Single cell atlas reveals multilayered metabolic heterogeneity across tumour types.

BACKGROUND: Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited. METHODS: We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties. FINDINGS: Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells. INTERPRETATION: This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism. FUNDING: National Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.

Multi-omic analysis identifies metabolic biomarkers for the early detection of breast cancer and therapeutic response prediction.

Reliable blood-based tests for identifying early-stage breast cancer remain elusive. Employing single-cell transcriptomic sequencing analysis, we illustrate a close correlation between nucleotide metabolism in the breast cancer and activation of regulatory T cells (Tregs) in the tumor microenvironment, which shows distinctions between subtypes of patients with triple-negative breast cancer (TNBC) and non-TNBC, and is likely to impact cancer prognosis through the A2AR-Treg pathway. Combining machine learning with absolute quantitative metabolomics, we have established an effective approach to the early detection of breast cancer, utilizing a four-metabolite panel including inosine and uridine. This metabolomics study, involving 1111 participants, demonstrates high accuracy across the training, test, and independent validation cohorts. Inosine and uridine prove predictive of the response to neoadjuvant chemotherapy (NAC) in patients with TNBC. This study deepens our understanding of nucleotide metabolism in breast cancer development and introduces a promising non-invasive method for early breast cancer detection and predicting NAC response in patients with TNBC.

Characterization of a ST137 multidrug-resistant Campylobacter jejuni strain with a tet(O)-positive genomic island from a bloodstream infection patient.

Campylobacter jejuni (C. jejuni) is a major cause of gastroenteritis and rarely cause bloodstream infection. Herein, we characterized a multidrug-resistant C. jejuni strain LZCJ isolated from a tumor patient with bloodstream infection. LZCJ was resistant to norfloxacin, ampicillin, ceftriaxone, ciprofloxacin and tetracycline. It showed high survival rate in serum and acidic environment. Whole genome sequencing (WGS) analysis revealed that strain LZCJ had a single chromosome of 1,629,078 bp (30.6 % G + C content) and belonged to the ST137 lineage. LZCJ shared the highest identity of 99.66 % with the chicken-derived C. jejuni MTVDSCj20. Four antimicrobial resistance genes (ARGs) were detected, blaOXA-61, tet(O), gyrA (T86I), and cmeR (G144D and S207G). In addition, a 12,746 bp genomic island GI_LZCJ carrying 15 open reading frames (ORFs) including the resistance gene tet(O) was identified. Sequence analysis found that the GI_LZCJ was highly similar to the duck-derived C. jejuni ZS004, but with an additional ISChh1-like sequence. 137 non-synonymous mutations in motility related genes (flgF, fapR, flgS), capsular polysaccharide (CPS) coding genes (kpsE, kpsF, kpsM, kpsT), metabolism associated genes (nuoF, nuoG, epsJ, holB), and transporter related genes (comEA, gene0911) were confirmed in LZCJ compared with the best closed chicken-derived strain MTVDSCj20. Our study showed that C. jejuni strain LZCJ was highly similar to the chicken-derived strain MTVDSCj20 but with a lot of SNPs involved in motility, CPS and metabolism coding genes. This strain possessed a tet(O)-positive genomic island GI_LZCJ, which was closed to duck-derived C. jejuni ZS004, but with an additional ISChh1-like sequence. The above data indicated that the LZCJ strain may originate from foodborne bacteria on animals and the importance of continuous surveillance for the spread of foodborne bacteria.

CD36+ pro-inflammatory macrophages interact with ZCCHC12+ tumor cells in papillary thyroid cancer promoting tumor progression and recurrence.

