[Research Progress on Predictive Biomarkers of Immunotherapy Efficacy
in Non-small Cell Lung Cancer].

Lung cancer is one of the most common malignant tumors in the world, of which non-small cell lung cancer (NSCLC) is the majority. The emergence of immune checkpoint inhibitors (ICIs) has greatly changed the treatment strategy of NSCLC and improved the prognosis of patients. However, in reality, only a small number of patients can achieve long-term benefit. Therefore, the identification of reliable predictive biomarkers is essential for the selection of treatment modalities. With the development of molecular biology and genome sequencing technology in recent years, as well as the in-depth understanding of tumor and its host immune microenvironment, research on biomarkers has emerged in an endless stream. This review focuses on the predictive biomarkers of immunotherapy efficacy in NSCLC, in order to provide some guidance for precision immunotherapy.
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Immuno-oncologic profiling of pediatric brain tumors reveals major clinical significance of the tumor immune microenvironment.

With the success of immunotherapy in cancer, understanding the tumor immune microenvironment (TIME) has become increasingly important; however in pediatric brain tumors this remains poorly characterized. Accordingly, we developed a clinical immune-oncology gene expression assay and used it to profile a diverse range of 1382 samples with detailed clinical and molecular annotation. In low-grade gliomas we identify distinct patterns of immune activation with prognostic significance in BRAF V600E-mutant tumors. In high-grade gliomas, we observe immune activation and T-cell infiltrates in tumors that have historically been considered immune cold, as well as genomic correlates of inflammation levels. In mismatch repair deficient high-grade gliomas, we find that high tumor inflammation signature is a significant predictor of response to immune checkpoint inhibition, and demonstrate the potential for multimodal biomarkers to improve treatment stratification. Importantly, while overall patterns of immune activation are observed for histologically and genetically defined tumor types, there is significant variability within each entity, indicating that the TIME must be evaluated as an independent feature from diagnosis. In sum, in addition to the histology and molecular profile, this work underscores the importance of reporting on the TIME as an essential axis of cancer diagnosis in the era of personalized medicine.

Immunological assay using a solid-state pore with a low limit of detection.

Emerging infectious diseases, cancer, and other diseases are quickly tested mainly via immune reactions based on specific molecular recognition between antigens and antibodies. By changing the diameter of solid-state pores, biomolecules of various sizes can be rapidly detected at the single-molecule level. The combination of immunoreactions and solid-state pores paves the way for an efficient testing method with high specificity and sensitivity. The challenge in developing this method is achieving quantitative analysis using solid-state pores. Here, we demonstrate a method with a low limit of detection for testing tumor markers using a combination of immunoreactions and solid-state pore technology. Quantitative analysis of the mixing ratio of two and three beads with different diameters was achieved with an error rate of up to 4.7%. The hybrid solid-state pore and immunoreaction methods with prostate-specific antigen (PSA) and anti-PSA antibody-modified beads achieved a detection limit of 24.9 fM PSA in 30 min. The hybrid solid-state pore and immunoreaction enabled the rapid development of easy-to-use tests with lower limit of detection and greater throughput than commercially available immunoassay for point-of-care testing.

Interleukin-10 as a marker for response to dendritic cells-dribbles immunotherapy in hepatocellular carcinoma, a mice model.

Cancer immunotherapy is a promising strategy in cancer management, including hepatocellular carcinoma (HCC). This experimental study aimed to evaluate interleukin-10 (IL-10) as a biomarker for monitoring the response of tumor-derived autophagosomes vaccine in inducing antitumor immunity in HCC induced mice. It was conducted on 56 BALB/c mice; divided into 20 normal and 36, cancer induced with human liver cancer cell line (HepG2) cells. The latter group was subdivided into a positive control group (n=6) and a treated group (n=30), that was subdivided into 3 subgroups: (A) treated with dendritic cells (DC) vaccine only, (B) treated with vaccine named Dribbles only, and (C) treated with DC plus Dribbles. Serum IL-10 was assessed after immunotherapy. The mean percentage of tumor volume reduction in mice vaccinated by DC plus Dribbles was significantly superior to DC and Dribbles groups (p= 0.013, and p= 0.043, respectively). There was a statistically significant difference in IL-10 levels between different immunotherapy groups (p= 0.0003). As the mean IL-10 level was 19.50 pg/ml for the positive control group, 13 pg/ml for Dribbles group, 10 pg/ml for DCs group and 3.50 pg/ml for DCs plus Dribbles group. We conclude that DC-Dribbles vaccine has a remarkable efficacy superior to either Dribbles alone or DC alone in the decline of HCC development and survival improvement. IL-10 is a predictive biomarker for response after immunotherapy.

