Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.

BACKGROUND: This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI). METHODS: A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility. RESULTS: Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model. CONCLUSIONS: The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.

Acidic/hypoxia dual-alleviated nanoregulators for enhanced treatment of tumor chemo-immunotherapy.

Chemotherapy plays a crucial role in triple-negative breast cancer (TNBC) treatment as it not only directly kills cancer cells but also induces immunogenic cell death. However, the chemotherapeutic efficacy was strongly restricted by the acidic and hypoxic tumor environment. Herein, we have successfully formulated PLGA-based nanoparticles concurrently loaded with doxorubicin (DOX), hemoglobin (Hb) and CaCO3 by a CaCO3-assisted emulsion method, aiming at the effective treatment of TNBC. We found that the obtained nanomedicine (DHCaNPs) exhibited effective drug encapsulation and pH-responsive drug release behavior. Moreover, DHCaNPs demonstrated robust capabilities in neutralizing protons and oxygen transport. Consequently, DHCaNPs could not only serve as oxygen nanoshuttles to attenuate tumor hypoxia but also neutralize the acidic tumor microenvironment (TME) by depleting lactic acid, thereby effectively overcoming the resistance to chemotherapy. Furthermore, DHCaNPs demonstrated a notable ability to enhance antitumor immune responses by increasing the frequency of tumor-infiltrating effector lymphocytes and reducing the frequency of various immune-suppressive cells, therefore exhibiting a superior efficacy in suppressing tumor growth and metastasis when combined with anti-PD-L1 (αPD-L1) immunotherapy. In summary, this study highlights that DHCaNPs could effectively attenuate the acidic and hypoxic TME, offering a promising strategy to figure out an enhanced chemo-immunotherapy to benefit TNBC patients.

Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer (BC) patients who underwent total mastectomy (TM) and had 1-2 positive sentinel lymph nodes (SLNs). MATERIALS AND METHODS: In total, 494 patients were retrospectively enrolled from two hospitals, and were divided into the training (n = 286), internal validation (n = 122), and external validation (n = 86) cohorts. Features were extracted from DCE-MRI images for each patient and screened. Six ML classifies were trained and the best classifier was evaluated to calculate radiomics (Rad)-scores. A combined model was developed based on Rad-scores and clinical risk factors, then the calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS: 14 radiomics features were ultimately selected. The random forest (RF) classifier showed the best performance, with the highest average area under the curve (AUC) of 0.833 in the validation cohorts. The combined model incorporating RF-based Rad-scores, tumor size, lymphovascular invasion, and proportion of positive SLNs resulted in the best discrimination ability, with AUCs of 0.903, 0.890, and 0.836 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the combined model significantly improved the classification accuracy and clinical benefit for NSLN metastasis prediction. CONCLUSION: A RF-based combined model using DCE-MRI images exhibited a promising performance for predicting NSLN metastasis in Chinese BC patients who underwent TM and had 1-2 positive SLNs, thereby aiding in individualized clinical treatment decisions.

Multiple Immunomodulatory Strategies Based on Targeted Regulation of Proprotein Convertase Subtilisin/Kexin Type 9 and Immune Homeostasis against Hepatocellular Carcinoma.

Immunotherapy is the most promising systemic therapy for hepatocellular carcinoma. However, the outcome remains poor. Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a role in altering cell-surface protein levels, potentially undermining the efficacy of immunotherapy against tumors. This highlights its potential as a target for antitumor therapy. Herein, CaCO3-based nanoparticles coencapsulated with DOX, an immunogenic cell death (ICD) inducer, and evolocumab was developed to enhanced the efficacy of immunotherapy. The obtained DOX/evolocumab-loaded CaCO3 nanoparticle (named DECP) exhibits a good capacity of acid neutralization and causes ICD of cancer cells. In addition, DECP is able to evaluate the cell-surface level of MHC-I, a biomarker that correlates positively with patients' overall survival. Upon intravenous injection, DECP accumulates within the tumor site, leading to growth inhibition of hepa1-6 bearing subcutaneous tumors. Specifically, DECP treatment causes augmented ratios of matured dendritic cells, tumor-infiltrating CD8+ T cells and natural killing cells, while concurrently depleting Foxp3+ regulatory T cells. Peritumoral delivery of DECP enhances the immune response of distant tumors and exhibits antitumor effects when combined with intravenous αPD-L1 therapy in a bilateral tumor model. This study presents CaCO3-based nanoparticles with multiple immunomodulatory strategies against hepatocellular carcinoma by targeting PCSK9 inhibition and modulating immune homeostasis in the unfavorable TME.