Local recurrence and distal metastasis negatively impact the survival and quality of life in patients with papillary thyroid cancer (PTC). Therefore, identifying potential biomarkers and therapeutic targets for PTC is clinically crucial. In this study, we performed a multi-omics analysis that identified a subset of CD36+ pro-inflammatory macrophages within the tumor microenvironment of PTC. The recruitment of CD36+ macrophages to pre-malignant regions strongly correlated with unfavorable outcomes in PTC and the presence of tumor-infiltrating CD36+ macrophages was determined to be a risk factor for recurrence. The CD36+ macrophages exhibited interactions with metabolically active ZCCHC12+ tumor cells. By secreting SPP1, the CD36+ macrophages activated the PI3K-AKT signaling pathway, thereby promoting proliferation of the cancer cells. Dysregulation of iodine metabolism was closely related to the acquisition of the pro-inflammatory phenotype in macrophages. Iodine supplementation inhibited the activation of pro-inflammatory signaling and impeded the development of CD36+ macrophages by enhancing DUSP2 expression. Overall, our findings shed light on the intricate crosstalk between CD36+ macrophages and ZCCHC12+ tumor cells, providing valuable insights for the treatment and prognosis of PTC.

NuSEA: Nuclei Segmentation With Ellipse Annotations.

OBJECTIVE: Nuclei segmentation is a crucial pre-task for pathological microenvironment quantification. However, the acquisition of manually precise nuclei annotations for improving the performance of deep learning models is time-consuming and expensive. METHODS: In this paper, an efficient nuclear annotation tool called NuSEA is proposed to achieve accurate nucleus segmentation, where a simple but effective ellipse annotation is applied. Specifically, the core network U-Light of NuSEA is lightweight with only 0.86 M parameters, which is suitable for real-time nuclei segmentation. In addition, an Elliptical Field Loss and a Texture Loss are proposed to enhance the edge segmentation and constrain the smoothness simultaneously. RESULTS: Extensive experiments on three public datasets (MoNuSeg, CPM-17, and CoNSeP) demonstrate that NuSEA is superior to the state-of-the-art (SOTA) methods and better than existing algorithms based on point, rectangle, and text annotations. CONCLUSIONS: With the assistance of NuSEA, a new dataset called NuSEA-dataset v1.0, encompassing 118,857 annotated nuclei from the whole-slide images of 12 organs is released. SIGNIFICANCE: NuSEA provides a rapid and effective annotation tool for nuclei in histopathological images, benefiting future explorations in deep learning algorithms.

Establishment of a cholangiocarcinoma risk evaluation model based on mucin expression levels.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant cancer, characterized by frequent mucin overexpression. MUC1 has been identified as a critical oncogene in the progression of CCA. However, the comprehensive understanding of how the mucin family influences CCA progression and prognosis is still incomplete. AIM: To investigate the functions of mucins on the progression of CCA and to establish a risk evaluation formula for stratifying CCA patients. METHODS: Single-cell RNA sequencing data from 14 CCA samples were employed for elucidating the roles of mucins, complemented by bioinformatic analyses. Subsequent validations were conducted through spatial transcriptomics and immunohistochemistry. The construction of a risk evaluation model utilized the least absolute shrinkage and selection operator regression algorithm, which was further confirmed by independent cohorts and diverse data types. RESULTS: CCA tumor cells with elevated levels of MUC1 and MUC4 showed activated nucleotide metabolic pathways and increased invasiveness. MUC5AC-high cells were found to promote CCA progression through WNT signaling. MUC5B-high cells exhibited robust cellular oxidation activities, leading to resistance against antitumoral treatments. MUC13-high cells were observed to secret chemokines, recruiting and transforming macrophages into the M2-polarized state, thereby suppressing antitumor immunity. MUC16-high cells were found to promote tumor progression through interleukin-1/nuclear factor kappa-light-chain-enhancer of activated B cells signaling upon interaction with neutrophils. Utilizing the expression levels of these mucins, a risk factor evaluation formula for CCA was developed and validated across multiple cohorts. CCA samples with higher risk factors exhibited stronger metastatic potential, chemotherapy resistance, and poorer prognosis. CONCLUSION: Our study elucidates the functional mechanisms through which mucins contribute to CCA development, and provides tools for risk stratification in CCA.

Tumor-Associated Monocytes Reprogram CD8+ T Cells into Central Memory-Like Cells with Potent Antitumor Effects.