Nanomaterial-Mediated Immunosensors and Their Performance in Detecting Tumor Markers.

The detection of tumor markers is crucial for assessing the progression of specific cancers. Numerous research studies have shown that immunosensors can convert immune-specific response biosignals into visual signals, enabling the highly sensitive tracking and detection of tumor markers. This offers a promising solution for early cancer diagnosis. However, most tumor markers are inert molecules that are challenging to detect at low concentrations in the early stages of cancer. Therefore, there is a need to develop immunosensor analysis platforms with a higher sensitivity. Nanomaterials, with their advantages of high stability, low cost, and versatility in design, have emerged as ideal candidates for enhancing the performance of immunosensor analysis. In this paper, we review the design ideas of nanomaterials in antibody-based electrochemical, electrochemiluminescent, and photoelectrochemical immunosensors, including electrode interface modification, signaling probes for stimulating sensing signals, and design strategies of modified materials in signaling mechanisms. In addition, we have thoroughly analyzed the performance, advantages and disadvantages of different immunosensors. Therefore, the aim of this paper is to review the recent advances in advanced nanomaterial strategies for different immunosensors and their biomedical applications, and to point out the challenges and prospects of immunosensors in future clinical applications.

Integrated analysis reveals a novel 5-fluorouracil resistance-based prognostic signature with promising implications for predicting the efficacy of chemotherapy and immunotherapy in patients with colorectal cancer.

BACKGROUND: 5-Fluorouracil (5-FU) has been used as a standard first-line treatment for colorectal cancer (CRC) patients. Although 5-FU-based chemotherapy and immune checkpoint blockade (ICB) have achieved success in treating CRC, drug resistance and low response rates remain substantial limitations. Thus, it is necessary to construct a 5-FU resistance-related signature (5-FRSig) to predict patient prognosis and identify ideal patients for chemotherapy and immunotherapy. METHODS: Using bulk and single-cell RNA sequencing data, we established and validated a novel 5-FRSig model using stepwise regression and multiple CRC cohorts and evaluated its associations with the prognosis, clinical features, immune status, immunotherapy, neoadjuvant therapy, and drug sensitivity of CRC patients through various bioinformatics algorithms. Unsupervised consensus clustering was performed to categorize the 5-FU resistance-related molecular subtypes of CRC. The expression levels of 5-FRSig, immune checkpoints, and immunoregulators were determined using quantitative real-time polymerase chain reaction (RT‒qPCR). Potential small-molecule agents were identified via Connectivity Map (CMap) and molecular docking. RESULTS: The 5-FRSig and cluster were confirmed as independent prognostic factors in CRC, as patients in the low-risk group and Cluster 1 had a better prognosis. Notably, 5-FRSig was significantly associated with 5-FU sensitivity, chemotherapy response, immune cell infiltration, immunoreactivity phenotype, immunotherapy efficiency, and drug selection. We predicted 10 potential compounds that bind to the core targets of 5-FRSig with the highest affinity. CONCLUSION: We developed a valid 5-FRSig to predict the prognosis, chemotherapeutic response, and immune status of CRC patients, thus optimizing the therapeutic benefits of chemotherapy combined with immunotherapy, which can facilitate the development of personalized treatments and novel molecular targeted therapies for patients with CRC.

Anti-BIRC5 autoantibody serves as a valuable biomarker for diagnosing AFP-negative hepatocellular carcinoma.

