Construction of metal interpretable scoring system and identification of tungsten as a novel risk factor in COPD.

Numerous studies have highlighted the correlation between metal intake and deteriorated pulmonary function, emphasizing its pivotal role in the progression of Chronic Obstructive Pulmonary Disease (COPD). However, the efficacy of traditional models is often compromised due to overfitting and high bias in datasets with low-level exposure, rendering them ineffective in delineating the contemporary risk trends associated with pulmonary diseases. To address these limitations, we embarked on developing advanced, interpretable models, crucial for elucidating the intricate mechanisms of metal toxicity and enriching the domain knowledge embedded in toxicity models. In this endeavor, we scrutinized extensive, long-term metal exposure datasets from NHANES to explore the interplay between metal and pulmonary functionality. Employing a variety of machine-learning approaches, we opted for the "Mixer of Experts" model for its proficiency in identifying a myriad of toxicological trends and sensitivities. We conceptualized and illustrated the TSAP (Toxicity Score at Population-level), a metal interpretable scoring system offering performance nearly equivalent to the amalgamation of standard interpretable methods addressing the "black box" conundrum. This streamlined, bifurcated procedural analysis proved instrumental in discerning established risk factors, thereby uncovering Tungsten as a novel contributor to COPD risk. SYNOPSIS: TSAP achieved satisfied performance with transparent interpretability, suggesting tungsten intake need further action for COPD prevention.

Efficient In/SSZ-39 catalysts for the selective catalytic reduction of NO with CH4.

The M/SSZ-39 catalysts (M = In, Co, Cu, Fe) with different metal species and metal loadings were synthesized using the wet impregnation method on a small-pore SSZ-39 molecular sieve. X-ray diffraction (XRD), transmission electron microscopy (TEM), nitrogen adsorption-dehydrogenation and hydrogen temperature program reduction (H2-TPR) were employed to characterize the effects of various metal components and metal loadings on the performance of CH4 selective catalytic reduction of NO reaction (CH4-SCR). The characterization results showed that the In/SSZ-39 catalyst exhibited significantly higher catalytic activity compared to the Cu-, Co-, and Fe/SSZ-39 catalysts, suggesting that indium (In) is a more suitable active ingredient for the CH4-SCR reaction. The xIn/SSZ-39 (x = 1, 2, 3, x represents the In loadings of 1.0 wt%, 2.0 wt% and 3.0 wt%) catalysts, with different In loadings, all present excellent CH4-SCR performance. By varying the In loadings, the type of In species present in the catalyst can be regulated, thus enhancing DeNOx activity and CH4 selectivity in the CH4-SCR reaction. At a low temperature of 400 °C and a low CH4/NO feed ratio (CH4/NO = 1), the 3In/SSZ-39 catalyst, featuring highly active InOx clusters, achieves the best low-temperature CH4-SCR performance, with a high NO conversion rate of up to 90% and a CH4 selectivity of up to 74.2%.

Utilizing radiomics for differential diagnosis of inverted papilloma and chronic rhinosinusitis with polyps based on unenhanced CT scans.

To evaluate whether radiomics models based on unenhanced paranasal sinuses CT images could be a useful tool for differentiating inverted papilloma (IP) from chronic rhinosinusitis with polyps (CRSwNP). This retrospective study recruited 240 patients with CRSwNP and 106 patients with IP from three centers. 253 patients from Qilu Hospital were randomly divided into the training set (n = 151) and the internal validation set (n = 102) with a ratio of 6:4. 93 patients from the other two centers were used as the external validation set. The patients with the unilateral disease (n = 115) from Qilu Hospital were selected to further develop a subgroup analysis. Lesion segmentation was manually delineated in CT images. Least absolute shrinkage and selection operator algorithm was performed for feature reduction and selection. Decision tree, support vector machine, random forest, and adaptive boosting regressor were employed to establish the differential diagnosis models. 43 radiomic features were selected for modeling. Among the models, RF achieved the best results, with an AUC of 0.998, 0.943, and 0.934 in the training set, the internal validation set, and the external validation set, respectively. In the subgroup analysis, RF achieved an AUC of 0.999 in the training set and 0.963 in the internal validation set. The proposed radiomics models offered a non-invasion and accurate differential approach between IP and CRSwNP and has some significance in guiding clinicians determining the best treatment plans, as well as predicting the prognosis.

