Features Of Different Subtypes Research Articles

Prediction of the survival status and tumor microenvironment in colorectal cancer through genotyping analysis based on toll-like receptors.

Colorectal cancer (CRC) ranks third in both the incidence and mortality rates among male and female cancers, and it is the leading digestive system cancer. Due to the inter- and intratumor heterogeneity of cancer, the TNM system is insufficient for predicting prognosis, necessitating the use of molecular biomarkers for prognostic prediction. Toll-like receptors (TLRs) have been associated with CRC survival rates. This study focused on the investigation of the role and potential value of TLRs in CRC genotyping to aid in immunotherapy for CRC patients. Differential gene expression analysis was performed on CRC transcriptomic data from The Cancer Genome Atlas database. TLRs were referred from the literature, and their intersection with differentially expressed genes (DEGs) in CRC yielded TLR-DEGs. The expression patterns of TLR-DEGs were predicted using the STRING website, and copy number variations of TLR-DEGs were analyzed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on TLR-DEGs. ConsensusClusterPlus R package was used for clustering CRC patients, and ESTIMATE and GSEAbase were employed to analyze immune characteristics of different subtypes. Immune phenotyping scores and tumor immune dysfunction and exclusion scores were evaluated. DEGs of different subtypes were analyzed, followed by GO and KEGG enrichment analyses, the protein-protein interaction (PPI) network analysis, and further selection of hub genes. The sensitivity of drugs was assessed using the identified hub genes. We identified 37 TLR-DEGs, and the PPI analysis revealed their coexpression, although they were distributed on different chromosomes. Enrichment analyses indicated that the 37 TLR-DEGs were linked to cancer cell immune response. Based on these TLR-DEGs, CRC patients were classified into three subtypes. Cluster2 exhibited lower survival rates and higher immune infiltration levels and predicted poorer response to immune checkpoint inhibitor therapy. The intersection of DEGs from cluster2 and cluster1 with DEGs from cluster2 and cluster3 yielded a set of 426 commonly shared DEGs. Enrichment analyses revealed that these shared DEGs might regulate immune cell viability. Eight common hub genes for different subtypes were further identified to predict drug-related correlations. The developed TLR genotyping was used to predict the survival status and tumor microenvironment of CRC, providing a foundation for understanding the molecular mechanisms of TLR signaling and deepening its clinical significance.

Abstract PS09-09: Multiomics profiling and molecular classification refine precision treatment strategies for HER2-positive breast cancer