Background: Autoantibodies targeting tumor-associated antigens (TAAbs) have emerged as promising biomarkers for early cancer detection. This research aimed to assess the diagnostic capacity of anti-BIRC5 autoantibody in detecting AFP-negative hepatocellular carcinoma (ANHCC). Methods: This research was carried out in three stages (discovery phase, validation phase, and evaluation phase) and included a total of 744 participants. Firstly, the anti-BIRC5 autoantibody was discovered using protein microarray, exhibiting a higher positive rate in ANHCC samples (ANHCCs) compared to normal control samples (NCs). Secondly, the anti-BIRC5 autoantibody was validated through enzyme-linked immunosorbent assay (ELISA) in 85 ANHCCs and 85 NCs from two clinical centers (Zhengzhou and Nanchang). Lastly, the diagnostic usefulness of the anti-BIRC5 autoantibody for hepatocellular carcinoma (HCC) was evaluated by ELISA in a cohort consisting of an additional 149 AFP-positive hepatocellular carcinoma samples (APHCCs), 95 ANHCCs and 244 NCs. The association of elevated autoantibody to high expression of BIRC5 in HCC was further explored by the database from prognosis, immune infiltration, DNA methylation, and gene mutation level. Results: In the validation phase, the area under the ROC curve (AUC) of anti-BIRC5 autoantibody to distinguish ANHCCs from NCs in Zhengzhou and Nanchang centers was 0.733 and 0.745, respectively. In the evaluation phase, the AUCs of anti-BIRC5 autoantibody for identifying ANHCCs and HCCs from NCs were 0.738 and 0.726, respectively. Furthermore, when combined with AFP, the AUC for identifying HCCs from NCs increased to 0.914 with a sensitivity of 77.5% and specificity of 91.8%. High expression of BIRC5 gene is not only correlated with poor prognosis of HCCs, but also significantly associated with infiltration of immune cells, DNA methylation, and gene mutation. Conclusion: The findings suggest that the anti-BIRC5 autoantibody could serve as a potential biomarker for ANHCC, in addition to its supplementary role alongside AFP in the diagnosis of HCC. Next, we can carry out specific verification and explore the function of anti-BIRC5 autoantibody in the occurrence and development of HCC.

Alteration of the immune microenvironment in the axillary metastatic lymph nodes of luminal A breast cancer patients.

BACKGROUND: The alteration of the immune microenvironment in the axillary metastatic lymph nodes of luminal A breast cancer patients is still unclear. METHODS: Postsurgical tissues from the enrolled luminal A BCs were divided into five categories: primary BC lesion at stage N0 (PL1), primary BC lesion at stage N1 (PL2), negative axillary lymph node at stage N0 BC (LN1), negative axillary lymph node at stage N1 BC (LN2), and positive axillary lymph node at stage N1 BC (LN3). The frequencies of positive immune markers (CD4, CD8, PD1, PD-L1, T-cell immunoglobulin and mucin domain 3 (TIM3), and forkhead box protein 3 (Foxp3)) in the above tissues were quantified by AKOYA Opal Polaris 7 Color Manual IHC Detection Kit. RESULTS: A total of 50 female patients with luminal A BC were enrolled in this study. Among these patients, 23 had stage N1 disease, and 27 had stage N0 disease. Compared with that in the PL2 subgroup, the frequency of PD-1-positive cells was significantly greater in the PL1 subgroup, whether at the stromal or intratumoral level (P value < 0.05). Both the frequency of CD8 + T cells in LN1 and that in LN2 were significantly greater than that in LN3 (P value < 0.05). The frequency of TIM3 + T cells in LN1 was significantly greater than that in PL1 (P value < 0.05). The frequency of CD8 + TIM3 + T cells was significantly greater in both the LN2 and LN3 groups than in the PL2 group (P value < 0.05). The frequency of CD4 + Foxp3 + T cells was significantly greater in LN1 than in PL1 (P value < 0.05), which was the same for both LN3 and PL2 (P value < 0.05). CONCLUSION: Increased frequencies of CD8 + PD1+, CD8 + TIM3 + and CD4 + Foxp3 + T cells might inhibit the immune microenvironment of axillary metastatic lymph nodes in luminal A breast cancer patients and subsequently promote lymph node metastasis.