Hippo pathway in non-small cell lung cancer: mechanisms, potential targets, and biomarkers.

Lung cancer is the primary contributor to cancer-related deaths globally, and non-small cell lung cancer (NSCLC) constitutes around 85% of all lung cancer cases. Recently, the emergence of targeted therapy and immunotherapy revolutionized the treatment of NSCLC and greatly improved patients' survival. However, drug resistance is inevitable, and extensive research has demonstrated that the Hippo pathway plays a crucial role in the development of drug resistance in NSCLC. The Hippo pathway is a highly conserved signaling pathway that is essential for various biological processes, including organ development, maintenance of epithelial balance, tissue regeneration, wound healing, and immune regulation. This pathway exerts its effects through two key transcription factors, namely Yes-associated protein (YAP) and transcriptional co-activator PDZ-binding motif (TAZ). They regulate gene expression by interacting with the transcriptional-enhanced associate domain (TEAD) family. In recent years, this pathway has been extensively studied in NSCLC. The review summarizes a comprehensive overview of the involvement of this pathway in NSCLC, and discusses the mechanisms of drug resistance, potential targets, and biomarkers associated with this pathway in NSCLC.

Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics.

OBJECTIVES: Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer. METHODS: A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves. RESULTS: Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively. CONCLUSIONS: The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients. ADVANCES IN KNOWLEDGE: This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.

The outcomes of different regimens depend on the molecular subtypes of pulmonary large-cell neuroendocrine carcinoma: A retrospective study in China.

BACKGROUND: The optimal systemic treatment for pulmonary large-cell neuroendocrine carcinoma (LCNEC) remains controversial, and recent advances in LCNEC molecular subtype classification have provided potential strategies for assisting in treatment decisions. Our study aimed to investigate the impact of treatment regimens, molecular subtypes and their concordance on clinical outcomes of patients diagnosed with LCNEC. PATIENTS AND METHODS: All patients diagnosed with advanced pulmonary LCNEC in Peking Union Medical College Hospital (PUMCH) between January 2000 and October 2021 were enrolled in this retrospective study. The tumor samples were collected and sequenced using a tumor-specific gene panel, while clinical information was retrieved from the medical records system. The survival and therapeutic response were analyzed and compared between different subgroups classified by treatment regimen (SCLC or NSCLC-based), molecular subtype (type I or II) or the combination. RESULTS: In univariate subgroup analysis categorized only by treatment regimen or molecular subtype, there were no differences identified in DCR, ORR, PFS, or OS. Nevertheless, the group with consistent treatment regimen and molecular subtype exhibited significantly longer OS than that of the inconsistent group (median OS 37.7 vs. 8.3 months; p = 0.046). Particularly, the OS of patients with type II LCNEC treated with SCLC-based regimen was significantly prolonged than that of others (median 37.7 vs. 10.5 months; p = 0.039). CONCLUSIONS: Collectively, our study revealed the clinical outcomes of different treatment regimens for LCNEC patients highly depend on their molecular subtypes, highlighting the need for sequencing-guided therapy.

