BACKGROUND: Emerged coronavirus disease 2019 (COVID-19) is a pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Disease severity is associated with elevated levels of proinflammatory cytokines, such as interleukin-6 (IL-6). Genetic polymorphisms in the regulatory regions of cytokine genes may be associated with differential cytokine production in COVID-19 patients. This study aimed to investigate the association between three potentially functional single-nucleotide polymorphisms (SNPs) in the promoter region of IL-6 and the severity of susceptibility to COVID-19 in an Iranian population.METHODS: In total, 346 individuals (175 patients with severe COVID-19 and 171 patients with mild COVID-19) were recruited for this cohort study. Genomic DNA was extracted from peripheral blood leukocytes of patients to determine the genotypes of three selected SNPs (rs1800795 (-174 G > C), rs1800796 (-572 G > C), and rs1800797 (-597 G > A)) in the promoter region of the IL-6 gene using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method.RESULTS: There were no significant differences in the genotype or allele distribution of selected SNPs (rs1800795 (-174 G > C), rs1800796 (-572 G > C), and rs1800797 (-597 G > A)) in the promoter region of the IL-6 gene in patients with severe COVID-19 and patients with mild COVID-19.DISCUSSION: Our study indicated that these SNPs are not associated with COVID-19 severity in the Kurdish population from Kermanshah, Iran. <<<

Pingting Ying, Can Chen, Zequn Lu, Shuoni Chen, Ming Zhang, Yimin Cai, Fuwei Zhang, Jinyu Huang, Linyun Fan, Caibo Ning, Yanmin Li, Wenzhuo Wang, Hui Geng, Yizhuo Liu, Wen Tian, Zhiyong Yang, Jiuyang Liu, Chaoqun Huang, Xiaojun Yang, Bin Xu, Heng Li, Xu Zhu, Ni Li, Bin Li, Yongchang Wei, Ying Zhu, Jianbo Tian, Xiaoping Miao <<<

Genome-wide association studies have identified numerous variants associated with human complex traits, most of which reside in the non-coding regions, but biological mechanisms remain unclear. However, assigning function to the non-coding elements is still challenging. Here we apply Activity-by-Contact (ABC) model to evaluate enhancer-gene regulation effect by integrating multi-omics data and identified 544,849 connections across 20 cancer types. ABC model outperforms previous approaches in linking regulatory variants to target genes. Furthermore, we identify over 30,000 enhancer-gene connections in colorectal cancer (CRC) tissues. By integrating large-scale population cohorts (23,813 cases and 29,973 controls) and multipronged functional assays, we demonstrate an ABC regulatory variant rs4810856 associated with CRC risk (Odds Ratio = 1.11, 95%CI = 1.05-1.16, P = 4.02 × 10) by acting as an allele-specific enhancer to distally facilitate PREX1, CSE1L and STAU1 expression, which synergistically activate p-AKT signaling. Our study provides comprehensive regulation maps and illuminates a single variant regulating multiple genes, providing insights into cancer etiology. <<<

Daniel Levine, Syed Asad Rizvi, Sacha Lévy, Nazreen Pallikkavaliyaveetil, Ruiming Wu, Zihe Zheng, Antonio Oliveira Fonseca, Xingyu Chen, Sina Ghadermarzi, Rahul M. Dhodapkar, David van Dijk <<<

AbstractLarge language models like GPT have shown impressive performance on natural language tasks. Here, we present a novel method to directly adapt these pretrained models to a biological context, specifically single-cell transcriptomics, by representing gene expression data as text. Our Cell2Sentence approach converts each cell’s gene expression profile into a sequence of gene names ordered by expression level. We show that these gene sequences, which we term “cell sentences”, can be used to fine-tune causal language models like GPT-2. Critically, we find that natural language pretraining boosts model performance on cell sentence tasks. When fine-tuned on cell sentences, GPT-2 generates biologically valid cells when prompted with a cell type. Conversely, it can also accurately predict cell type labels when prompted with cell sentences. This demonstrates that language models fine-tuned using Cell2Sentence can gain a biological understanding of single-cell data, while retaining their ability to generate text. Our approach provides a simple, adaptable framework to combine natural language and transcriptomics using existing models and libraries. Our code is available at:https://github.com/vandijklab/cell2sentence-ft. <<<