CRET-based immunoassay on magnetic beads for selective and sensitive detection of Nanog antigen as a key cancer stem cell marker.

A method is presented for chemiluminescence resonance energy transfer (CRET) using APTES-Fe3O4 as a highly efficient energy acceptor with strong magnetic effectiveness over extended distances, while an Au@BSA-luminol composite acts as the donor. In order to boost the chemiluminescence reactions, CuO nanoparticles were successfully employed. The distance between the donor and acceptor is a crucial factor in the occurrence of the CRET phenomenon. A sensitive and high-throughput sandwich chemiluminescence immunosensor has been developed accordingly with a linear range of 1.0 × 10-7 g/L to 6.0 × 10-5 g/L and a limit of detection of 0.8 × 10-7 g/L. The CRET-based sandwich immunosensor has the potential to be implemented to early cancer diagnosis because of its high sensitivity in detecting Nanog, fast analysis (30 min), and simplicity. Furthermore, this approach has the potential to be adapted for the recognition of other antigen-antibody immune complexes by utilizing the corresponding antigens and their selective antibodies.

Comprehensive peripheral blood immunoprofiling reveals five immunotypes with immunotherapy response characteristics in patients with cancer.

The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient's immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types and gene expression profiles. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.

Development of a Risk Model and Genotyping Patterns Based on Disulfidptosis-Related lncRNAs to Predict Prognosis and Immune Landscape in Osteosarcoma.

BACKGROUND: Osteosarcoma (OS) is the most prevalent orthopedic malignancy with a dismal prognosis. Disulfidptosis-related lncRNAs (DRLncs) may be related to the progression of OS, but their potential molecular regulatory role is still unclear. METHODS: Based on the data collected from The Cancer Genome Atlas (TCGA), we conducted correlation analysis and the univariate Cox analysis to screen prognosis-related DRLncs, followed by developing genotyping patterns and corresponding classifier. Subsequently, the survival analysis, enrichment analysis, drug sensitivity analysis and immune infiltration analysis were performed. Afterward, multivariate Cox regression was used to construct a risk model, which was further validated by the receiver operating characteristic (ROC) curve. The aberrant expression of hub DRLncs in OS was validated using the Reverse Transcription Polymerase Chain Reaction (RT-qPCR) assay. RESULTS: We identified 262 DRLncs and eleven prognosis-related DRLncs through filtering. We then constructed two distinct expression patterns of prognosis-related DRLncs and developed a classifier. We obtained 393 differentially expressed genes (DEGs) between different subtypes, which were significantly enriched in biological processes related to the extracellular matrix, integrin binding, focal adhesion, and Wnt signaling pathways. Through immune infiltration analysis, the activated CD4 memory T cells, resting natural killer (NK) cells, M1 macrophages, and resting dendritic cells (DC) were observed to exhibit different abundance in distinct subtypes. In the drug sensitivity analysis, tamoxifen showed a promising effect for drug-resistant OS. Furthermore, we identified five hub DRLncs and constructed a risk model. The RT-qPCR confirmed the aberrant expression of five hub DRLncs in OS. CONCLUSIONS: The present study identified DRLncs in OS, and conducted a comprehensive investigation of DRLncs-related expression patterns, survival status, immune landscape and drug sensitivity to reveal the difference in prognostic, pharmacological and immunological phenotype characteristics between distinct subtypes. Additionally, we developed a risk model to predict the prognosis, and constructed a genotyping classifier to predict the above phenotype characteristics in OS.

TRBC1 in flow cytometry: Assay development, validation, and reporting considerations.