Development and Validation of a Dual-Energy CT-Based Model for Predicting the Number of Central Lymph Node Metastases in Clinically Node-Negative Papillary Thyroid Carcinoma.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a dual-energy CT (DECT)-based model for preoperative prediction of the number of central lymph node metastases (CLNMs) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC) patients. MATERIALS AND METHODS: Between January 2016 and January 2021, 490 patients who underwent lobectomy or thyroidectomy, CLN dissection, and preoperative DECT examinations were enrolled and randomly allocated into the training (N = 345) and validation cohorts (N = 145). The patients' clinical characteristics and quantitative DECT parameters obtained on primary tumors were collected. Independent predictors of> 5 CLNMs were identified and integrated to construct a DECT-based prediction model, for which the area under the curve (AUC), calibration, and clinical usefulness were assessed. Risk group stratification was performed to distinguish patients with different recurrence risks. RESULTS: More than 5 CLNMs were found in 75 (15.3%) cN0 PTC patients. Age, tumor size, normalized iodine concentration (NIC), normalized effective atomic number (nZeff) and the slope of the spectral Hounsfield unit curve (λHu) in the arterial phase were independently associated with> 5 CLNMs. The DECT-based nomogram that incorporated predictors demonstrated favorable performance in both cohorts (AUC: 0.842 and 0.848) and significantly outperformed the clinical model (AUC: 0.688 and 0.694). The nomogram showed good calibration and added clinical benefit for predicting> 5 CLNMs. The KaplanMeier curves for recurrence-free survival showed that the high- and low-risk groups stratified by the nomogram were significantly different. CONCLUSION: The nomogram based on DECT parameters and clinical factors could facilitate preoperative prediction of the number of CLNMs in cN0 PTC patients.

Osimertinib versus comparator first-generation epidermal growth factor receptor tyrosine kinase inhibitors as first-line treatment in patients with advanced EGFR-mutated non-small cell lung cancer: a Chinese, multicenter, real-world cohort study.

Background: In the phase 3 FLAURA trial, osimertinib was compared with first-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) as a first-line treatment for EGFR-mutant non-small cell lung cancer (NSCLC). Osimertinib showed longer progression-free survival (PFS), overall survival (OS), and a similar safety profile. However, more studies demonstrating the effectiveness and safety of osimertinib as a first-line strategy are needed in real-world populations. Methods: We enrolled 1,556 patients with EGFR-mutated stage IIIc-IV NSCLC from the CAPTRA-Lung database. All patients received either osimertinib (n=202) or a first-generation EGFR-TKI (n=1,354) as their initial treatment. To adjust for differences in baseline characteristics between two groups, 1:2 propensity score matching (PSM) was performed. Propensity scores included gender, age, Eastern Cooperative Oncology Group performance status score, smoking history, family history of tumor, pathology, EGFR mutations, and central nervous system (CNS) metastases. The standardized mean differences (SMD) before and after PSM were calculated to examine the balance of covariate distributions between two groups. Results: After PSM, 202 patients receiving osimertinib and 404 patients receiving first-generation EGFR-TKIs were finally identified. SMD of each matched variable is less than 0.10. The median PFS was 19.4 months [95% confidence interval (CI): 14.3-24.4] in the osimertinib arm and 10.9 months (95% CI: 9.3-12.5) in the comparator arm [hazard ratio (HR) for progression, 0.47; 95% CI: 0.38-0.59; P<0.001). The median OS was 40.5 months (95% CI: 27.1-54.0) vs. 34.3 months (95% CI: 30.6-38.0) in two groups, respectively (HR for death, 0.76; 95% CI: 0.58-1.00; P=0.045). The incidence of grade 3 adverse events (AEs) between the two groups was 1% and 4.2%, respectively. No grade 4 AEs and treatment-related deaths were reported in both groups. Conclusions: In real-world settings, osimertinib demonstrates longer PFS and OS, with a similar safety profile to that of comparator EGFR-TKIs when used as a first-line strategy in NSCLC patients.

Divergent tumor and immune cell reprogramming underlying immunotherapy response and immune-related adverse events in lung squamous cell carcinoma.