BACKGROUND: Comparative gene expression studies in apes are fundamentally limited by the challenges associated with sampling across different tissues. Here, we used single-cell RNA sequencing of embryoid bodies to collect transcriptomic data from over 70 cell types in three humans and three chimpanzees.RESULTS: We find hundreds of genes whose regulation is conserved across cell types, as well as genes whose regulation likely evolves under directional selection in one or a handful of cell types. Using embryoid bodies from a human-chimpanzee fused cell line, we also infer the proportion of inter-species regulatory differences due to changes in cis and trans elements between the species. Using the cis/trans inference and an analysis of transcription factor binding sites, we identify dozens of transcription factors whose inter-species differences in expression are affecting expression differences between humans and chimpanzees in hundreds of target genes.CONCLUSIONS: Here, we present the most comprehensive dataset of comparative gene expression from humans and chimpanzees to date, including a catalog of regulatory mechanisms associated with inter-species differences. <<<

Reinforcement learning (RL) models have been influential in characterizing human learning and decision making, but few studies apply them to characterizing human spatial navigation and even fewer systematically compare RL models under different navigation requirements. Because RL can characterize one's learning strategies quantitatively and in a continuous manner, and one's consistency of using such strategies, it can provide a novel and important perspective for understanding the marked individual differences in human navigation and disentangle navigation strategies from navigation performance. One-hundred and fourteen participants completed wayfinding tasks in a virtual environment where different phases manipulated navigation requirements. We compared performance of five RL models (3 model-free, 1 model-based and 1 "hybrid") at fitting navigation behaviors in different phases. Supporting implications from prior literature, the hybrid model provided the best fit regardless of navigation requirements, suggesting the majority of participants rely on a blend of model-free (route-following) and model-based (cognitive mapping) learning in such navigation scenarios. Furthermore, consistent with a key prediction, there was a correlation in the hybrid model between the weight on model-based learning (i.e., navigation strategy) and the navigator's exploration vs. exploitation tendency (i.e., consistency of using such navigation strategy), which was modulated by navigation task requirements. Together, we not only show how computational findings from RL align with the spatial navigation literature, but also reveal how the relationship between navigation strategy and a person's consistency using such strategies changes as navigation requirements change. <<<

Zilong Zeng, Jiaying Zhang, Xinyuan Liang, Lianglong Sun, Yihe Zhang, Weiwei Men, Yanpei Wang, Rui Chen, Haibo Zhang, Shuping Tan, Jia-Hong Gao, Shaozheng Qin, Qiqi Tong, Hongjian He, Sha Tao, Qi Dong, Yong He, Tengda Zhao <<<

AbstractSusceptibility artifacts (SAs), which are inevitable for modern diffusion brain MR images with single-shot echo planar imaging (EPI) protocols in wide large-scale neuroimaging datasets, severely hamper the accurate detection of the human brain white matter structure. While several conventional and deep-learning based distortion correction methods have been proposed, the correction quality and model generality of these approaches are still limited. Here, we proposed the SACNet, a flexible SAs correction (SAC) framework for brain diffusion MR images of various phase-encoding EPI protocols based on an unsupervised learning-based registration convolutional neural network. This method could generate smooth diffeomorphic warps with optional neuroanatomy guidance to correct both geometric and intensity distortions of SAs. By employing near 2000 brain scans covering neonatal, child, adult and traveling participants, our SACNet consistently demonstrates state-of-the-art correction performance and effectively eliminates SAs-related multicenter effects compared with existing SAC methods. To facilitate the development of standard SAC tools for future neuroimaging studies, we also created easy-to-use command lines incorporating containerization techniques for quick user deployment. <<<

Anatoly Buchin, Rebecca de Frates, Anirban Nandi, Rusty Mann, Peter Chong, Lindsay Ng, Jeremy Miller, Rebecca Hodge, Brian Kalmbach, Soumita Bose, Ueli Rutishauser, Stephen McConoughey, Ed Lein, Jim Berg, Staci Sorensen, Ryder Gwinn, Christof Koch, Jonathan Ting, Costas A Anastassiou <<<