The assessment of T-cell clonality by flow cytometry has long been suboptimal, relying on aberrant marker expression and/or intensity. The introduction of TRBC1 shows much promise for improving the diagnosis of T-cell neoplasms in the clinical flow laboratory. Most laboratories considering this marker already have existing panels designed for T-cell workups and will be determining how best to incorporate TRBC1. We present this comprehensive summary of TRBC1 and supplemental case examples to familiarize the flow cytometry community with its potential for routine application, provide examples of how to incorporate it into T-cell panels, and signal caution in interpreting the results in certain diagnostic scenarios where appropriate.

Staining pattern of specific and cross-reacting Melan-A antibodies: A comparative study on 15,840 samples from 133 human tumor types.

The Melan-A (melanocyte antigen) protein, also termed 'melanoma antigen recognized by T cells 1' (MART-1) is a protein with unknown function whose expression is specific for the melanocyte lineage. Antibodies against Melan-A are thus used for identifying melanocytic tumors, but some Melan-A antibodies show an additional - diagnostically useful - cross-reactivity against an unspecified protein involved in corticosteroid hormone synthesis. To comprehensively compare the staining patterns of a specific and a cross-reactive Melan-A antibody in normal and neoplastic tissues, tissue microarrays containing 15,840 samples from 133 different tumor types and subtypes as well as 608 samples of 76 different normal tissue types were analyzed by immunohistochemistry. For the Melan-A-specific antibody 'Melan-A specific' (MSVA-900M), Melan-A positivity was seen in 96.0% of 25 benign nevi, 93.0% of 40 primary and 86.7% of 75 metastatic melanomas, 82.4% of 85 renal angiomyolipomas as well as 96.4% of 84 neurofibromas, 2.2% of 46 granular cell tumors, 1.0% of 104 schwannomas, and 1.1% of 87 leiomyosarcomas. The cross-reactive antibody 'Melan-A+' (MSVA-901M+) stained 98.1% of the tumors stained by 'Melan-A specific'. In addition, high positivity rates were seen in sex-cord-stroma tumors of the ovary (35.3%-100%) and the testis (86.7%) as well as for adrenocortical neoplasms (76.3%-83.0%). Only nine further tumor groups showed Melan-A+ staining, including five different categories of urothelial carcinomas. Our data provide a comprehensive overview on the staining patterns of specific and cross-reactive Melan-A antibodies. The data demonstrate that both antibodies are highly useful for their specific purpose. It is important for pathologists to distinguish these two Melan-A antibody subtypes for their daily work.

Unraveling pancreatic ductal adenocarcinoma immune prognostic signature through a naive B cell gene set.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC), a leading cause of cancer mortality, has a complex pathogenesis involving various immune cells, including B cells and their subpopulations. Despite emerging research on the role of these cells within the tumor microenvironment (TME), the detailed molecular interactions with tumor-infiltrating immune cells (TIICs) are not fully understood. METHODS: We applied CIBERSORT to quantify TIICs and naive B cells, which are prognostic for PDAC. Marker genes from scRNA-seq and modular genes from weighted gene co-expression network analysis (WGCNA) were integrated to identify naive B cell-related genes. A prognostic signature was constructed utilizing ten machine-learning algorithms, with validation in external cohorts. We further assessed the immune cell diversity, ESTIMATE scores, and immune checkpoint genes (ICGs) between patient groups stratified by risk to clarify the immune landscape in PDAC. RESULTS: Our analysis identified 994 naive B cell-related genes across single-cell and bulk transcriptomes, with 247 linked to overall survival. We developed a 12-gene prognostic signature using Lasso and plsRcox algorithms, which was confirmed by 10-fold cross-validation and showed robust predictive power in training and real-world cohorts. Notably, we observed substantial differences in immune infiltration between patients with high and low risk. CONCLUSION: Our study presents a robust prognostic signature that effectively maps the complex immune interactions in PDAC, emphasizing the critical function of naive B cells and suggesting new avenues for immunotherapeutic interventions. This signature has potential clinical applications in personalizing PDAC treatment, enhancing the understanding of immune dynamics, and guiding immunotherapy strategies.

Autoantibodies against Endophilin A2 as a novel biomarker are beneficial to early diagnosis of breast cancer.