APOL6 predicts immunotherapy efficacy of bladder cancer by ferroptosis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are rapidly evolving in the management of bladder cancer (BLCA). Nevertheless, effective biomarkers for predicting immunotherapeutic outcomes in BLCA are still insufficient. Ferroptosis, a form of immunogenic cell death, has been found to enhance patient sensitivity to ICIs. However, the underlying mechanisms of ferroptosis in promoting immunotherapy efficacy in BLCA remain obscure. METHODS: Our analysis of The Cancer Genome Atlas (TCGA) mRNA data using single sample Gene Set Enrichment Analysis (ssGSEA) revealed two immunologically distinct subtypes. Based on these subtypes and various other public cohorts, we identified Apolipoprotein L6 (APOL6) as a biomarker predicting the efficacy of ICIs and explored its immunological correlation and predictive value for treatment. Furthermore, the role of APOL6 in promoting ferroptosis and its mechanism in regulating this process were experimentally validated. RESULTS: The results indicate that APOL6 has significant immunological relevance and is indicative of immunologically hot tumors in BLCA and many other cancers. APOL6, interacting with acyl-coenzyme A synthetase long-chain family member 4 (ACSL4), mediates immunotherapy efficacy by ferroptosis. Additionally, APOL6 is regulated by signal transducer and activator of transcription 1 (STAT1). CONCLUSIONS: To conclude, our findings indicate APOL6 has potential as a predictive biomarker for immunotherapy treatment success estimation and reveal the STAT1/APOL6/GPX4 axis as a critical regulatory mechanism in BLCA.

Long-term in situ straw returning increased soil aggregation and aggregate associated organic carbon fractions in a paddy soil.

Despite the well-documentation of the effects of straw returning on soil structural stability and fertility, its long-term in situ impacts on profile aggregate size composition and organic carbon (OC) fractions remain poorly investigated. To address this research gap, the present nine-year field trial explored the co-effects of straw returning and chemical fertilization on soil total OC (TOC), dissolved OC (DOC), resistant OC (ROC), easily oxidative OC (EOC), as well as soil aggregate size composition of different soil depths (0-15, 15-30, and 30-50 cm) in a paddy field, East China. To do so, four different treatments were set up, including no straw returning plus no fertilization (CK), conventional fertilization (F), straw returning plus conventional fertilization (SF), and straw returning plus 80 % conventional fertilization (SDF). Our findings revealed that the >2 mm aggregates were dominant in all treatments, particularly in SF and SDF 0-30 cm soil layers ranging from 62 to 70 % (P < 0.05). The highest TOC contents happened in SF topsoil 0.25-2 mm aggregates (0-30 cm; 21.4 g/kg), 44.4 and 21.1 % higher than the CK and F treatments, respectively (P < 0.05). Regardless of soil depth, the highest EOC contents occurred in SDF 0.25-2 mm aggregates varying from 2.36 ± 0.1 to 7.7 ± 0.57 g/kg (P < 0.05). Further, the highest ROC and DOC contents took place in SF 0.25-2 mm and SF > 2 mm aggregates, respectively, differing from 3.86 to 15.8 g/kg and 250-413 mg/kg, respectively (P < 0.05). It is also worth noting that SF had the highest crop productivity with the seasonal yields of 3.51 and 13.5 t ha-1 for rapeseed and rice, respectively (P < 0.05). Altogether, our findings suggested that long-term straw returning coupled with conventional (SF) or 80 % conventional (SDF) fertilization are the most efficient schemes for the formation/stability of soil aggregates, as well as for the accumulation of different soil OC fractions and crop productivity in the Chaohu Lake agricultural soils of East China.

Electron transferring with oxygen defects on Ni-promoted Pd/Al2O3 catalysts for low-temperature lean methane combustion.

Methane (CH4) is the second most consequential greenhouse gas after CO2, with a substantial global warming potential. The CH4 catalytic combustion offers an efficient method for the elimination of CH4. However, improving the catalytic performance of Pd-based materials for low-temperature CH4 combustion remains a big challenge. In this study, we synthesized an enhanced Pd/5NiAlOx catalyst that demonstrated superior catalytic activity and improved water resistance compared to the Pd/Al2O3 catalyst. Specifically, the T90 was decreased by over 100 °C under both dry and wet conditions. Introducing Ni resulted in an enormously enhanced number of oxygen defects on the obtained 5NiAlOx support. This defect-rich support facilitates the anchoring of PdO through increased electron transfer, thereby inhibiting the production of high-valence Pd(2+δ)+ and stimulating the generation of unsaturated Pd sites. Pd0 can effectively activate surface oxygen and PdO plays a significant role in activating CH4, resulting in high activity for Pd/5NiAlOx. On the other hand, the increased water resistance of Pd/5NiAlOx was mainly due to the generation of *OOH species and the lower accumulation of surface -OH species during the reaction process.