Temporal lobe epilepsy is the fourth most common neurological disorder, with about 40% of patients not responding to pharmacological treatment. Increased cellular loss is linked to disease severity and pathological phenotypes such as heightened seizure propensity. While the hippocampus is the target of therapeutic interventions, the impact of the disease at the cellular level remains unclear. Here, we show that hippocampal granule cells change with disease progression as measured in living, resected hippocampal tissue excised from patients with epilepsy. We show that granule cells increase excitability and shorten response latency while also enlarging in cellular volume and spine density. Single-nucleus RNA sequencing combined with simulations ascribes the changes to three conductances: BK, Cav2.2, and Kir2.1. In a network model, we show that these changes related to disease progression bring the circuit into a more excitable state, while reversing them produces a less excitable, "early-disease-like" state. <<<

Heiko Schuster, Wenguang Shao, Tobias Weiss, Patrick G A Pedrioli, Patrick Roth, Michael Weller, David S Campbell, Eric W Deutsch, Robert L Moritz, Oliver Planz, Hans-Georg Rammensee, Ruedi Aebersold, Etienne Caron <<<

The large array of peptides presented to CD8+ T cells by major histocompatibility complex (MHC) class I molecules is referred to as the MHC class I immunopeptidome. Although the MHC class I immunopeptidome is ubiquitous in mammals and represents a critical component of the immune system, very little is known, in any species, about its composition across most tissues and organs in vivo. We applied mass spectrometry (MS) technologies to draft the first tissue-based atlas of the murine MHC class I immunopeptidome in health. Peptides were extracted from 19 normal tissues from C57BL/6 mice and prepared for MS injections, resulting in a total number of 28,448 high-confidence H2D/K-associated peptides identified and annotated in the atlas. This atlas provides initial qualitative data to explore the tissue-specificity of the immunopeptidome and serves as a guide to identify potential tumor-associated antigens from various cancer models. Our data were shared via PRIDE (PXD008733), SysteMHC Atlas (SYSMHC00018) and SWATH Atlas. We anticipate that this unique dataset will be expanded in the future and will find wide applications in basic and translational immunology. <<<

Traditional diagnostic formulations of psychotic disorders have low correspondence with underlying disease neurobiology. This has led to a growing interest in using brain-based biomarkers to capture biologically-informed psychosis constructs. Building upon our prior work on the B-SNIP Psychosis Biotypes, we aimed to examine whether structural MRI (an independent biomarker not used in the Biotype development) can effectively classify the Biotypes. Whole brain voxel-wise grey matter density (GMD) maps from T1-weighted images were used to train and test (using repeated randomized train/test splits) binary L2-penalized logistic regression models to discriminate psychosis cases (n = 557) from healthy controls (CON, n = 251). A total of six models were evaluated across two psychosis categorization schemes: (i) three Biotypes (B1, B2, B3) and (ii) three DSM diagnoses (schizophrenia (SZ), schizoaffective (SAD) and bipolar (BD) disorders). Above-chance classification accuracies were observed in all Biotype (B1 = 0.70, B2 = 0.65, and B3 = 0.56) and diagnosis (SZ = 0.64, SAD = 0.64, and BD = 0.59) models. However, the only model that showed evidence of specificity was B1, i.e., the model was able to discriminate B1 vs. CON and did not misclassify other psychosis cases (B2 or B3) as B1 at rates above nominal chance. The GMD-based classifier evidence for B1 showed a negative association with an estimate of premorbid general intellectual ability, regardless of group membership, i.e. psychosis or CON. Our findings indicate that, complimentary to clinical diagnoses, the B-SNIP Psychosis Biotypes may offer a promising approach to capture specific aspects of psychosis neurobiology. <<<

Jun Li, Melissa J Hubisz, Ethan M Earlie, Mercedes A Duran, Christy Hong, Austin A Varela, Emanuele Lettera, Matthew Deyell, Bernardo Tavora, Jonathan J Havel, Su M Phyu, Amit Dipak Amin, Karolina Budre, Erina Kamiya, Julie-Ann Cavallo, Christopher Garris, Simon Powell, Jorge S Reis-Filho, Hannah Wen, Sarah Bettigole, Atif J Khan, Benjamin Izar, Eileen E Parkes, Ashley M Laughney, Samuel F Bakhoum <<<

