UNASSIGNED: Clear cell renal cell carcinoma (ccRCC) is a metabolic disorder characterized by abnormal lipid accumulation in the cytoplasm. Lipid metabolism-related genes may have important clinical significance for prognosis prediction and individualized treatment.
UNASSIGNED: We collected bulk and single-cell transcriptomic data of ccRCC and normal samples to identify key lipid metabolism-related prognostic signatures. qPCR was used to confirm the expression of signatures in cancer cell lines. Based on the identified signatures, we developed a lipid metabolism risk score (LMRS) as a risk index. We explored the potential application value of prognostic signatures and LMRS in precise treatment from multiple perspectives.
UNASSIGNED: Through comprehensive analysis, we identified five lipid metabolism-related prognostic signatures (ACADM, ACAT1, ECHS1, HPGD, DGKZ). We developed a risk index LMRS, which was significantly associated with poor prognosis in patients. There was a significant correlation between LMRS and the infiltration levels of multiple immune cells. Patients with high LMRS may be more likely to respond to immunotherapy. The different LMRS groups were suitable for different anticancer drug treatment regimens.
UNASSIGNED: Prognostic signatures and LMRS we developed may be applied to the risk assessment of ccRCC patients, which may have potential guiding significance in the diagnosis and precise treatment of ccRCC patients.

Lung cancer is categorized into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer. Of these, NSCLC accounts for approximately 85% of all cases and encompasses varieties such as squamous cell carcinoma and adenocarcinoma. For patients with advanced NSCLC that do not have oncogene addiction, the preferred treatment approach is a combination of immunotherapy and chemotherapy. However, the progression-free survival (PFS) typically ranges only from about 6 to 8 months, accompanied by certain adverse events. In order to carry out individualized treatment more effectively, it is urgent to accurately screen patients with PFS for more than 12 months under this treatment regimen. Therefore, this study undertook a retrospective collection of pulmonary CT images from 60 patients diagnosed with NSCLC treated at the First Affiliated Hospital of Wenzhou Medical University. It developed a machine learning model, designated as bSGSRIME-SVM, which integrates the rime optimization algorithm with self-adaptive Gaussian kernel probability search (SGSRIME) and support vector machine (SVM) classifier. Specifically, the model initiates its process by employing the SGSRIME algorithm to identify pivotal image features. Subsequently, it utilizes an SVM classifier to assess these features, aiming to enhance the model\'s predictive accuracy. Initially, the superior optimization capability and robustness of SGSRIME in IEEE CEC 2017 benchmark functions were validated. Subsequently, employing color moments and gray-level co-occurrence matrix methods, image features were extracted from images of 60 NSCLC patients undergoing immunotherapy combined with chemotherapy. The developed model was then utilized for analysis. The results indicate a significant advantage of the model in predicting the efficacy of immunotherapy combined with chemotherapy for NSCLC, with an accuracy of 92.381% and a specificity of 96.667%. This lays the foundation for more accurate PFS predictions and personalized treatment plans.

BACKGROUND: Programmed death 1 (PD-1)/programmed death 1 ligand 1 (PD-L1)-directed immunotherapy has revolutionized the treatments for advanced non-small cell lung cancer (NSCLC), whereas the optimal therapeutic combinations remain uncertain.
METHODS: Our study encompassed phase Ⅱ/III randomized controlled trials (RCTs) that involved anti-PD-(L)1-based therapies for stage-IV NSCLC. The primary outcomes included overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and incidences of adverse events (AEs). Subgroup analyses were conducted by treatment lines, PD-L1 expression levels, histological types, and metastatic sites.
RESULTS: Our analysis incorporated 38 publications, covering 14 therapeutic combinations and involving 18,048 participants. PD-(L)1+chemotherapy (CT), PD-(L)1+ cytotoxic T lymphocyte-associated antigen-4 (CTLA4) +CT, and PD-(L)1+ T-cell immunoglobulin and ITIM domain (TIGIT) were notably effective in prolonging OS. Overall, PD-(L)1+CT and PD-(L)1+CT+ vascular endothelial growth factor (VEGF) were significantly beneficial for PFS and ORR. As for the subsequent-line treatments, incorporating radiotherapy can enhance PFS and ORR (ranked fourth among enrolled treatments). For patients with PD-L1 < 1%, PD-(L)1+CT+VEGF and PD-(L)1+CTLA4+CT were favorable approaches. Conversely, in patients with PD-L1 ≥ 50%, PD-(L)1+CT represented an effective treatment. Patients with non-squamous cell carcinoma or liver metastases might benefit from the addition of VEGF. In cases of squamous cell carcinoma or brain metastases, the combination of PD-(L)1+CTLA4+CT yielded superior benefits.
CONCLUSIONS: This study underscores the enhanced efficacy of combination immunotherapies over monotherapy. It highlights the necessity for personalized treatment, considering individual factors. These insights are vital for clinical decision-making in the management of advanced NSCLC.