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.

ARID1A orchestrates SWI/SNF-mediated sequential binding of transcription factors with ARID1A loss driving pre-memory B cell fate and lymphomagenesis.

ARID1A, a subunit of the canonical BAF nucleosome remodeling complex, is commonly mutated in lymphomas. We show that ARID1A orchestrates B cell fate during the germinal center (GC) response, facilitating cooperative and sequential binding of PU.1 and NF-kB at crucial genes for cytokine and CD40 signaling. The absence of ARID1A tilts GC cell fate toward immature IgM+CD80-PD-L2- memory B cells, known for their potential to re-enter new GCs. When combined with BCL2 oncogene, ARID1A haploinsufficiency hastens the progression of aggressive follicular lymphomas (FLs) in mice. Patients with FL with ARID1A-inactivating mutations preferentially display an immature memory B cell-like state with increased transformation risk to aggressive disease. These observations offer mechanistic understanding into the emergence of both indolent and aggressive ARID1A-mutant lymphomas through the formation of immature memory-like clonal precursors. Lastly, we demonstrate that ARID1A mutation induces synthetic lethality to SMARCA2/4 inhibition, paving the way for potential precision therapy for high-risk patients.

Effect of sludge humic acid-derived nano-biochars on anaerobic degradation of sulfamethoxazole by Shewanella oneidensis MR-1.

Some nano-biochars (nano-BCs) as electron mediators could enter into cells to directly promote intracellular electron transfer and cell activities. However, little information was available on the effect of nano-BCs on SMX degradation. In this study, nano-BCs were prepared using sludge-derived humic acid (SHA) and their effects on SMX degradation by Shewanella oneidensis MR-1 were investigated. Results showed that nano-BCs (Carbon dots, CDs, <10 nm) synthesized using SHA performed a better accelerating effect than that of the nano-BCs with a larger size (10-100 nm), which could be attributed to the better electron transfer abilities of CDs. The degradation rate of 10 mg/L SMX in the presence of 100 mg/L CDs was significantly increased by 84.6% compared to that without CDs. Further analysis showed that CDs could not only be combined with extracellular Fe(III) to accelerate its reduction, but also participate in the reduction of 4-aminobenzenesulphonic acid as an intermediate metabolite of SMX via coupling with extracellular Fe(III) reduction. Meanwhile, CDs could enter cells to directly participate in intracellular electron transfer, resulting in 32.2% and 25.2% increases of electron transfer system activity and ATP level, respectively. Moreover, the activities of SMX-degrading enzymes located in periplasm and cytoplasm were increased by around 2.2-fold in the presence of CDs. These results provide an insight into the accelerating effect of nano-BCs with the size of <10 nm on SMX degradation and an approach for SHA utilization.

MAGIC-DR: An interpretable machine-learning guided approach for acute myeloid leukemia measurable residual disease analysis.

Multiparameter flow cytometry is widely used for acute myeloid leukemia minimal residual disease testing (AML MRD) but is time consuming and demands substantial expertise. Machine learning offers potential advancements in accuracy and efficiency, but has yet to be widely adopted for this application. To explore this, we trained single cell XGBoost classifiers from 98 diagnostic AML cell populations and 30 MRD negative samples. Performance was assessed by cross-validation. Predictions were integrated with UMAP as a heatmap parameter for an augmented/interactive AML MRD analysis framework, which was benchmarked against traditional MRD analysis for 25 test cases. The results showed that XGBoost achieved a median AUC of 0.97, effectively distinguishing diverse AML cell populations from normal cells. When integrated with UMAP, the classifiers highlighted MRD populations against the background of normal events. Our pipeline, MAGIC-DR, incorporated classifier predictions and UMAP into flow cytometry standard (FCS) files. This enabled a human-in-the-loop machine learning guided MRD workflow. Validation against conventional analysis for 25 MRD samples showed 100% concordance in myeloid blast detection, with MAGIC-DR also identifying several immature monocytic populations not readily found by conventional analysis. In conclusion, Integrating a supervised classifier with unsupervised dimension reduction offers a robust method for AML MRD analysis that can be seamlessly integrated into conventional workflows. Our approach can support and augment human analysis by highlighting abnormal populations that can be gated on for quantification and further assessment. This has the potential to speed up MRD analysis, and potentially improve detection sensitivity for certain AML immunophenotypes.

