Proteomic, single-cell and bulk transcriptomic analysis of plasma and tumor tissues unveil core proteins in response to anti-PD-L1 immunotherapy in triple negative breast cancer.

BACKGROUND: Anti-PD-1/PD-L1 treatment has achieved durable responses in TNBC patients, whereas a fraction of them showed non-sensitivity to the treatment and the mechanism is still unclear. METHODS: Pre- and post-treatment plasma samples from triple negative breast cancer (TNBC) patients treated with immunotherapy were measured by tandem mass tag (TMT) mass spectrometry. Public proteome data of lung cancer and melanoma treated with immunotherapy were employed to validate the findings. Blood and tissue single-cell RNA sequencing (scRNA-seq) data of TNBC patients treated with or without immunotherapy were analyzed to identify the derivations of plasma proteins. RNA-seq data from IMvigor210 and other cancer types were used to validate plasma proteins in predicting response to immunotherapy. RESULTS: A random forest model constructed by FAP, LRG1, LBP and COMP could well predict the response to immunotherapy. The activation of complement cascade was observed in responders, whereas FAP and COMP showed a higher abundance in non-responders and negative correlated with the activation of complements. scRNA-seq and bulk RNA-seq analysis suggested that FAP, COMP and complements were derived from fibroblasts of tumor tissues. CONCLUSIONS: We constructe an effective plasma proteomic model in predicting response to immunotherapy, and find that FAP+ and COMP+ fibroblasts are potential targets for reversing immunotherapy resistance.

Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system.

Purpose: We developed an Infant Retinal Intelligent Diagnosis System (IRIDS), an automated system to aid early diagnosis and monitoring of infantile fundus diseases and health conditions to satisfy urgent needs of ophthalmologists. Methods: We developed IRIDS by combining convolutional neural networks and transformer structures, using a dataset of 7697 retinal images (1089 infants) from four hospitals. It identifies nine fundus diseases and conditions, namely, retinopathy of prematurity (ROP) (mild ROP, moderate ROP, and severe ROP), retinoblastoma (RB), retinitis pigmentosa (RP), Coats disease, coloboma of the choroid, congenital retinal fold (CRF), and normal. IRIDS also includes depth attention modules, ResNet-18 (Res-18), and Multi-Axis Vision Transformer (MaxViT). Performance was compared to that of ophthalmologists using 450 retinal images. The IRIDS employed a five-fold cross-validation approach to generate the classification results. Results: Several baseline models achieved the following metrics: accuracy, precision, recall, F1-score (F1), kappa, and area under the receiver operating characteristic curve (AUC) with best values of 94.62% (95% CI, 94.34%-94.90%), 94.07% (95% CI, 93.32%-94.82%), 90.56% (95% CI, 88.64%-92.48%), 92.34% (95% CI, 91.87%-92.81%), 91.15% (95% CI, 90.37%-91.93%), and 99.08% (95% CI, 99.07%-99.09%), respectively. In comparison, IRIDS showed promising results compared to ophthalmologists, demonstrating an average accuracy, precision, recall, F1, kappa, and AUC of 96.45% (95% CI, 96.37%-96.53%), 95.86% (95% CI, 94.56%-97.16%), 94.37% (95% CI, 93.95%-94.79%), 95.03% (95% CI, 94.45%-95.61%), 94.43% (95% CI, 93.96%-94.90%), and 99.51% (95% CI, 99.51%-99.51%), respectively, in multi-label classification on the test dataset, utilizing the Res-18 and MaxViT models. These results suggest that, particularly in terms of AUC, IRIDS achieved performance that warrants further investigation for the detection of retinal abnormalities. Conclusions: IRIDS identifies nine infantile fundus diseases and conditions accurately. It may aid non-ophthalmologist personnel in underserved areas in infantile fundus disease screening. Thus, preventing severe complications. The IRIDS serves as an example of artificial intelligence integration into ophthalmology to achieve better outcomes in predictive, preventive, and personalized medicine (PPPM / 3PM) in the treatment of infantile fundus diseases. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00350-y.

An Artificial Intelligence System for Screening and Recommending the Treatment Modalities for Retinopathy of Prematurity.