BACKGROUND: Due to the lack of early symptoms, breast cancer is frequently overlooked, leading to distant metastases and multi-organ lesions that directly threaten patients' lives. We have identified a novel tumor marker, antibodies to endophilin A2 (EA2), to improve early diagnosis of breast cancer. METHODS: Antibody levels of EA2 were analyzed in sera of patients with cancers of different origins and stages by indirect enzyme-linked immunosorbent assay (ELISA). Diagnostic accuracy and reference range were determined by the area under the receiver operating curve and distribution curve. The levels of EA2 antigen in sera were determined by sandwich ELISA. RESULTS: The levels of antibodies against EA2 were higher in sera of patients with breast cancer (P < 0.0001), liver cancer (P = 0.0005), gastric cancer (P = 0.0026), and colon cancer (P = 0.0349) than those in healthy controls, but not in patients with rectal cancer (P = 0.1151), leukemia (P = 0.7508), or lung cancer (P = 0.2247). The highest diagnostic value was for breast cancer, particularly in early cases (AUC = 0.8014) and those with distant metastases (AUC = 0.7885). The titers of EA2 antibodies in sera were correlated with levels of EA2 antigen in breast cancer patients. CONCLUSION: Antibodies to EA2 are novel blood biomarkers for early diagnosis of breast cancer that warrants further study in larger-scale cohort studies.

Serum anti-CFL1, anti-EZR, and anti-CYPA autoantibody as diagnostic markers in ovarian cancer.

The purpose of this study was to identify novel autoantibodies against tumor-associated antigens (TAAs) and explore a diagnostic panel for Ovarian cancer (OC). Enzyme-linked immunosorbent assay was used to detect the expression of five anti-TAA autoantibodies in the discovery (70 OC and 70 normal controls) and validation cohorts (128 OC and 128 normal controls). Machine learning methods were used to construct a diagnostic panel. Serum samples from 81 patients with benign ovarian disease were used to identify the specificity of anti-TAA autoantibodies for OC. In both the discovery and validation cohorts, the expression of anti-CFL1, anti-EZR, anti-CYPA, and anti-PFN1 was higher in patients with OC than that in normal controls. The area under the receiver operating characteristic curve, sensitivity, and specificity of the panel containing anti-CFL1, anti-EZR, and anti-CYPA were 0.762, 55.56%, and 81.31%. The panel identified 53.06%, 53.33%, and 51.11% of CA125 negative, HE4 negative and the Risk of Ovarian Malignancy Algorithm negative OC patients, respectively. The combination of the three anti-TAA autoantibodies can serve as a favorable diagnostic tool for OC and has the potential to be a complementary biomarker for CA125 and HE4 in the diagnosis of ovarian cancer.

Comprehensive analysis reveals that LTBR is a immune-related biomarker for glioma.

Glioma is a common malignant brain tumor with great heterogeneity and huge difference in clinical outcomes. Although lymphotoxin (LT) beta receptor (LTBR) has been linked to immune system and response development for decades, the expression and function in glioma have not been investigated. To confirm the expression profile of LTBR, integrated RNA-seq data from glioma and normal brain tissues were analyzed. Functional enrichment analysis, TMEscore analysis, immune infiltration, the correlation of LTBR with immune checkpoints and ferroptosis, and scRNAseq data analysis in gliomas were in turn performed, which pointed out that LTBR was pertinent to immune functions of macrophages in gliomas. In addition, after being trained and validated in the tissue samples of the integrated dataset, an LTBR DNA methylation-based prediction model succeeded to distinguish gliomas from non-gliomas, as well as the grades of glioma. Moreover, by virtue of the candidate LTBR CpG sites, a prognostic risk-score model was finally constructed to guide the chemotherapy, radiotherapy, and immunotherapy for glioma patients. Taken together, LTBR is closely correlated with immune functions in gliomas, and LTBR DNA methylation could serve as a biomarker for diagnosis and prognosis of gliomas.

Construction of an immunosensor based on Cys/Au@TiO2 modification for the detection of liver cancer marker PIVKA-II.

