature was constructed to reveal that patients with a low FMGsScore had higher immune checkpoint expression, higher immune checkpoint inhibitor (ICI) scores, increased immune microenvironment infiltration, better survival advantage, and were more sensitive to immunotherapy than those with a high FMGsScore. Finally, the expression and function of the signature key gene NDUFAB1 were examined by in vitro experiments. This study significantly demonstrates the substantial impact of FMGs on the immune microenvironment of breast cancer, and that FMGsScores can be used to guide the prediction of immunotherapy efficacy in breast cancer patients. In vitro experiments, knockdown of the NDUFAB1 gene resulted in reduced proliferation and migration of MCF-7 and MDA-MB-231 cell lines.

Research progress on the correlation between intestinal flora and colorectal cancer.

Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies in the world. With the rapid pace of life and changes in diet structure, the incidence and mortality of CRC increase year by year posing a serious threat to human health. As the most complex and largest microecosystem in the human body, intestinal microecology is closely related to CRC. It is an important factor that affects and participates in the occurrence and development of CRC. Advances in next-generation sequencing technology and metagenomics have provided new insights into the ecology of gut microbes. It also helps to link intestinal flora with CRC, and the relationship between intestinal flora and CRC can be continuously understood from different levels. This paper summarizes the relationship between intestinal flora and CRC and its potential role in the diagnosis of CRC providing evidence for early screening and treatment of CRC.

Enhancing the diagnostic accuracy of colorectal cancer through the integration of serum tumor markers and hematological indicators with machine learning algorithms.

BACKGROUND: Colorectal cancer has a high incidence and mortality rate due to a low rate of early diagnosis. Therefore, efficient diagnostic methods are urgently needed. PURPOSE: This study assesses the diagnostic effectiveness of Carbohydrate Antigen 19-9 (CA19-9), Carcinoembryonic Antigen (CEA), Alpha-fetoprotein (AFP), and Cancer Antigen 125 (CA125) serum tumor markers for colorectal cancer (CRC) and investigates a machine learning-based diagnostic model incorporating these markers with blood biochemical indices for improved CRC detection. METHOD: Between January 2019 and December 2021, data from 800 CRC patients and 697 controls were collected; 52 patients and 63 controls attending the same hospital in 2022 were collected as an external validation set. Markers' effectiveness was analyzed individually and collectively, using metrics like ROC curve AUC and F1 score. Variables chosen through backward regression, including demographics and blood tests, were tested on six machine learning models using these metrics. RESULT: In the case group, the levels of CEA, CA199, and CA125 were found to be higher than those in the control group. Combining these with a fourth serum marker significantly improved predictive efficacy over using any single marker alone, achieving an Area Under the Curve (AUC) value of 0.801. Using stepwise regression (backward), 17 variables were meticulously selected for evaluation in six machine learning models. Among these models, the Gradient Boosting Machine (GBM) emerged as the top performer in the training set, test set, and external validation set, boasting an AUC value of over 0.9, indicating its superior predictive power. CONCLUSION: Machine learning models integrating tumor markers and blood indices offer superior CRC diagnostic accuracy, potentially enhancing clinical practice.

Application of machine learning algorithms to identify people with low bone density.