Keratin gene signature expression drives epithelial-mesenchymal transition through enhanced TGF-ß signaling pathway activation and correlates with adverse prognosis in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) stands as the foremost histological subtype of non-small-cell lung cancer, accounting for approximately 40% of all lung cancer diagnoses. However, there remains a critical unmet need to enhance the prediction of clinical outcomes and therapy responses in LUAD patients. Keratins (KRTs), serving as the structural components of the intermediate filament cytoskeleton in epithelial cells, play a crucial role in the advancement of tumor progression. This study investigated the prognostic significance of the KRT family gene and developed a KRT gene signature (KGS) for prognostic assessment and treatment guidance in LUAD. Methods: Transcriptome profiles and associated clinical details of LUAD patients were meticulously gathered from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The KGS score was developed based on the expression of five prognostic KRT genes (KRT7, KRT8, KRT17, KRT18, and KRT80), and the upper quartile of the KGS score was chosen as the cutoff. The Kaplan-Meier method was evaluated to compare survival outcomes between KGS-high and KGS-low groups. The underlying mechanism was further investigated by GSEA, GSVA, and other bioinformatic algorithms. Results: High expression of the KGS signature exhibited a robust association with poorer overall survival (OS) in the TCGA-LUAD dataset (HR: 1.81; 95% CI: 1.35-2.42, P = 0.00011). The association was further corroborated in three external GEO cohorts, including GSE31210 (HR: 3.31; 95% CI: 1.7-6.47, P = 0.00017), GSE72094 (HR: 1.95; 95% CI: 1.34-2.85, P = 0.00057) and GSE26939 (HR: 3.19; 95% CI: 1.74-5.84, P < 0.0001). Interestingly, KGS-high tumors revealed enrichments in TGF-ß and WNT-ß catenin signaling pathways, exhibited heightened activation of the epithelial-mesenchymal transition (EMT) pathway and proved intensified tumor stemness compared to their KGS-low counterparts. Additionally, KGS-high tumor cells exhibited increased sensitivity to several targeted agents, including gefitinib, erlotinib, lapatinib, and trametinib, in comparison to KGS-low cells. Conclusion: This study developed a KGS score that independently predicts the prognosis in LUAD. High expression of KGS score, accompanied by upregulation of TGF-ß and WNT-ß catenin signaling pathways, confers more aggressive EMT and tumor progression.

TRPML1 contributes to antimony-induced nephrotoxicity by initiating ferroptosis via chaperone-mediated autophagy.

Evidence suggests that ferroptosis participates in kidney injury. However, the role of ferroptosis in antimony (Sb) induced nephrotoxicity and the mechanism are unknown. Here, we demonstrated that Sb induced injury in renal tubular epithelial cells (RTECs) and ferroptosis. Inhibition of ferroptosis reduced RTECs injury. Besides, elimination of reactive oxygen species (ROS) alleviated ferroptosis and RTECs injury. Moreover, exposure to Sb not only increased the co-localization of glutathione peroxidase 4 (GPX4) and LAMP1, but also decreased the levels of MEF2D and LRRK2, while increased the levels of HSC70, HSP90, and LAMP2a. These findings suggest that Sb activates chaperone-mediated autophagy (CMA), enhances lysosomal transport and subsequent degradation of GPX4, ultimately leads to ferroptosis. Additionally, up-regulation of lysosomal cationic channel, TRPML1, mitigated RTECs injury and ferroptosis. Mechanistically, up-regulation of TRPML1 mitigated the changes in CMA-associated proteins induced by Sb, diminished the binding of HSC70, HSP90, and TRPML1 with LAMP2a. Furthermore, NAC restored the decreased TRPML1 level caused by Sb. In summary, deficiency of TRPML1, secondary to increased ROS induced by Sb, facilitates the CMA-dependent degradation of GPX4, thereby leading to ferroptosis and RTECs injury. These findings provide insights into the mechanism underlying Sb-induced nephrotoxicity and propose TRPML1 as a promising therapeutic target.

