Purpose: Dual anti–human epidermal growth factor receptor 2 (HER2) drugs have become the standard regimen for neoadjuvant systemic treatment (NST) to HER2-positive breast cancer patients. However, the efficacy varies greatly among patients with different HER2 protein expression levels. Materials and Methods: A total of 575 HER2-positive breast cancer patients from multiple centers throughout China from 2013 to 2022 were retrospectively analyzed. We compared clinicopathological features in different HER2 immunohistochemistry classes (HER2 2+/in situ hybridization [ISH] + or HER2 3+), and their difference in response to NST and survival with single or dual anti-HER2 drugs. Drug sensitivity assays were used to evaluate different efficacy of anti-HER2 drugs in vitro. Results: Compared to HER2 3+ subgroup, the HER2 2+/ISH+ group had a higher proportion of hormone receptor–positive status (48.7% vs. 76.1%, p < 0.001), more HER2 protein loss after NST, lower pathological complete response (pCR) rate (46.07% vs. 16.24%, p < 0.001), and tended to have worse disease-free survival (DFS). In HER2 2+/ISH+ patients, treated with pertuzumab and trastuzumab in combination had no significant improvement in pCR (19.12% vs. 12.24%, p=0.287) and DFS (p=0.908) than using alone. Drug sensitivity assay showed poor efficacy with dual anti-HER2 drugs in HER2 2+/ISH+ cell lines; however, fam-trastuzumab deruxtecan drugs had a satisfactory effect. Conclusion Owing to the differences in clinicopathological features and treatment efficacy, we considered the HER2 2+/ISH+ group to be a distinct subtype and defined it as the HER2-moderate–positive subgroup. In this subgroup, dual anti-HER2 drugs did not exert significant improvement in pCR and DFS. Therefore, treatment optimization is warranted, with antibody-drug conjugate drugs as potential options.

Objective To analyze the nonlinear relationship between hypothermic machine perfusion (HMP) parameters and delayed graft function (DGF) and optimize the construction of a predictive model for DGF. Methods The data of 923 recipients who underwent kidney transplantation from deceased donors were retrospectively analyzed. According to the occurrence of DGF, the recipients were divided into DGF group (n=823) and non-DGF group (n=100). Donor data, HMP parameters and recipient data were analyzed for both groups. The nonlinear relationship between HMP parameters and the occurrence of DGF was explored based on restricted cubic splines (RCS). Over-sampling, under-sampling and balanced sampling were used to address the imbalance in the proportion of DGF to construct logistic regression predictive models. The area under the curve (AUC) of each model was compared in the validation set, and a nomogram model was constructed. Results Donor BMI, cold ischemia time of the donor kidney, and HMP parameters (initial and final pressures, resistance, and perfusion time) were significantly different between the DGF and non-DGF groups (all P<0.05). The RCS analysis revealed a threshold-like nonlinear relationship between HMP parameters and the risk of DGF. Among the models constructed using different sampling methods, the balanced sampling model had the highest AUC. Using this model, a nomogram was constructed to stratify recipients based on risk scores. Recipients in the high-risk group had higher serum creatinine levels at 1, 6, and 12 months after kidney transplantation compared to those in the low-risk group (all P<0.05). Conclusions There is a nonlinear relationship between HMP parameters and the risk of DGF, and the threshold is helpful for organ quality assessment and monitoring of graft function after transplantation. The predictive model for DGF constructed on the base of balanced sampling algorithms helps perioperative decision-making and postoperative graft function monitoring of kidney transplantation.

OBJECTIVES: To investigate the prognostic significance of PDZ-binding kinase (PBK) in pan-cancer and its potential as a therapeutic target for pancreatic cancer. METHODS: PBK expression levels were investigated in 33 cancer types based on data from TCGA, GEO and CPTAC databases. RT-PCR and Western blotting were employed to examine PBK expression in clinical pancreatic cancer specimens and cell lines. The diagnostic and prognostic value of PBK in pancreatic cancer was evaluated using survival analysis, Cox regression analysis, ROC curve analysis, and clinical correlation studies. Gene enrichment and immune correlation analyses were conducted to explore the potential role of PBK in tumor microenvironment, and its correlation with drug sensitivity was investigated using GDSC and CTRP datasets. In pancreatic cancer BXPC-3 cells, the effects of lentivirus-mediated PBK knockdown on cell proliferation, migration, and invasion were examined using CCK-8, colony formation, and Transwell assays. The interaction between PBK and non-SMC condensin II complex subunit G2 (NCAPG2) was analyzed using co-immunoprecipitation and Western blotting. RESULTS: PBK was overexpressed in multiple cancer types, including pancreatic cancer. A high PBK expression was associated with a poor prognosis of the patients and correlated with immune infiltration and alterations in the tumor microenvironment. Elevated PBK expression was positively correlated with the sensitivity to MEK inhibitors (Trametinib) and EGFR inhibitors (Afatinib) but negatively with the sensitivity to Bcl-2 inhibitors (TW37) and niclosamide. In BXPC-3 cells, PBK knockdown significantly suppressed NCAPG2 expression and inhibited cell proliferation, migration, and invasion. Co-immunoprecipitation confirmed a direct binding between PBK and NCAPG2. CONCLUSIONS PBK is a key regulator of pancreatic cancer and interacts with NCAPG2 to promote tumor progression, suggesting its value as a potential biomarker and therapeutic target for pancreatic cancer.
Humans ; Pancreatic Neoplasms/genetics* ; Prognosis ; Biomarkers, Tumor/genetics* ; Cell Line, Tumor ; Cell Proliferation ; Adenocarcinoma/metabolism* ; Tumor Microenvironment ; Cell Movement ; Mitogen-Activated Protein Kinase Kinases