Background: Osteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis. Objective: This study aimed to develop a machine learning algorithm to effectively identify people with low bone density, using readily available demographic and blood biochemical data. Methods: Using NHANES 2017-2020 data, participants over 50 years old with complete femoral neck BMD data were selected. This cohort was randomly divided into training (70%) and test (30%) sets. Lasso regression selected variables for inclusion in six machine learning models built on the training data: logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), naive Bayes (NB), artificial neural network (ANN) and random forest (RF). NHANES data from the 2013-2014 cycle was used as an external validation set input into the models to verify their generalizability. Model discrimination was assessed via AUC, accuracy, sensitivity, specificity, precision and F1 score. Calibration curves evaluated goodness-of-fit. Decision curves determined clinical utility. The SHAP framework analyzed variable importance. Results: A total of 3,545 participants were included in the internal validation set of this study, of whom 1870 had normal bone density and 1,675 had low bone density Lasso regression selected 19 variables. In the test set, AUC was 0.785 (LR), 0.780 (SVM), 0.775 (GBM), 0.729 (NB), 0.771 (ANN), and 0.768 (RF). The LR model has the best discrimination and a better calibration curve fit, the best clinical net benefit for the decision curve, and it also reflects good predictive power in the external validation dataset The top variables in the LR model were: age, BMI, gender, creatine phosphokinase, total cholesterol and alkaline phosphatase. Conclusion: The machine learning model demonstrated effective classification of low BMD using blood biomarkers. This could aid clinical decision making for osteoporosis prevention and management.

Single-cell sequencing and bulk RNA data reveal the tumor microenvironment infiltration characteristics of disulfidptosis related genes in breast cancer.

BACKGROUND: Immunotherapy, represented by immune checkpoint inhibitors, has made significant progress in the treatment of cancer. Numerous studies have demonstrated that antitumor therapies targeting cell death exhibit synergistic effects with immunotherapy. Disulfidptosis is a recently discovered form of cell death, and its potential influence on immunotherapy, similar to other regulated cell death processes, requires further investigation. The prognostic value of disulfidptosis in breast cancer and its role in the immune microenvironment has not been investigated. METHODS: High dimensional weighted gene coexpression network analysis (hdWGCNA) and Weighted co-expression network analysis (WGCNA) methods were employed to integrate breast cancer single-cell sequencing data and bulk RNA data. These analyses aimed to identify genes associated with disulfidptosis in breast cancer. Risk assessment signature was constructed using Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses. RESULTS: In this study, we constructed a risk signature by disulfidptosis-related genes to predict overall survival and immunotherapy response in BRCA patients. The risk signature demonstrated robust prognostic power and accurately predicted survival compared to traditional clinicopathological features. It also effectively predicted the response to immunotherapy in patients with breast cancer. Through cell communication analysis in additional single-cell sequencing data, we identified TNFRSF14 as a key regulatory gene. Combining TNFRSF14 targeting and immune checkpoint inhibition to induce disulfidptosis in tumor cells could potentially suppress tumor proliferation and enhance survival in patients with BRCA.

Synaptic transistor with multiple biological functions based on metal-organic frameworks combined with the LIF model of a spiking neural network to recognize temporal information.

Spiking neural networks (SNNs) have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption. The leaky integration and firing (LIF) model and spike-timing-dependent plasticity (STDP) are the fundamental components of SNNs. Here, a neural device is first demonstrated by zeolitic imidazolate frameworks (ZIFs) as an essential part of the synaptic transistor to simulate SNNs. Significantly, three kinds of typical functions between neurons, the memory function achieved through the hippocampus, synaptic weight regulation and membrane potential triggered by ion migration, are effectively described through short-term memory/long-term memory (STM/LTM), long-term depression/long-term potentiation (LTD/LTP) and LIF, respectively. Furthermore, the update rule of iteration weight in the backpropagation based on the time interval between presynaptic and postsynaptic pulses is extracted and fitted from the STDP. In addition, the postsynaptic currents of the channel directly connect to the very large scale integration (VLSI) implementation of the LIF mode that can convert high-frequency information into spare pulses based on the threshold of membrane potential. The leaky integrator block, firing/detector block and frequency adaptation block instantaneously release the accumulated voltage to form pulses. Finally, we recode the steady-state visual evoked potentials (SSVEPs) belonging to the electroencephalogram (EEG) with filter characteristics of LIF. SNNs deeply fused by synaptic transistors are designed to recognize the 40 different frequencies of EEG and improve accuracy to 95.1%. This work represents an advanced contribution to brain-like chips and promotes the systematization and diversification of artificial intelligence.