BACKGROUND: Lung squamous cell carcinoma (LUSC) remains a leading cause of cancer-related deaths with few therapeutic strategies. Immune checkpoint inhibitors (ICIs) have demonstrated promising efficacy in patients with LUSC. However, ICIs could also lead to a unique spectrum of immune-related adverse events (irAEs), which dampen the clinical outcome. In-depth characterization of the immune hallmarks of antitumor responses and irAEs remains an unmet need to maximize ICI-treatment benefits of patients. METHODS: We performed single-cell RNA sequencing (scRNA-seq) on pre-ICI and on-ICI treatment tumor biopsies. We used bulk RNA-seq data of matched pretreatment/on-treatment tumors and irAE affected organs to validate observations from scRNA-seq analysis. Two independent patient cohorts were collected to determine circulating tumor necrosis factor (TNF) protein expression levels. RESULTS: We found that increased proportions of a macrophage subcluster with highly expressed secreted phosphoprotein 1 (SPP1) and two tumor cell subclusters in irAE patients, whereas proportions of two cytotoxic CD8+ T cell subclusters were higher in patients with partial response (PR). TNF signaling pathway was conversely associated with treatment efficacy and irAE development in most macrophage and tumor cell subclusters. Cell-cell communications for TNF ligand-receptor pairs between macrophage/T cells and tumor cells were also bidirectionally remodeled in responders versus non-responders and irAE versus non-irAE patients. Bulk RNA-seq analysis on matched pretreatment/on-treatment tumors and irAE affected organs revealed remarkably enhanced macrophage abundance and TNF signaling pathway in on-treatment tumors and organs developed irAEs. Furthermore, we observed significantly increased circulating TNF protein in plasma or serum of irAE patients but not ICI responders, based on analysis of two independent LUSC patient cohorts and one published ICI patient cohort. CONCLUSIONS: Our data depicts specific reprogramming of macrophage, T cells and tumor cells associated with ICI response and irAEs, elucidates divergent roles of TNF signaling in antitumor immunity and irAEs, and highlights the significance of TNF expression in irAE development in the LUSC setting.

Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study.

Background: For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images. Methods: In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n = 519), internal validation set (n = 129), external test set 1 (n = 296), and external test set 2 (n = 44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740. Findings: For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0 cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model. Interpretation: The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively. Funding: National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.

[CT-Based Weighted Radiomic Score Predicts Tumor Response to Immunotherapy in Non-Small Cell Lung Cancer].

Objective To develop a CT-based weighted radiomic model that predicts tumor response to programmed death-1(PD-1)/PD-ligand 1(PD-L1)immunotherapy in patients with non-small cell lung cancer.Methods The patients with non-small cell lung cancer treated by PD-1/PD-L1 immune checkpoint inhibitors in the Peking Union Medical College Hospital from June 2015 to February 2022 were retrospectively studied and classified as responders(partial or complete response)and non-responders(stable or progressive disease).Original radiomic features were extracted from multiple intrapulmonary lesions in the contrast-enhanced CT scans of the arterial phase,and then weighted and summed by an attention-based multiple instances learning algorithm.Logistic regression was employed to build a weighted radiomic scoring model and the radiomic score was then calculated.The area under the receiver operating characteristic curve(AUC)was used to compare the weighted radiomic scoring model,PD-L1 model,clinical model,weighted radiomic scoring + PD-L1 model,and comprehensive prediction model.Results A total of 237 patients were included in the study and randomized into a training set(n=165)and a test set(n=72),with the mean ages of(64±9)and(62±8)years,respectively.The AUC of the weighted radiomic scoring model reached 0.85 and 0.80 in the training set and test set,respectively,which was higher than that of the PD-L1-1 model(Z=37.30,P<0.001 and Z=5.69,P=0.017),PD-L1-50 model(Z=38.36,P<0.001 and Z=17.99,P<0.001),and clinical model(Z=11.40,P<0.001 and Z=5.76,P=0.016).The AUC of the weighted scoring model was not different from that of the weighted radiomic scoring + PD-L1 model and the comprehensive prediction model(both P>0.05).Conclusion The weighted radiomic scores based on pre-treatment enhanced CT images can predict tumor responses to immunotherapy in patients with non-small cell lung cancer.