An ultrasensitive immunosensor of Cys/Au@TiO2 based on disposable screen-printed electrodes (SPE) for PIVKA-II detection for hepatocellular carcinoma (HCC) diagnosis was developed by utilizing Cystine (Cys) and nanocomposite Au@TiO2. Firstly, HAuCl4 underwent a reduction reaction with NaBH4, then Au nanoparticles were coated onto TiO2 nanoparticles. Followed, Cys/Au@TiO2 was formed through self-assembly of cysteine to allow the monoclonal antibody of abnormal thrombospondin to bound to the amino group on the surface of the composite by covalent bonding. The mechanism is to determine the changes in the current of the sensor caused by the specific binding of the abnormal prothrombin monoclonal antibody adsorbed by the complex with its antigen. The Cys/Au@TiO2 immunosensor was fully characterized by various analytical approaches and it showed a wide linear testing range of 1-10000 pg mL-1 (R2 = 0.991) and the limit of detection down to 0.77 pg ml-1, with highly sensitivity and specificity. The results showed that the developed immunosensor platform can effectively detect trace amounts of PIVKA-II protein and has potent clinical application for HCC diagnosis.

Construction of S100 family members prognosis prediction model and analysis of immune microenvironment landscape at single-cell level in pancreatic adenocarcinoma: a tumor marker prognostic study.

Pancreatic adenocarcinoma characterized by a mere 10% 5-year survival rate, poses a formidable challenge due to its specific anatomical location, making tumor tissue acquisition difficult. This limitation underscores the critical need for novel biomarkers to stratify this patient population. Accordingly, this study aimed to construct a prognosis prediction model centered on S100 family members. Leveraging six S100 genes and their corresponding coefficients, an S100 score was calculated to predict survival outcomes. The present study provided comprehensive internal and external validation along with power evaluation results, substantiating the efficacy of the proposed model. Additionally, the study explored the S100-driven potential mechanisms underlying malignant progression. By comparing immune cell infiltration proportions in distinct patient groups with varying prognoses, the research identified differences driven by S100 expression. Furthermore, the analysis explored significant ligand-receptor pairs between malignant cells and immune cells influenced by S100 genes, uncovering crucial insights. Notably, the study identified a novel biomarker capable of predicting the sensitivity of neoadjuvant chemotherapy, offering promising avenues for further research and clinical application.

Exploratory pilot study to characterize the immune landscapes of malignant pleural effusions and their corresponding primary tumors from patients with breast carcinoma and lung adenocarcinoma.

INTRODUCTION: Malignant pleural effusion (MPE) is a frequent complication of advanced malignancies. In this pilot study, we characterized the immune landscapes of MPEs, compared them to their primary tumor (PT) samples from breast carcinoma (BC) and lung adenocarcinoma (LADC), and tested the utility of multiplexed image technology in cytological samples. MATERIALS AND METHODS: We evaluated the immune contexture of 6 BC and 5 LADC MPEs and their PTs using 3 multiplex immunofluorescence panels. We explored the associations between sample characteristics and pleural effusion-free survival. RESULTS: No MPE samples had positive programmed death-ligand 1 expression in malignant cells, although 3 of 11 PTs has positive programmed death-ligand 1 expression (more than 1% expression in malignant cells). Overall, in LADC samples, cluster of differentiation 3 (CD3)+ T cells and CD3+CD8+ cytotoxic T cells predominated (median percentages for MPEs versus PTs: 45.6% versus 40.7% and 4.7% versus 6.6%, respectively) compared with BC. CD68+ macrophages predominated in the BC samples (medians for MPEs 61.2% versus PTs for 57.1%) but not in the LADC samples. Generally in PTs, CD3+CD8+ forkhead box P3+ T cells and the median distances from the malignant cells to CD3+CD8+Ki67+ and CD3+ programmed cell death protein 1 + T cells correlated to earlier MPE after PT diagnosis. CONCLUSIONS: The immune cell phenotypes in the MPEs and PTs were similar within each cancer type but different between BC versus LADC. An MPE analysis can potentially be used as a substitute for a PT analysis, but an expanded study of this topic is essential.