SpatialSort: a Bayesian model for clustering and cell population annotation of spatial proteomics data.

MOTIVATION: Recent advances in spatial proteomics technologies have enabled the profiling of dozens of proteins in thousands of single cells in situ. This has created the opportunity to move beyond quantifying the composition of cell types in tissue, and instead probe the spatial relationships between cells. However, most current methods for clustering data from these assays only consider the expression values of cells and ignore the spatial context. Furthermore, existing approaches do not account for prior information about the expected cell populations in a sample. RESULTS: To address these shortcomings, we developed SpatialSort, a spatially aware Bayesian clustering approach that allows for the incorporation of prior biological knowledge. Our method is able to account for the affinities of cells of different types to neighbour in space, and by incorporating prior information about expected cell populations, it is able to simultaneously improve clustering accuracy and perform automated annotation of clusters. Using synthetic and real data, we show that by using spatial and prior information SpatialSort improves clustering accuracy. We also demonstrate how SpatialSort can perform label transfer between spatial and nonspatial modalities through the analysis of a real world diffuse large B-cell lymphoma dataset. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at: https://github.com/Roth-Lab/SpatialSort.

Deep learning radiomics-based preoperative prediction of recurrence in chronic rhinosinusitis.

Chronic rhinosinusitis (CRS) is characterized by poor prognosis and propensity for recurrence even after surgery. Identification of those CRS patients with high risk of relapse preoperatively will contribute to personalized treatment recommendations. In this paper, we proposed a multi-task deep learning network for sinus segmentation and CRS recurrence prediction simultaneously to develop and validate a deep learning radiomics-based nomogram for preoperatively predicting recurrence in CRS patients who needed surgical treatment. 265 paranasal sinuses computed tomography (CT) images of CRS from two independent medical centers were analyzed to build and test models. The sinus segmentation model achieved good segmentation results. Furthermore, the nomogram combining a deep learning signature and clinical factors also showed excellent recurrence prediction ability for CRS. Our study not only facilitates a technique for sinus segmentation but also provides a noninvasive method for preoperatively predicting recurrence in patients with CRS.

Integrated single-cell and transcriptome sequencing analyses develops a metastasis-based risk score system for prognosis and immunotherapy response in uveal melanoma.

Background: Uveal melanoma (UM) is the most frequent ocular neoplasm with a strong metastatic ability. The prognostic value of metastasis-associated genes (MAGs) of UM remains unclear. It is urgent to develop a prognostic score system according to the MAGs of UM. Methods: Unsupervised clustering was used to identify MAGs-based molecular subtypes. Cox methods were utilized to generate a prognostic score system. The prognostic ability of the score system was detected by plotting ROC and survival curves. The immune activity and underlying function were depicted by CIBERSORT GSEA algorithms. Results: Gene cluster analysis determined two MAGs-based subclusters in UM, which were remarkably different in clinical outcomes. A risk score system containing six MAGs (COL11A1, AREG, TIMP3, ADAM12, PRRX1 and GAS1) was set up. We employed ssGSEA to compare immune activity and immunocyte infiltration between the two risk groups. Notch, JAK/STAT and mTOR pathways were greatly enriched in the high-risk group. Furthermore, we observed that knockdown of AREG could inhibit UM proliferation and metastasis by in vitro assays. Conclusion: The MAGs-based subtype and score system in UM can enhance prognosis assessment, and the core system provides valuable reference for clinical decision-making.

A prognostic model and immune regulation analysis of uterine corpus endometrial carcinoma based on cellular senescence.

Background: This study aimed to explore the clinical significance of cellular senescence in uterine corpus endometrial carcinoma (UCEC). Methods: Cluster analysis was performed on GEO data and TCGA data based on cellular senescence related genes, and then performed subtype analysis on differentially expressed genes between subtypes. The prognostic model was constructed using Lasso regression. Survival analysis, microenvironment analysis, immune analysis, mutation analysis, and drug susceptibility analysis were performed to evaluate the practical relevance. Ultimately, a clinical nomogram was constructed and cellular senescence-related genes expression was investigated by qRT-PCR. Results: We ultimately identified two subtypes. The prognostic model divides patients into high-risk and low-risk groups. There were notable discrepancies in prognosis, tumor microenvironment, immunity, and mutation between the two subtypes and groups. There was a notable connection between drug-sensitive and risk scores. The nomogram has good calibration with AUC values between 0.75-0.8. In addition, cellular senescence-related genes expression was investigated qRT-PCR. Conclusion: Our model and nomogram may effectively forecast patient prognosis and serve as a reference for patient management.