Myasthenia gravis (MG) is an antibody-mediated autoimmune disease with a prevalence of 150-250 cases per million individuals. Autoantibodies include long-lived antibodies against the acetylcholine receptor (AChR), mainly of the IgG1 subclass, and IgG4, produced almost exclusively by short-lived plasmablasts, which are prevalent in muscle-specific tyrosine kinase (MuSK) myasthenia gravis. Numerous investigations have demonstrated that MG patients receiving conventional medication today still do not possess satisfactory symptom control, indicating a substantial disease burden. Subsequently, based on the type of the autoantibody and the pathogenesis, we synthesized the published material to date and reached a conclusion regarding the literature related to personalized targeted therapy for MG. Novel agents for AChR MG have shown their efficacy in clinical research, such as complement inhibitors, FcRn receptor antagonists, and B-cell activating factor (BAFF) inhibitors. Rituximab, a representative drug of anti-CD20 therapy, has demonstrated benefits in treatment of MuSK MG patients. Due to the existence of low-affinity antibodies or unidentified antibodies that are inaccessible by existing methods, the treatment for seronegative MG remains complicated; thus, special testing and therapy considerations are necessary. It may be advantageous to initiate the application of novel biologicals at an early stage of the disease. Currently, therapies can also be combined and individualized according to different types of antibodies. With such a wide range of drugs, how to tailor treatment strategies to patients with various conditions and find the most suitable solution for each MG profile are our necessary and urgent aims.

Lipopolysaccharide (LPS) is widely used to establish various animal models, including models of acute lung injury, cardiomyocyte damage, and acute kidney injury. Currently, there is no consensus on the diagnosis and treatment of LPS-induced disease. We herein present a case series of four patients who developed dose-dependent multi-organ injury, including acute lung injury and acute kidney injury, after inhaling LPS gas in a sealed room. These patients exhibited varying degrees of multi-organ injury characterized by inflammatory cell infiltration and secretion of proinflammatory cytokines. One patient showed progressive symptoms even with active treatment, leading to mild pulmonary fibrosis. This study emphasizes the importance of early diagnosis and treatment of significant LPS exposure and suggests personalized treatment approaches for managing LPS poisoning.

BACKGROUND: Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients.
METHODS: Utilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan-Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients.
RESULTS: The research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker.
CONCLUSIONS: The identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

Tumor vaccines have been considered a promising therapeutic approach for treating cancer in recent years. With the development of sequencing technologies, tumor vaccines based on neoantigens or genomes specifically expressed in tumor cells, mainly in the form of peptides, nucleic acids, and dendritic cells, are beginning to receive widespread attention. Therefore, in this review, we have introduced different forms of neoantigen vaccines and discussed the development of these vaccines in treating cancer. Furthermore, neoantigen vaccines are influenced by factors such as antigen stability, weak immunogenicity, and biosafety in addition to sequencing technology. Hence, the biological nanomaterials, polymeric nanomaterials, inorganic nanomaterials, etc., used as vaccine carriers are principally summarized here, which may contribute to the design of neoantigen vaccines for improved stability and better efficacy.