PURPOSE: The purpose of this study was to develop an artificial intelligence (AI) system for the identification of disease status and recommending treatment modalities for retinopathy of prematurity (ROP). METHODS: This retrospective cohort study included a total of 24,495 RetCam images from 1075 eyes of 651 preterm infants who received RetCam examination at the Shenzhen Eye Hospital in Shenzhen, China, from January 2003 to August 2021. Three tasks included ROP identification, severe ROP identification, and treatment modalities identification (retinal laser photocoagulation or intravitreal injections). The AI system was developed to identify the 3 tasks, especially the treatment modalities of ROP. The performance between the AI system and ophthalmologists was compared using extra 200 RetCam images. RESULTS: The AI system exhibited favorable performance in the 3 tasks, including ROP identification [area under the receiver operating characteristic curve (AUC), 0.9531], severe ROP identification (AUC, 0.9132), and treatment modalities identification with laser photocoagulation or intravitreal injections (AUC, 0.9360). The AI system achieved an accuracy of 0.8627, a sensitivity of 0.7059, and a specificity of 0.9412 for identifying the treatment modalities of ROP. External validation results confirmed the good performance of the AI system with an accuracy of 92.0% in all 3 tasks, which was better than 4 experienced ophthalmologists who scored 56%, 65%, 71%, and 76%, respectively. CONCLUSIONS: The described AI system achieved promising outcomes in the automated identification of ROP severity and treatment modalities. Using such algorithmic approaches as accessory tools in the clinic may improve ROP screening in the future.

Electroacupuncture at ST36 acupoint regulates stem cells during experimental autoimmune encephalomyelitis.

BACKGROUND: Electroacupuncture (EA) is given to assist in the treatment of MS, which is an effective therapeutic method. However, the therapy mechanism of EA related to stem cells in the treatment of MS is not yet known. In this study, we used a classic animal model of multiple sclerosis: experimental autoimmune encephalomyelitis (EAE) to evaluate the therapeutic effect of EA at Zusanli (ST36) acupoint in EAE and shed light on its potential roles in the effects of stem cells in vivo. METHODS: The EAE animal models were established. From the first day after immunization, EAE model mice received EA at ST36 acupoint, named the EA group. The weight and clinical score of the three groups were recorded for 28 days. The demyelination, inflammatory cell infiltration, and markers of neural stem cells (NSCs), hematopoietic stem cells (HSCs), and mesenchymal stem cells (MSCs) were compared. RESULTS: We showed that EAE mice treated with EA at ST36 acupoint, were suppressed in demyelination and inflammatory cell infiltration, and thus decreased clinical score and weight loss and mitigated the development of EAE when compared with the EAE group. Moreover, our data revealed that the proportions of NSCs, HSCs, and MSCs increased in the EA group compared with the EAE group. CONCLUSIONS: Our study suggested that EA at ST36 acupoint was an effective nonpharmacological therapeutic protocol that not only reduced the CNS demyelination and inflammatory cell infiltration in EAE disease but also increased the proportions of various stem cells. Further study is necessary to better understand how EA at the ST36 acupoint affects EAE.

Exosomes-regulated lipid metabolism in tumorigenesis and cancer progression.

Increasing evidence highlights the role of lipid metabolism in tumorigenesis and tumor progression. Targeting the processes of lipid metabolism, including lipogenesis, lipid uptake, fatty acid oxidation, and lipolysis, is an optimal strategy for anti-cancer therapy. Beyond cell-cell membrane surface interaction, exosomes are pivotal factors that transduce intercellular signals in the tumor microenvironment (TME). Most research focuses on the role of lipid metabolism in regulating exosome biogenesis and extracellular matrix (ECM) remodeling. The mechanisms of exosome and ECM-mediated reprogramming of lipid metabolism are currently unclear. We summarize several mechanisms associated with the regulation of lipid metabolism in cancer, including transport of exosomal carriers and membrane receptors, activation of the PI3K pathway, ECM ligand-receptor interactions, and mechanical stimulation. This review aims to highlight the significance of these intercellular factors in TME and to deepen the understanding of the functions of exosomes and ECM in the regulation of lipid metabolism.

Extracellular matrix remodeling in tumor progression and immune escape: from mechanisms to treatments.