Molecular subtypes and prognostic models for predicting prognosis of lung adenocarcinoma based on miRNA-related genes.

BACKGROUND: MicroRNAs (miRNAs) are crucial in cancer development and progression, and therapies targeting miRNAs demonstrate great therapeutic promise. AIM: We sought to predict the prognosis and therapeutic response of lung adenocarcinoma (LUAD) by classifying molecular subtypes and constructing a prognostic model based on miRNA-related genes. METHOD: This study was based on miRNA-mRNA action pairs and ceRNA networks in the Cancer Genome Atlas (TCGA) database. Three molecular subtypes were determined based on 64 miRNA-associated target genes identified in the ceRNA network. The S3 subtype had the best prognosis, and the S2 subtype had the worst prognosis. The S2 subtype had a higher tumor mutational load (TMB) and a lower immune score. The S2 subtype was more suitable for immunotherapy and sensitive to chemotherapy. The least absolute shrinkage and selection operator (LASSO) algorithm was performed to determine eight miRNA-associated target genes for the construction of prognostic models. RESULT: High-risk patients had a poorer prognosis, lower immune score, and lower response to immunotherapy. Robustness was confirmed in the Gene-Expression Omnibus (GEO) database cohort (GSE31210, GSE50081, and GSE37745 datasets). Overall, our study deepened the understanding of the mechanism of miRNA-related target genes in LUAD and provided new ideas for classification. CONCLUSION: Such miRNA-associated target gene characterization could be useful for prognostic prediction and contribute to therapeutic decision-making in LUAD.

Clinical significance of DNA damage response mutations in stage I and stage IIIa NSCLC.

BACKGROUND: DNA damage response (DDR) pathways are essential to sustain genomic stability and play a critical role in cancer development and progression. Here, we investigated the profile of DDR gene mutations in early-stage non-small cell lung cancer (NSCLC) and their prognostic values. METHODS: We first examined 74 DDR genes involved in seven DDR pathways and then focused on six specific genes: ATM, BRCA1, BRCA2, CHEK1, BARD1, and BRIP1. A total of 179 stage I and IIIa NSCLC patients who received curative resection in Peking Union Medical College Hospital and their corresponding samples were collected for DNA sequencing, immunohistochemistry and survival analysis. RESULTS: A total of 167 eligible patients were finally analyzed. Mutation frequencies were 82% and 26.3% for the selected 74 genes and six genes, respectively. Mismatch repair (MMR) and nucleotide excision repair (NER) alterations were observed more frequently in lung squamous cell carcinoma (LUSC) and smokers were more likely to develop the selected six DDR gene mutations than those who never smoked. Deleterious mutations in the six genes were independent prognostic indicators of significantly longer disease-free survival and overall survival. No association was found between DDR gene status and PD-L1 expression, CD8 positive lymphocyte and tumor-associated macrophage infiltration in tumor area. However, numbers of mutations were significantly increased among patients with DDR alterations. CONCLUSIONS: Deleterious mutations of these six genes were common in resected NSCLC and could serve as prognostic biomarkers.

Mesenchymal stem cell-derived extracellular vesicles in skin wound healing: roles, opportunities and challenges.

Skin wounds are characterized by injury to the skin due to trauma, tearing, cuts, or contusions. As such injuries are common to all human groups, they may at times represent a serious socioeconomic burden. Currently, increasing numbers of studies have focused on the role of mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs) in skin wound repair. As a cell-free therapy, MSC-derived EVs have shown significant application potential in the field of wound repair as a more stable and safer option than conventional cell therapy. Treatment based on MSC-derived EVs can significantly promote the repair of damaged substructures, including the regeneration of vessels, nerves, and hair follicles. In addition, MSC-derived EVs can inhibit scar formation by affecting angiogenesis-related and antifibrotic pathways in promoting macrophage polarization, wound angiogenesis, cell proliferation, and cell migration, and by inhibiting excessive extracellular matrix production. Additionally, these structures can serve as a scaffold for components used in wound repair, and they can be developed into bioengineered EVs to support trauma repair. Through the formulation of standardized culture, isolation, purification, and drug delivery strategies, exploration of the detailed mechanism of EVs will allow them to be used as clinical treatments for wound repair. In conclusion, MSC-derived EVs-based therapies have important application prospects in wound repair. Here we provide a comprehensive overview of their current status, application potential, and associated drawbacks.