CD8+ T cells are critical for host antitumor responses, whereas persistent antigenic stimulation and excessive inflammatory signals lead to T cell dysfunction or exhaustion. Increasing early memory T cells can improve T cell persistence and empower T cell-mediated tumor eradication, especially for adoptive cancer immunotherapy. Here, it is reported that tumor-associated monocytes (TAMos) are highly correlated with the accumulation of CD8+ memory T cells in human cancers. Further analysis identifies that TAMos selectively reprogram CD8+ T cells into T central memory-like (TCM-like) cells with enhanced recall responses. L-NMMA, a pan nitric oxide synthase inhibitor, can mitigate TAMo-mediated inhibition of T cell proliferation without affecting TCM-like cell generation. Moreover, the modified T cells by TAMo exposure and L-NMMA treatment exhibit long-term persistence and elicit superior antitumor effects in vivo. Mechanistically, the transmembrane protein CD300LG is involved in TAMo-mediated TCM-like cell polarization in a cell-cell contact-dependent manner. Thus, the terminally differentiated TAMo subset (CD300LGhighACElow) mainly contributes to TCM-like cell development. Taken together, these findings establish the significance of TAMos in boosting T-cell antitumor immunity.

Analysis of TLR2 in Primary Endocrine Resistant of Breast Cancer.

BACKGROUND: Previous clinical studies have suggested that Toll-like receptor (TLR)2 had predictive function for endocrine resistance in HER2-positive breast cancer (BCa). Nevertheless, it remains unclear whether TLR2 would relate to development of endocrine therapy resistance in triple-positive breast cancer (TPBC). METHODS: Bioinformatic analysis of TLR2 was carried out through a database. Ten tumor tissues were obtained from TPBC patients who underwent surgery, with five patients displaying primary resistance to tamoxifen (TAM) with the remaining 5 being sensitive. Different levels of proteins were identified through mass spectrometry analysis and confirmed through reverse transcription polymerase chain reaction (RT-PCR) and western blot. TAM-resistant cell lines (BT474-TAM) were established by continuous exposure to TAM, and TAM resistance was assessed via IC50. Additionally, TLR2 mRNA was analyzed through western blot and RT-PCR in BT474, BT474-TAM, MCF-7, and MCF10A cells. Furthermore, TLR2-specific interference sequences were utilized to downregulate TLR2 expression in BT474-TAM cells to elucidate its role in TAM resistance. RESULTS: TLR2 had a correlation with decreased relapse-free survival in BCa patients from the GSE1456-GPL96 cohort, and it was involved in cancer development predominantly mediated by MAPK and PI3K pathways. TLR2 protein expression ranked in the top 5 proteins within the TAM-resistant group, and was 1.9 times greater than that in the sensitive group. Additionally, TLR2 mRNA and protein expression increased significantly in the established TAM-resistant BT474/TAM cell lines. The sensitivity of TAM was restored upon TLR2 downregulation in BT474/TAM cells. CONCLUSIONS: TLR2 might have a therapeutic value as it was involved in the TAM resistance in TPBC, with potential to be a marker for primary endocrine resistance.

Structural and functional insights into the lipid regulation of human anion exchanger 2.

Anion exchanger 2 (AE2) is an electroneutral Na+-independent Cl-/HCO3- exchanger belongs to the SLC4 transporter family. The widely expressed AE2 participates in a variety of physiological processes, including transepithelial acid-base secretion and osteoclastogenesis. Both the transmembrane domains (TMDs) and the N-terminal cytoplasmic domain (NTD) are involved in regulation of AE2 activity. However, the regulatory mechanism remains unclear. Here, we report a 3.2 Å cryo-EM structure of the AE2 TMDs in complex with PIP2 and a 3.3 Å full-length mutant AE2 structure in the resting state without PIP2. We demonstrate that PIP2 at the TMD dimer interface is involved in the substrate exchange process. Mutation in the PIP2 binding site leads to the displacement of TM7 and further stabilizes the interaction between the TMD and the NTD. Reduced substrate transport activity and conformation similar to AE2 in acidic pH indicating the central contribution of PIP2 to the function of AE2.

CircPTEN-MT from PTEN regulates mitochondrial energy metabolism.