Single-cell profiling reveals a memory B cell-like subtype of follicular lymphoma with increased transformation risk.

Follicular lymphoma (FL) is an indolent cancer of mature B-cells but with ongoing risk of transformation to more aggressive histology over time. Recurrent mutations associated with transformation have been identified; however, prognostic features that can be discerned at diagnosis could be clinically useful. We present here comprehensive profiling of both tumor and immune compartments in 155 diagnostic FL biopsies at single-cell resolution by mass cytometry. This revealed a diversity of phenotypes but included two recurrent patterns, one which closely resembles germinal center B-cells (GCB) and another which appears more related to memory B-cells (MB). GCB-type tumors are enriched for EZH2, TNFRSF14, and MEF2B mutations, while MB-type tumors contain increased follicular helper T-cells. MB-type and intratumoral phenotypic diversity are independently associated with increased risk of transformation, supporting biological relevance of these features. Notably, a reduced 26-marker panel retains sufficient information to allow phenotypic profiling of future cohorts by conventional flow cytometry.

HPV-Related Prognostic Signature Predicts Survival in Head and Neck Squamous Cell Carcinoma.

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers, worldwide. Considering the role of human papilloma virus (HPV) in tumor development and sensitivity to treatment of HNSCC, we aimed to explore the prognostic classification ability of HPV-related signatures in head and neck cancer. Methods: HPV-related signatures were screened out based on Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. HPV-related signatures with prognostic value were identified through univariate Cox regression analysis and a risk signature was established by least absolute shrinkage and selection operator (LASSO). Further, we developed a nomogram by integrating independent prognostic factors. Results: A total of 55 HPV-associated signatures were differentially expressed and ten of them were associated with prognosis of HNSCC patients. The prognostic signature based on CDKN2A, CELSR3, DMRTA2, SERPINE1, TJP3, FADD, and IGF2BP2 expression was constructed. Univariate and multivariate regression analyses demonstrated that the novel prognostic signature was an independent prognostic factor of HNSCC. The nomogram integrating the prognostic signature and other independent prognostic factors was developed. Conclusion: In summary, the prognostic signature of the HPV-related signatures might serve as an important prognostic biomarker for patients with HNSCC.

Comprehensive analysis of PTPN gene family revealing PTPN7 as a novel biomarker for immuno-hot tumors in breast cancer.

Background: The non-receptor protein tyrosine phosphatase (PTPN) gene family has been considered to be involved in the oncogenesis and development of multiple cancers. However, its prognostic utility and immunological relevance in breast cancer (BrCa) have not been clarified. Methods: A transcriptional level interpretation of the expressions and prognostic values was analyzed using the data from The Cancer Genome Atlas (TCGA) cohort. In addition, GO and DAVID pinpoint the functional enrichment of PTPNs. Moreover, the immune correlations of PTPN7 in BrCa and pan-cancer were further investigated based on the TCGA cohort and were testified using the in-house and the Gene Expression Omnibus (GEO) cohorts. Results: For systematic analysis of the PTPN family, we found that the expression levels of PTPN1, PTPN6, PTPN7, PTPN18, PTPN20, and PTPN22 was promoted in tumor tissues while comparing with paraneoplastic tissues during our study. We further investigated their functions and protein-protein interactions (PPI), and these results strongly suggested that PTPN family was associated with protein dephosphorylation. Next, we performed an immunological relevance analysis and found that PTPN7 was correlated with immune infiltration, suggesting a stronger association of PTPN7 with immuno-hot tumors in BrCa. In addition, results from the in-house cohort confirmed the positive correlation between PTPN7 and PD-L1. The pan-cancer analysis revealed that PTPN7 was related to PD-L1 and CTLA-4 expression in almost all cancer types. Finally, the predictive value of PTPN7 for immunotherapy was significant in two independent GEO cohorts. Conclusion: In conclusion, this is the first extensive research on the correlation between PTPN family expression and immune characterization in BrCa. As results, PTPN7 expression is associated with immuno-hot tumors and could be a promising predictive biomarker for immunotherapy in not only BrCa but multiple cancers.