Chromosomal instability (CIN) is a driver of cancer metastasis, yet the extent to which this effect depends on the immune system remains unknown. Using ContactTracing-a newly developed, validated and benchmarked tool to infer the nature and conditional dependence of cell-cell interactions from single-cell transcriptomic data-we show that CIN-induced chronic activation of the cGAS-STING pathway promotes downstream signal re-wiring in cancer cells, leading to a pro-metastatic tumour microenvironment. This re-wiring is manifested by type I interferon tachyphylaxis selectively downstream of STING and a corresponding increase in cancer cell-derived endoplasmic reticulum (ER) stress response. Reversal of CIN, depletion of cancer cell STING or inhibition of ER stress response signalling abrogates CIN-dependent effects on the tumour microenvironment and suppresses metastasis in immune competent, but not severely immune compromised, settings. Treatment with STING inhibitors reduces CIN-driven metastasis in melanoma, breast and colorectal cancers in a manner dependent on tumour cell-intrinsic STING. Finally, we show that CIN and pervasive cGAS activation in micronuclei are associated with ER stress signalling, immune suppression and metastasis in human triple-negative breast cancer, highlighting a viable strategy to identify and therapeutically intervene in tumours spurred by CIN-induced inflammation. <<<

Ana Luísa Correia, Joao C Guimaraes, Priska Auf der Maur, Duvini De Silva, Marcel P Trefny, Ryoko Okamoto, Sandro Bruno, Alexander Schmidt, Kirsten Mertz, Katrin Volkmann, Luigi Terracciano, Alfred Zippelius, Marcus Vetter, Christian Kurzeder, Walter Paul Weber, Mohamed Bentires-Alj <<<

The persistence of undetectable disseminated tumour cells (DTCs) after primary tumour resection poses a major challenge to effective cancer treatment. These enduring dormant DTCs are seeds of future metastases, and the mechanisms that switch them from dormancy to outgrowth require definition. Because cancer dormancy provides a unique therapeutic window for preventing metastatic disease, a comprehensive understanding of the distribution, composition and dynamics of reservoirs of dormant DTCs is imperative. Here we show that different tissue-specific microenvironments restrain or allow the progression of breast cancer in the liver-a frequent site of metastasis that is often associated with a poor prognosis. Using mouse models, we show that there is a selective increase in natural killer (NK) cells in the dormant milieu. Adjuvant interleukin-15-based immunotherapy ensures an abundant pool of NK cells that sustains dormancy through interferon-γ signalling, thereby preventing hepatic metastases and prolonging survival. Exit from dormancy follows a marked contraction of the NK cell compartment and the concurrent accumulation of activated hepatic stellate cells (aHSCs). Our proteomics studies on liver co-cultures implicate the aHSC-secreted chemokine CXCL12 in the induction of NK cell quiescence through its cognate receptor CXCR4. CXCL12 expression and aHSC abundance are closely correlated in patients with liver metastases. Our data identify the interplay between NK cells and aHSCs as a master switch of cancer dormancy, and suggest that therapies aimed at normalizing the NK cell pool might succeed in preventing metastatic outgrowth. <<<

Dynamic feature selection, where we sequentially query features to make accurate predictions with a minimal budget, is a promising paradigm to reduce feature acquisition costs and provide transparency into the prediction process. The problem is challenging, however, as it requires both making predictions with arbitrary feature sets and learning a policy to identify the most valuable selections. Here, we take an information-theoretic perspective and prioritize features based on their mutual information with the response variable. The main challenge is learning this selection policy, and we design a straightforward new modeling approach that estimates the mutual information in a discriminative rather than generative fashion. Building on our learning approach, we introduce several further improvements: allowing variable feature budgets across samples, enabling non-uniform costs between features, incorporating prior information, and exploring modern architectures to handle partial input information. We find that our method provides consistent gains over recent state-of-the-art methods across a variety of datasets. <<<