Phosphatase and tensin homolog (PTEN) is a multifunctional gene involved in a variety of physiological and pathological processes. Circular RNAs (circRNAs) are generated from back-splicing events during mRNA processing and participate in cell biological processes through binding to RNAs or proteins. However, PTEN-related circRNAs are largely unknown. Here, we report that circPTEN- mitochondria (MT) (hsa_circ_0002934) is a circular RNA encoded by exons 3, 4, and 5 of PTEN and is a critical regulator of mitochondrial energy metabolism. CircPTEN-MT is localized to mitochondria and physically associated with leucine-rich pentatricopeptide repeat-containing protein (LRPPRC), which regulates posttranscriptional gene expression in mitochondria. Knocking down circPTEN-MT reduces the interaction of LRPPRC and steroid receptor RNA activator (SRA) stem-loop interacting RNA binding protein (SLIRP) and inhibits the polyadenylation of mitochondrial mRNA, which decreases the mRNA level of the mitochondrial complex I subunit and reduces mitochondrial membrane potential and adenosine triphosphate production. Our data demonstrate that circPTEN-MT is an important regulator of cellular energy metabolism. This study expands our understanding of the role of PTEN, which produces both linear and circular RNAs with different and independent functions.

One-pot synthesis of novel mesoporous FeOOH modified NaZrH(PO4)2·H2O for the enhanced removal of Co(II) from aqueous solution.

One-pot synthesis of a novel mesoporous hydroxyl oxidize iron functional Na-zirconium phosphate (FeOOH-NaZrH(PO4)2·H2O) composites was firstly characterized and investigated its Co(II) adsorption from aqueous solution. Compared to NaZrH(PO4)2·H2O (65.7 mg⋅g-1), the maximum Co(II) adsorption capacity of FeOOH-NaZrH(PO4)2·H2O was improved to be 95.1 mg⋅g-1. BET verified the mesoporous structures of FeOOH-NaZrH(PO4)2·H2O with a larger pore volume than NaZrH(PO4)2·H2O. High pH values, initial Co(II) concentration, and temperature benefited the Co(II) adsorption. Kinetics, isotherms, and thermodynamics indicated an endothermic, spontaneous chemisorption process. FeOOH-NaZrH(PO4)2·H2O has a better Co(II) adsorption selectivity than that of NaZrH(PO4)2·H2O. In particular, FeOOH-NaZrH(PO4)2·H2O exhibited an outstanding reusability after ten cycles of tests. The main possible mechanism for adsorbents uptake Co(II) involved in ion exchange, electrostatic interaction, and -OH, Zr-O bond coordination based on FTIR and XPS analysis. This work presents a feasible strategy to prepare novel modified zirconium phosphate composites for extracting Co(II) from solutions and providing a new insight into the understanding of Co(II) adsorption in the real nuclear Co(II)-containing wastewater.

Cancer metabolites: promising biomarkers for cancer liquid biopsy.

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

A Deep Learning-Based System Trained for Gastrointestinal Stromal Tumor Screening Can Identify Multiple Types of Soft Tissue Tumors.

The accuracy and timeliness of the pathologic diagnosis of soft tissue tumors (STTs) critically affect treatment decision and patient prognosis. Thus, it is crucial to make a preliminary judgement on whether the tumor is benign or malignant with hematoxylin and eosin-stained images. A deep learning-based system, Soft Tissue Tumor Box (STT-BOX), is presented herein, with only hematoxylin and eosin images for malignant STT identification from benign STTs with histopathologic similarity. STT-BOX assumed gastrointestinal stromal tumor as a baseline for malignant STT evaluation, and distinguished gastrointestinal stromal tumor from leiomyoma and schwannoma with 100% area under the curve in patients from three hospitals, which achieved higher accuracy than the interpretation of experienced pathologists. Particularly, this system performed well on six common types of malignant STTs from The Cancer Genome Atlas data set, accurately highlighting the malignant mass lesion. STT-BOX was able to distinguish ovarian malignant sex-cord stromal tumors without any fine-tuning. This study included mesenchymal tumors that originated from the digestive system, bone and soft tissues, and reproductive system, where the high accuracy of migration verification may reveal the morphologic similarity of the nine types of malignant tumors. Further evaluation in a pan-STT setting would be potential and prospective, obviating the overuse of immunohistochemistry and molecular tests, and providing a practical basis for clinical treatment selection in a timely manner.