Biomaterials evolution: from inert to instructive.

The field of biomaterials has experienced substantial evolution in recent years, driven by advancements in materials science and engineering. This has led to an expansion of the biomaterials definition to include biocompatibility, bioactivity, bioderived materials, and biological tissues. Consequently, the intended performance of biomaterials has shifted from a passive role wherein a biomaterial is merely accepted by the body to an active role wherein a biomaterial instructs its biological environment. In the future, the integration of bioinspired designs and dynamic behavior into fabrication technologies will revolutionize the field of biomaterials. This perspective presents the recent advances in the evolution of biomaterials in fabrication technologies and provides a brief insight into smart biomaterials.

Predictive Models for Colon Adenocarcinoma Diagnosis, Prognosis, and Immune Microenvironment Based on 2 Hypoxia-Related Genes: KDM3A and ENO3.

Background: Hypoxia is known to play a critical role in tumor occurrence, progression, prognosis, and therapy resistance. However, few studies have investigated hypoxia markers for diagnosing and predicting prognosis in colon adenocarcinoma (COAD). This study aims to identify a hypoxia genes-based biomarker for predicting COAD patients' prognosis and response to immunotherapy on an individual basis. Methods: Hypoxia-related genes were extracted from the Molecular Signatures Database. Gene expression, clinical data, and mutation data of COAD were collected retrospectively from the Cancer Genome Atlas, the Gene Expression Omnibus, and the International Cancer Genome Consortium databases. Univariate and multivariate cox regression, and the least absolute shrinkage and selection operator method were used to select the genes most associated with the prognosis of COAD patients. Kaplan-Meier survival analysis, receiver operating characteristic curves, calibration curves, and decision curve analyses were performed to validate the efficacy of the signature in predicting the prognosis of COAD patients. EdU incorporation assays, cell survival assays, western blot assays, and trans-well invasion assays were performed to further confirm the function of the screened genes in tumorigenesis. Results: ENO3 and KDM3A were identified as key genes for constructing prognostic and diagnostic signatures, which were found to be independent risk factors for predicting the prognosis and diagnosis of COAD patients. Using these signatures, COAD patients could be stratified into high-risk and low-risk groups, with the latter exhibiting better overall survival outcomes. Moreover, the high-risk group displayed elevated levels of immune checkpoint genes and tumor mutation burden, indicating that these patients may benefit from immune checkpoint inhibitor therapy. Conclusion: The signature developed in this study demonstrates excellent efficacy in prognosticating the outcomes of COAD patients. Moreover, it can serve as a valuable tool for clinicians to identify COAD patients who are suitable for ICI therapy.

The Presence of Lung Cancer Affects SARS-CoV-2 Vaccine Uptake and Attitude in Chinese Patients: A Clinical Cross-Sectional Investigation.

With the SARS-CoV-2 mutations evolving and prompt of SARS-CoV-2 vaccines, no information is available on SARS-CoV-2 vaccination status in Chinese patients with lung cancer. An electronic questionnaire including sociodemographic characteristics, vaccine status, side effect post-vaccination, and attitude towards a fourth dose of vaccine was conducted within 1018 Chinese patients with lung cancer from October 18th, 2022, to November 25th, 2022. Among 1018 patients, a total of 75 (13.7%) patients reported acceptable systemic adverse events in those had received the SARS-CoV-2 vaccine (549, 54%), the most common of which was fever (39, 7%). Factors including females (OR, 1.512; 95% CI, 1.076-2.124), residents in the municipality (OR, 2.048; 95% CI, 1.238-3.389), undergoing therapy (OR, 2.897; 95% CI, 1.348-6.226), disagree to vaccines is safe for patients with lung cancer (OR, 3.816; 95% CI, 2.198-6.626) contributed to hesitancy. Among 373 patients had received three doses, half respondents (206, 55.2%) were hesitant to receive a fourth dose due to the safety concern and efficacy towards the variants. In conclusion, low vaccine uptake rates in patients with lung cancer could be improved by increasing confidence in vaccine safety, particularly for those with negative beliefs. Appropriate guidance and individualized vaccination plans that meet the healthcare needs of patients with lung cancer were needed during the constantly evolving pandemic.