Knowledge of the human microbiome, which is likely a critical factor in the initiation, progression, and prognosis of multiple forms of cancer, is rapidly expanding. In this review, we focus on recent investigations to discern putative, causative microbial species and the microbiome composition and structure currently associated with procarcinogenesis and tumorigenesis at select body sites. We specifically highlight forms of cancer, gastrointestinal and nongastrointestinal, that have significant bacterial associations and well-defined experimental evidence with the aim of generating directions for future experimental and translational investigations to develop a clearer understanding of the multifaceted mechanisms by which microbiota affect cancer formation. SIGNIFICANCE: Emerging and, for some cancers, strong experimental and translational data support the contribution of the microbiome to cancer biology and disease progression. Disrupting microbiome features and pathways contributing to cancer may provide new approaches to improving cancer outcomes in patients. <<<

Hong Yang , Xiao Chen , Zuo-Bing Chen , Le Li , Xue-Ying Li , Francisco Xavier Castellanos , Tong-Jian Bai , Qi-Jing Bo , Jun Cao , Zhi-Kai Chang , Guan-Mao Chen , Ning-Xuan Chen , Wei Chen , Chang Cheng , Yu-Qi Cheng , Xi-Long Cui , Jia Duan , Yiru Fang , Qi-Yong Gong , Wen-Bin Guo , Zheng-Hua Hou , Lan Hu , Li Kuang , Feng Li , Hui-Xian Li , Kai-Ming Li , Tao Li , Yan-Song Liu , Zhe-Ning Liu , Yi-Cheng Long , Bin Lu , Qing-Hua Luo , Hua-Qing Meng , Daihui Peng , Hai-Tang Qiu , Jiang Qiu , Yue-Di Shen , Yu-Shu Shi , Tian-Mei Si , Yan-Qing Tang , Chuan-Yue Wang , Fei Wang , Kai Wang , Li Wang , Xiang Wang , Ying Wang , Yu-Wei Wang , Xiao-Ping Wu , Xin-Ran Wu , Chun-Ming Xie , Guang-Rong Xie , Hai-Yan Xie , Peng Xie , Xiu-Feng Xu , Jian Yang , Jia-Shu Yao , Shu-Qiao Yao , Ying-Ying Yin , Yong-Gui Yuan , Yu-Feng Zang , Ai-Xia Zhang , Hong Zhang , Ke-Rang Zhang , Lei Zhang , Zhi-Jun Zhang , Jing-Ping Zhao , Rubai Zhou , Yi-Ting Zhou , Jun-Juan Zhu , Zhi-Chen Zhu , Chao-Jie Zou , Xi-Nian Zuo , Chao-Gan Yan <<<

AbstractAberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks. <<<

Events associated with aversive or rewarding outcomes are prioritized in memory. This memory boost is commonly attributed to the elicited affective response, closely linked to noradrenergic and dopaminergic modulation of hippocampal plasticity. Herein we review and compare this 'affect' mechanism to an additional, recently discovered, 'prediction' mechanism whereby memories are strengthened by the extent to which outcomes deviate from expectations, that is, by prediction errors (PEs). The mnemonic impact of PEs is separate from the affective outcome itself and has a distinct neural signature. While both routes enhance memory, these mechanisms are linked to different - and sometimes opposing - predictions for memory integration. We discuss new findings that highlight mechanisms by which emotional events strengthen, integrate, and segment memory. <<<

AbstractThe infant brain develops rapidly and this area of research has great clinical implications. Neurodevelopmental disorders such as autism and developmental delay have their origins, potentially, in abnormal early brain maturation. Searching for potential early neural markers requiresa prioriknowledge about infant brain development and anatomy. One of the most common methods of characterizing brain features requires normalization of individual images into a standard stereotactic space and conduct of group-based analyses in this space. A population representative brain template is critical for these population-based studies. Little research is available on constructing brain templates for typical developing Chinese infants. In the present work, a total of 112 babies from 6 to 98 days of age were included with high resolution structural magnetic resonance imaging scans. T1-weighted and T2-weighted templates were constructed using an unbiased registration approach for babies from newborn to 3 months of age. Age-specific templates were also estimated for babies aged at 0, 1, 2 and 3 months old. Then we conducted a series of evaluations and statistical analyses over whole tissue segmentations and brain parcellations. Compared to the use of population mismatched templates, using our established templates resulted in lower deformation energy to transform individual images into the template space and produced a smaller registration error, i.e., smaller standard deviation of the registered images. Significant volumetric growth was observed across total brain tissues and most of the brain regions within the first three months of age. The total brain tissues exhibited larger volumes in baby boys compared to baby girls. To the best of our knowledge, this is the first study focusing on the construction of Chinese infant brain templates. These templates can be used for investigating birth related conditions such as preterm birth, detecting neural biomarkers for neurological and neurodevelopmental disorders in Chinese populations, and exploring genetic and cultural effects on the brain. <<<