MiR-135a-5p/SP1 Axis Regulates Spinal Astrocyte Proliferation and Migration.

Following spinal cord injury (SCI), astrocyte activation and proliferation result in the development of glial scars, which impede axonal growth and neurological recovery. Dysregulation of microRNAs (miRNAs) during SCI results in altered expression of downstream genes. Our previous study has revealed that miR-135a-5p regulates neuronal apoptosis and axonal growth by targeting specificity protein 1 (SP1). This study attempted to investigate whether the miR-135a-5p/SP1 axis has regulatory effect on astrocytes. Herein, lipopolysaccharide (LPS) reduced miR-135a-5p expression in astrocytes. miR-135a-5p overexpression in astrocytes resulted in a decrease in CyclinD1, MMP9, GFAP, and vimentin proteins, and thus attenuated LPS-induced proliferation and migration of astrocytes. Moreover, miR-135a-5p overexpression decreased astrocyte size and the total quantity of cell protrusions, suggesting a role for miR-135a-5p in regulating astrocyte morphology. SP1 silencing also decreased astrocyte proliferation and migration by LPS. SP1 silencing could significantly reverse the promoting effect of miR-135a-5p inhibition on astrocyte proliferation and migration. In summary, the miR-135a-5p/SP1 axis regulates astrocyte proliferation and migration after SCI. This finding benefits for the development of novel ways in treating SCI effectively.

Palbociclib plus endocrine therapy in hormone receptor-positive and HER2 negative metastatic breast cancer: a multicenter real-world study in the northwest of China.

BACKGROUND: Real-world data of Palbociclib are insufficient in China. This study aimed to investigate the treatment pattern and real-world outcomes in hormone receptor positive and human epidermal growth factor 2 receptor negative (HR+/HER2-) metastatic breast cancer (MBC) patients treated with Palbociclib in the northwest of China. METHODS: HR+/HER2- MBC patients who received Palbociclib in 8 centers from July 2017 to September 2019 were retrospectively included in this study. Real-world objective response rate (ORR), progression-free survival (PFS) and safety profiles were analyzed. The survival curves were plotted by the Kaplan-Meier method to analyze PFS, which was verified by the log-rank test. RESULTS: In total, 211 women were eligible for the analysis. A total of 85 patients (40.3%), 78 (37.0%), and 48 (22.7%) received Palbociclib in the first-, second-, third- or later-line setting, respectively. 46 patients achieved partial response and 145 patients experienced stable disease, with an ORR of 21.8% and a disease control rate of 90.5%. Following a median follow-up period of 14.2 months, the median PFS was 12.2 months (95% confidence interval, 10.1-14.3 m), and the median overall survival was not reached. Early Palbociclib initiation, sensitivity or acquired resistance to endocrine therapy, estrogen receptor and progesterone receptor double positivity, less than 3 metastatic sites, without visceral metastasis, bone metastasis only, without prior chemotherapy or endocrine therapy were associated with a prolonged PFS in MBC (All P < 0.05). The most common grade 3 or 4 adverse events (AE) was neutropenia (36.5%), and the most common nonhematologic AE was fatigue (10.9%). No patient experienced AE leading to treatment discontinuation. CONCLUSION: Palbociclib plus endocrine therapy exhibited favorable effectiveness and manageable toxicities in the real-world setting, supporting their use in Chinese patients with HR+/HER2 - MBC.

Separation and Purification of Two Saponins from Paris polyphylla var. yunnanensis by a Macroporous Resin.