OBJECTIVE: To summarize the latest research progress of graphene and its derivatives (GDs) in bone repair. METHODS: The relevant research literature at home and abroad in recent years was extensively accessed. The properties of GDs in bone repair materials, including mechanical properties, electrical conductivity, and antibacterial properties, were systematically summarized, and the unique advantages of GDs in material preparation, functionalization, and application, as well as the contributions and challenges to bone tissue engineering, were discussed. RESULTS: The application of GDs in bone repair materials has broad prospects, and the functionalization and modification technology effectively improve the osteogenic activity and material properties of GDs. GDs can induce osteogenic differentiation of stem cells through specific signaling pathways and promote osteogenic activity through immunomodulatory mechanisms. In addition, the parameters of GDs have significant effects on the cytotoxicity and degradation behavior. CONCLUSION GDs has great potential in the field of bone repair because of its excellent physical and chemical properties and biological properties. However, the cytotoxicity, biodegradability, and functionalization strategies of GDs still need to be further studied in order to achieve a wider application in the field of bone tissue engineering.
Graphite/pharmacology* ; Tissue Engineering/methods* ; Humans ; Osteogenesis/drug effects* ; Biocompatible Materials/pharmacology* ; Bone Regeneration ; Tissue Scaffolds/chemistry* ; Cell Differentiation ; Bone and Bones ; Bone Substitutes/chemistry* ; Animals

In the earliest existing pharmacological monograph in China,Shennong Bencao Jing,there is a record of a parasitic plant known as"desert ginseng"——Cistanche deserticola.It exerts beneficial effects on the function of the heart,kidney,spleen,and lung.As research into its pharmacological properties has progressed,the active components of Cistanche deserticola also have shown promise for treating intestinal disorders.Due to the complex pathological mechanisms of sepsis,which often accompany multiple organ dysfunction,aligns closely with the multi-target pharmacological characteristics of Cistanche deserticola's.Furthermore,because Cistanche deserticola is both edible and medicinal,and exhibits a wide therapeutic window,it has a natural advantage in the clinical transformation and application after drug development.Currently,to expand its medicinal range,a review of the main active components of Cistanche deserticola and their related pharmacological effects is conducted,and combined with the specific characteristics of intestinal injury in sepsis,the potential value of Cistanche deserticola in intestinal barrier protection,microbial coordination,and intestinal motility regulation is further elaborated,providing new ideas and a theoretical basis for the application of Cistanche deserticola in sepsis-induced intestinal injury.

Objective:To analyze the screening results of the Urban Cancer Early Diagnosis and Treatment Project in Qinghai Province from 2019 to 2024.Methods:A summary and statistical analysis were conducted on six years of screening data from the Urban Cancer Early Dia-gnosis and Treatment Program in Qinghai Province,with the high-risk rate,screening rate,and detection rate calculated separately for each type of cancer.Results:From 2019 to 2024,56,882 high-risk individuals were identified.The high-risk rates for lung,colorectal,breast,up-per gastrointestinal,and liver cancer were 22.02%,21.57%,14.23%,13.52%,and 6.10%,respectively.Overall,13,592 individuals com-pleted clinical screening,with detection rates of 0.32%for lung cancer,0.41%for liver cancer,0.08%for precancerous gastric lesions,3.63%for precancerous colorectal lesions,0.08%for esophageal cancer,0.16%for gastric cancer,and 0.14%for colorectal cancer.Conclusions:The implementation of the Urban Cancer Early Diagnosis and Treatment Program in Qinghai Province aids in the early detection of cancer,improves early diagnosis and survival rates,and reduces mortality.Nevertheless,due to low public awareness and limited participation,en-hancements in program management and public outreach are required.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective:To report the preliminary clinical exploration result of spatially fractionated radiation therapy (SFPT) using the pencil-beam-scanning (PBS) technique at a single center for the treatment of patients with bulky tumors.Methods:Data on the clinical characteristics, tumor characteristics, and dosimetric parameters were retrospectively collected from patients with bulky tumors at the Radiation Oncology Department, Ion Medical Center, First Affiliated Hospital of University of Science and Technology of China (i.e., the Hefei Ion Medical Center) from April 2024 to December 2024. Three-dimensional lattice radiotherapy (LRT) was primarily utilized in the SFRT, with multi-field robust optimization performed using pencil beam scanning. SFRT target volumes (STVs) were defined as 1.0 cm-diameter spheres. Primary observation indicators included the remission rate of tumor-associated symptoms, followed by the local tumor control rate.Results:A total of 12 patients were enrolled in this study, with a median age of 61 years (28-85 years). The primary tumors included hepatocellular carcinoma (six patients), sarcomas (three patients), and lung cancer (two patients). Eight patients received concurrent systemic therapy. The SFRT plans showed a median gross tumor volume (GTV) of 429.63 cm 3 (120.60-2 053.30 cm 3), a median STV number of 8 (3-20), a single-fraction dose to STVs of 10 GyE, a median irradiation quantity of 6 (3-8), a median STV of 8 cm 3 (3-20 cm 3), a median STV proportion of 2.09% (0.62%-3.30%), a median GTV corresponding to a single STV of 52.91 cm 3 (30.25-159.82 cm 3), and a median peak-to-valley dose ratio of 3.37 (2.29-7.60). All patients received conventionally fractionated proton therapy (CFPT), with a median prescription dose of 50 GyE (40-60 GyE). Furthermore, these patients showed a median follow-up time of 174 d (133-235 d), a remission rate of tumor-associated symptoms of 75%, and a local control rate of 100%. Four patients experienced grade 1-2 treatment-related adverse events, suggesting high overall tolerability of the patients. Conclusions:SFRT represents a promising technique with high control rates and tolerability for bulky tumors, providing the possibility for quick symptom relief and the control of tumor progression.