Noninvasively mapping the layout of cortical areas in humans is a continuing challenge for neuroscience. We present a new method of mapping cortical areas based on myelin content as revealed by T1-weighted (T1w) and T2-weighted (T2w) MRI. The method is generalizable across different 3T scanners and pulse sequences. We use the ratio of T1w/T2w image intensities to eliminate the MR-related image intensity bias and enhance the contrast to noise ratio for myelin. Data from each subject were mapped to the cortical surface and aligned across individuals using surface-based registration. The spatial gradient of the group average myelin map provides an observer-independent measure of sharp transitions in myelin content across the surface--i.e., putative cortical areal borders. We found excellent agreement between the gradients of the myelin maps and the gradients of published probabilistic cytoarchitectonically defined cortical areas that were registered to the same surface-based atlas. For other cortical regions, we used published anatomical and functional information to make putative identifications of dozens of cortical areas or candidate areas. In general, primary and early unimodal association cortices are heavily myelinated and higher, multimodal, association cortices are more lightly myelinated, but there are notable exceptions in the literature that are confirmed by our results. The overall pattern in the myelin maps also has important correlations with the developmental onset of subcortical white matter myelination, evolutionary cortical areal expansion in humans compared with macaques, postnatal cortical expansion in humans, and maps of neuronal density in non-human primates. <<<

Deep learning in computer vision has achieved great success with the price of large-scale labeled training data. However, exhaustive data annotation is impracticable for each task of all domains of interest, due to high labor costs and unguaranteed labeling accuracy. Besides, the uncontrollable data collection process produces non-IID training and test data, where undesired duplication may exist. All these nuisances may hinder the verification of typical theories and exposure to new findings. To circumvent them, an alternative is to generate synthetic data via 3D rendering with domain randomization. We in this work push forward along this line by doing profound and extensive research on bare supervised learning and downstream domain adaptation. Specifically, under the well-controlled, IID data setting enabled by 3D rendering, we systematically verify the typical, important learning insights, e.g., shortcut learning, and discover the new laws of various data regimes and network architectures in generalization. We further investigate the effect of image formation factors on generalization, e.g., object scale, material texture, illumination, camera viewpoint, and background in a 3D scene. Moreover, we use the simulation-to-reality adaptation as a downstream task for comparing the transferability between synthetic and real data when used for pre-training, which demonstrates that synthetic data pre-training is also promising to improve real test results. Lastly, to promote future research, we develop a new large-scale synthetic-to-real benchmark for image classification, termed S2RDA, which provides more significant challenges for transfer from simulation to reality. The code and datasets are available at this https URL. <<<

Guangxi Wang, Mantang Qiu, Xudong Xing, Juntuo Zhou, Hantao Yao, Mingru Li, Rong Yin, Yan Hou, Yang Li, Shuli Pan, Yuqing Huang, Fan Yang, Fan Bai, Honggang Nie, Shuangshuang Di, Limei Guo, Zhu Meng, Jun Wang, Yuxin Yin <<<

Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism-related pathway. Untargeted lipidomics was carried out in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we identified nine lipids (lysophosphatidylcholines 16:0, 18:0, and 20:4; phosphatidylcholines 16:0-18:1, 16:0-18:2, 18:0-18:1, 18:0-18:2, and 16:0-22:6; and triglycerides 16:0-18:1-18:1) as the features most important for early-stage cancer detection. Using these nine features, we developed a liquid chromatography-mass spectrometry (MS)-based targeted assay using multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low-dose computed tomography and a prospective clinical cohort containing 109 participants, the assay reached more than 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization MS imaging confirmed that the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. This method, designated as Lung Cancer Artificial Intelligence Detector, may be useful for early detection of lung cancer or large-scale screening of high-risk populations for cancer prevention. <<<