An effective method for separating and purifying critical saponins (polyphyllin II and polyphyllin VII) from a Paris polyphylla var. yunnanensis extract was developed in this study which was environmentally friendly and economical. Static adsorption kinetics, thermodynamics, and the dynamic adsorption-desorption of macroporous resins were investigated, and then the conditions of purification and separation were optimized by fitting with an adsorption thermodynamics equation and a kinetic equation. Effective NKA-9 resin from seven macroporous resins was screened out to separate and purify the two saponins. The static adsorption and dynamic adsorption were chemical and physical adsorption dual-processes on the NKA-9 resin. Under the optimum parameters, the contents of polyphyllin II and polyphyllin VII in the product were 17.3-fold and 28.6-fold those in plant extracts, respectively. The total yields of the two saponins were 93.16%. This research thus provides a theoretical foundation for the large-scale industrial production of the natural drugs polyphyllin II and polyphyllin VII.

RPA1 controls chromatin architecture and maintains lipid metabolic homeostasis.

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease, with a prevalence of 25% worldwide. However, the underlying molecular mechanism involved in the development and progression of the NAFLD spectrum remains unclear. Single-stranded DNA-binding protein replication protein A1 (RPA1) participates in DNA replication, recombination, and damage repair. Here, we show that Rpa1+/- mice develop fatty liver disease during aging and in response to a high-fat diet. Liver-specific deletion of Rpa1 results in downregulation of genes related to fatty acid oxidation and impaired fatty acid oxidation, which leads to hepatic steatosis and hepatocellular carcinoma. Mechanistically, RPA1 binds gene regulatory regions, chromatin-remodeling factors, and HNF4A and remodels chromatin architecture, through which RPA1 promotes HNF4A transcriptional activity and fatty acid ß oxidation. Collectively, our data demonstrate that RPA1 is an important regulator of NAFLD through controlling chromatin accessibility.

Metabolic detection of malignant brain gliomas through plasma lipidomic analysis and support vector machine-based machine learning.

BACKGROUND: Most malignant brain gliomas (MBGs) are associated with dismal outcomes, mainly due to their late diagnosis. Current diagnostic methods for MBGs are based on imaging and histological examination, which limits their early detection. Here, we aimed to identify reliable plasma lipid biomarkers for non-invasive diagnosis for MBGs. METHODS: Untargeted lipidomic analysis was firstly performed using a discovery cohort (n=107). The data were processed by a support vector machine (SVM)-based discriminating model to retrieve a panel of candidate biomarkers. Then, a targeted quantification method was developed, and the SVM-based diagnostic model was constructed using a training cohort (n=750) and tested using a test cohort (n=225). Finally, the performance of the diagnostic model was further evaluated in an independent validation cohort (n=920) enrolled from multiple medical centers. FINDINGS: A panel of 11 plasma lipids was identified as candidate biomarkers with an accuracy of 0.999. The diagnostic model developed achieved a high performance in distinguishing MBGs patients from normal controls with an area under the receiver-operating characteristic curve (AUC) of 0.9877 and 0.9869 in the training and test cohorts, respectively. In the validation cohort, the 11 lipid panel still achieved an accuracy of 0.9641 and an AUC of 0.9866. INTERPRETATION: The present study demonstrates the applicability and robustness of utilizing a machine learning algorithm to analyze lipidomic data for efficient and reliable biomarker screening. The 11 lipid biomarkers show great potential for the non-invasive diagnosis of MBGs with high throughput. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgments section.

Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis.

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.

Metabolic detection and systems analyses of pancreatic ductal adenocarcinoma through machine learning, lipidomics, and multi-omics.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, characterized by rapid progression, metastasis, and difficulty in diagnosis. However, there are no effective liquid-based testing methods available for PDAC detection. Here we introduce a minimally invasive approach that uses machine learning (ML) and lipidomics to detect PDAC. Through greedy algorithm and mass spectrum feature selection, we optimized 17 characteristic metabolites as detection features and developed a liquid chromatography-mass spectrometry-based targeted assay. In this study, 1033 patients with PDAC at various stages were examined. This approach has achieved 86.74% accuracy with an area under curve (AUC) of 0.9351 in the large external validation cohort and 85.00% accuracy with 0.9389 AUC in the prospective clinical cohort. Accordingly, single-cell sequencing, proteomics, and mass spectrometry imaging were applied and revealed notable alterations of selected lipids in PDAC tissues. We propose that the ML-aided lipidomics approach be used for early detection of PDAC.