The malignant tumor is a multi-etiological, systemic and complex disease characterized by uncontrolled cell proliferation and distant metastasis. Anticancer treatments including adjuvant therapies and targeted therapies are effective in eliminating cancer cells but in a limited number of patients. Increasing evidence suggests that the extracellular matrix (ECM) plays an important role in tumor development through changes in macromolecule components, degradation enzymes and stiffness. These variations are under the control of cellular components in tumor tissue via the aberrant activation of signaling pathways, the interaction of the ECM components to multiple surface receptors, and mechanical impact. Additionally, the ECM shaped by cancer regulates immune cells which results in an immune suppressive microenvironment and hinders the efficacy of immunotherapies. Thus, the ECM acts as a barrier to protect cancer from treatments and supports tumor progression. Nevertheless, the profound regulatory network of the ECM remodeling hampers the design of individualized antitumor treatment. Here, we elaborate on the composition of the malignant ECM, and discuss the specific mechanisms of the ECM remodeling. Precisely, we highlight the impact of the ECM remodeling on tumor development, including proliferation, anoikis, metastasis, angiogenesis, lymphangiogenesis, and immune escape. Finally, we emphasize ECM "normalization" as a potential strategy for anti-malignant treatment.

Combining miR-23b exposure with mesenchymal stem cell transplantation enhances therapeutic effects on EAE.

Bone marrow mesenchymal stem cells (BMSCs) have the immuno-modulatory capacity to ameliorate autoimmune diseases, such as multiple schlerosis (MS), systemic lupus erythematosus and rheumatoid arthritis. However, BMSC-mediated immunosuppression can be challenging to achieve. The efficacy of BMSC transplantation may be augmented by an adjuvant therapy. Here, we demonstrated that treatment of mice with experimental autoimmune encephalomyelitis (EAE), a model of MS, with BMSCs over-expressing microRNA (miR)-23b provided better synergistic and longer-term therapeutic effects than treatment with traditional BMSCs. Over-expression of miR-23b enhanced the ability of BMSCs to inhibit differentiation of Th17 cells and reduced IL-17 secretion. Compared to traditional BMSCs, the miR-23b over-expressing BMSCs (miR23b-BMSCs) exhibited enhanced secretion of tumor growth factor beta 1 (TGF-ß1), a cytokine that promotes the differentiation of regulatory T (Treg) cells. Pathologically, miR23b-BMSC transplantation delayed EAE progression, apparently by reducing the Th17/Treg cell ratio and inhibiting inflammatory cell infiltration across the blood-brain barrier, and thus slowing spinal cord demyelination. These results may lead to better utility of BMSCs as a treatment for autoimmune diseases.

Metabolism-induced tumor activator 1 (MITA1), an Energy Stress-Inducible Long Noncoding RNA, Promotes Hepatocellular Carcinoma Metastasis.

Metastasis is the main cause of cancer-related death, yet the underlying mechanisms are still poorly understood. Long noncoding RNAs (lncRNAs) are emerging as crucial regulators of malignancies; however, their functions in tumor metastasis remain largely unexplored. In this study, we identify a lncRNA, termed metabolism-induced tumor activator 1 (MITA1), which is up-regulated in hepatocellular carcinoma (HCC) and contributes to metastasis. MITA1, a chromatin-enriched lncRNA discovered by our nuclear RNA sequencing, is significantly induced by energy stress. This induction of MITA1 is governed by the liver kinase B1-adenosine monophosphate-activated protein kinase (LKB1-AMPK) pathway and DNA methylation. Knockdown of MITA1 dramatically inhibits the migration and invasion of liver cancer cells in vitro and HCC metastasis in vivo. Mechanistically, MITA1 promotes the epithelial-mesenchymal transition, an early and central step of metastasis, which may partly attribute to an increase in Slug (snail family zinc finger 2) transcription. MITA1 deficiency reduces the expression of the mesenchymal cell markers, especially Slug, whereas Slug overexpression greatly impairs the effects of MITA1 deficiency on HCC migration and invasion. Correspondingly, there is a positive correlation between the levels of MITA1 and Slug precursors in HCC tissues. Conclusion: Our data reveal MITA1 as a crucial driver of HCC metastasis, and highlight the identified AMPK-MITA1-Slug axis as a potential therapeutic strategy for HCC.