UNASSIGNED: The proteasome is a crucial mechanism that regulates protein fate and eliminates misfolded proteins, playing a significant role in cellular processes. In the context of lung cancer, the proteasome\'s regulatory function is closely associated with the disease\'s pathophysiology, revealing multiple connections within the cell. Therefore, studying proteasome inhibitors as a means to identify potential pathways in carcinogenesis and metastatic progression is crucial in in-depth insight into its molecular mechanism and discovery of new therapeutic target to improve its therapy, and establishing effective biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized treatment for lung squamous carcinoma in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine).
UNASSIGNED: This study identified differentially expressed proteasome genes (DEPGs) in lung squamous carcinoma (LUSC) and developed a gene signature validated through Kaplan-Meier analysis and ROC curves. The study used WGCNA analysis to identify proteasome co-expression gene modules and their interactions with the immune system. NMF analysis delineated distinct LUSC subtypes based on proteasome gene expression patterns, while ssGSEA analysis quantified immune gene-set abundance and classified immune subtypes within LUSC samples. Furthermore, the study examined correlations between clinicopathological attributes, immune checkpoints, immune scores, immune cell composition, and mutation status across different risk score groups, NMF clusters, and immunity clusters.
UNASSIGNED: This study utilized DEPGs to develop an eleven-proteasome gene-signature prognostic model for LUSC, which divided samples into high-risk and low-risk groups with significant overall survival differences. NMF analysis identified six distinct LUSC clusters associated with overall survival. Additionally, ssGSEA analysis classified LUSC samples into four immune subtypes based on the abundance of immune cell infiltration with clinical relevance. A total of 145 DEGs were identified between high-risk and low-risk score groups, which had significant biological effects. Moreover, PSMD11 was found to promote LUSC progression by depending on the ubiquitin-proteasome system for degradation.
UNASSIGNED: Ubiquitinated proteasome genes were effective in developing a prognostic model for LUSC patients. The study emphasized the critical role of proteasomes in LUSC processes, such as drug sensitivity, immune microenvironment, and mutation status. These data will contribute to the clinically relevant stratification of LUSC patients for personalized 3P medical approach. Further, we also recommend the application of the ubiquitinated proteasome system in multi-level diagnostics including multi-omics, liquid biopsy, prediction and targeted prevention of chronic inflammation and metastatic disease, and mitochondrial health-related biomarkers, for LUSC 3PM practice.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13167-024-00352-w.

Pancreatic cancer, an exceptionally malignant tumor of the digestive system, presents a challenge due to its lack of typical early symptoms and highly invasive nature. The majority of pancreatic cancer patients are diagnosed when curative surgical resection is no longer possible, resulting in a poor overall prognosis. In recent years, the rapid progress of Artificial intelligence (AI) in the medical field has led to the extensive utilization of machine learning and deep learning as the prevailing approaches. Various models based on AI technology have been employed in the early screening, diagnosis, treatment, and prognostic prediction of pancreatic cancer patients. Furthermore, the development and application of three-dimensional visualization and augmented reality navigation techniques have also found their way into pancreatic cancer surgery. This article provides a concise summary of the current state of AI technology in pancreatic cancer and offers a promising outlook for its future applications.

Cisplatin-containing combination chemotherapy has been used as the standard treatment for bladder cancer patients at advanced stage. However, nearly 50% of patients are nonresponders. To guide the selection of more effective chemotherapeutic agents, a bladder cancer spheroids microfluidic drug sensitivity analysis system was established in this study. Bladder cancer spheroids were established and successfully cultured in a customized microfluidic device to assess their response to different chemotherapeutic agents. The in vitro drug sensitivity results were also compared to patient-derived xenograft (PDX) models and clinical responses of patients. As a result, bladder cancer spheroids faithfully recapitulate the histopathological and genetic features of their corresponding parental tumors. Furthermore, the in vitro drug sensitivity outcomes of spheroids (n = 8) demonstrated a high level of correlation with the PDX (n = 2) and clinical response in patients (n = 2). Our study highlights the potential of combining bladder cancer spheroids and microfluidic devices as an efficient and accurate platform for personalized selection of chemotherapeutic agents.