MEX3A determines in vivo hepatocellular carcinoma progression and induces resistance to sorafenib in a Hippo-dependent way.

BACKGROUND: Hepatocellular carcinoma (HCC) is most common malignant tumor worldwide, and one of the most lethal malignancies. MEX3A, RNA-binding protein, is profoundly implicated in tumor initiation and progression. But its role and potential mechanism in HCC remains fully unclear. METHODS: The expression of MEX3A in HCC was analysis using the data derived from the Cancer Genome Atlas (TCGA) dataset and further confirmed by HCC samples and cells lines. The roles of MEX3A in the proliferation, migration and sorafenib resistance were detected both in vitro and vivo. In addition, the underline mechanism was investigated. RESULTS: In this study, MEX3A expression was upregulated in HCC tissue and cell lines. Knockdown or overexpression of MEX3A disturbed the proliferation, migration and apoptosis of HCC cells by modulating the activation of Hippo signaling pathway. The expression of MEX3A was negatively associated with sorafenib sensitivity and upregulated in sorafenib resistant HCC cells. MEX3A knockdown facilitated the expression of WWC1, a negative modulator of Hippo signaling pathway, and led to increase of the phosphorylation of LATS1 and YAP1. Pharmacological inhibition of LATS1 or WWC1 overexpression alleviated the proliferative and migrated suppression and increased sorafenib sensitivity, whereas WWC1 inhibition using genetic interference strategy showed opposite trend in MEX3A knockdown HCC cells. Importantly, MEX3A knockdown led to growth and lung metastasis inhibition using xenograft model established by means of subcutaneous or tail vein injection. In addition, a combination of MEX3A knockdown and WWC1 overexpression dramatically enhances the growth inhibition of sorafenib in vivo. CONCLUSION: MEX3A may facilitate HCC progression and hinder sorafenib sensitivity via inactivating Hippo signaling. The present study suggested that targeting MEX3A can be served as a novel therapeutic strategy for HCC.

Analysis of cytokines in bronchoalveolar lavage fluid in patients with checkpoint inhibitor pneumonitis and pulmonary infection: A case-control study.

BACKGROUND: The discovery of immune checkpoint inhibitors (ICIs) is a breakthrough in the field of cancer therapy. However, ICIs may cause immune-related adverse reactions, including checkpoint inhibitor-related pneumonitis (CIP). The aim of this study was to investigate cytokines in bronchoalveolar lavage fluid (BALF) of patients with CIP compared with patients with pulmonary infection and patients with cancer. METHODS: We retrospectively analyzed 34 cytokine levels and T cell subsets in BALF supernatant samples from ICI-treated patients with CIP (n = 13), pulmonary infection (n = 10), and progressive cancer (n = 12). Cytokine levels and T cell subsets were compared among the three groups of patients. RESULTS: We observed significantly higher levels of IFN-γ-induced protein 10(IP-10) (p = 0.002), and percentage of CD3 + CD8 + T cells (p = 0.020) in BALF of patients with CIP compared with the other two groups. However, we found significantly lower levels of interleukin-21 (p = 0.008) in BALF of patients with progressive disease compared with the other two groups. CONCLUSIONS: Cytokine profile and character of cell subsets in BALF was helpful for the differential diagnosis of CIP. IP-10 may play an important role in pathophysiology for CIP and also be a potential therapeutic target.