Abstract Background: Anti-HER2 targeted therapy has achieved a series of breakthroughs. However, the current treatment strategy regarding HER2-positive breast cancer remains indiscriminate and lacks specificity, which limits the further improvement of overall treatment response and may lead to overtreatment and extra cost for some patients. Our study aims to reveal the molecular heterogeneity of HER2-positive breast cancer to guide a more precise treatment. Patients and methods: We selected HER2-positive breast cancer patients treated at Fudan University Shanghai Cancer Center between 2013 and 2014 and conducted genomic, transcriptomic, proteomic and metabolomic profiling. We then applied a non-negative matrix factorization algorithm on transcriptomic data to obtain an unsupervised classification. And we further studied the correlation between subtypes and corresponding treatment strategies in multiple cohorts of adjuvant and neoadjuvant therapy. For clinical accessibility, we developed convolutional neural network models through deep learning algorithm based on digital pathology to identify different subtypes. Additionally, we explored novel treatment strategies using the patient-derived organoids (PDOs) models. Results: We established a novel multiomics cohort of 180 HER2- breast cancer patients and classified them into four clinically significant molecular subtypes: (1) A classical HER2-enriched (HER2-CLA, N=51) subtype characterized by strong ERBB2 signaling and remarkable sensitivity to anti-HER2-targeted therapy (pathologic complete response with dual-targeted therapy: 93%). (2) an immunomodulatory (HER2-IM, N=36) subtype characterized by an immune-activated microenvironment and excellent prognosis with current treatment (no relapse in 97% of patients with a median follow-up of 86 months). Tumors of this subtype were therefore candidates for de-escalatory treatment. (3) A luminal-like (HER2-LUM, N=55) subtype distinguished by activated estrogen receptor signaling and (4) a basal/mesenchymal-like (HER2-BM, N=38) subtype enriched in activated receptor tyrosine kinase pathways. HER2-LUM and HER2- BM showed limited benefit from anti-HER2 therapy, and thus, add-on therapies might be needed. The overall area under the curve (AUC) of the convolutional neural network model based on digital pathology for identifying different subtypes is 0.77. In the exploration of novel treatment strategies, we found in the PDO model that the HER2-LUM subtype is more sensitive to a treatment regimen combining standard (chemotherapy and targeted therapy) with subsequent endocrine therapy and CDK4/6 inhibitors compared to other subtypes. Additionally, the HER2-BM subtype demonstrated greater sensitivity to treatment with a combination of EGFR inhibitors, PDGFR inhibitors or VEGFR inhibitors. Conclusion: We uncovered a high degree of molecular heterogeneity in HER2-positive breast cancer and illustrated its impact on treatment response. More precise treatment can be given according to the characteristics of different subtypes, which may achieve good efficacy and simultaneously reduce overtreatment and extra cost. The comprehensive profiling of HER2-positive breast cancers could also serve as an important resource for further exploration. Key Words: HER2-positive breast cancer cohort; molecular classification; targeted therapy; precision treatment; de-escalatory treatment. Citation Format: Yu-Wei Li, Ding Ma, Xiang-Rong Wu, Lei-Jie Dai, Shen Zhao, Yu-Zheng Xu, Xi Jin, Xiao Yi, Ying Wang, Cai-Jin Lin, Yi-Fan Zhou, Tong Fu, Wen-Tao Yang, Ming Li, Hong Lv, Yi-Zhou Jiang, Zhi-Ming Shao. Multiomics profiling and molecular classification refine precision treatment strategies for HER2-positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS09-09.

New Classification of Rheumatoid Arthritis Based on Immune Cells and Clinical Characteristics.

Rheumatoid arthritis (RA) is a chronic systemic immune disease characterized by joint synovitis, but there are differences in clinical manifestations and serum test results among different patients. This is a bioinformatics study. We first obtained the gene expression profile of RA and normal synovium from the database, and screened the differentially expressed immune related genes for enrichment analysis. Subsequently, we classified RA into three subtypes by unsupervised clustering of serum gene expression profiles based on immune enrichment scores. Then, the enrichment and clinical characteristics of different subtypes were analyzed. Finally, according to the infiltration of different subtypes of immune cells, diagnostic markers were screened and verified by qRT-PCR. C1 subtype is related to the increase of neutrophils, C-reactive protein and erythrocyte sedimentation rate, and joint pain is more significant in patients. C2 subtype is related to the expression of CD8+T cells and Tregs, and patients have mild joint pain symptoms. The RF value of C3 subtype is higher, and the expression of various immune cells is increased. CD4 T cells, NK cells activated, macrophages M1 and neutrophils are immune cells significantly infiltrated in synovium and serum of RA patients. IFNGR1, TRAC, IFITM1 can be used as diagnostic markers of different subtypes. In this study, RA patients were divided into different immune molecular subtypes based on gene expression profile, and immune diagnostic markers were screened, which provided a new idea for the diagnosis and treatment of RA.

Dual path sub-type diagnostic model based on the anatomic morphological feature

Intracranial hematoma can compress the brain tissue, resulting in increased cranial pressure and a high fatality rate. Therefore, providing a rapid and accurate clinical diagnosis and treatment plan is a crucial step. Intracranial hematoma can be classified into clinical sub-types such as cerebral lobes, ventricular, basal ganglia, pons, and cerebellum hemorrhage based on the location of the hemorrhage. To accurately identify different sub-types and provide targeted treatment, this paper proposes a dual path sub-type diagnosis model based on the anatomic morphological feature. It extracts anatomic morphological features of hematoma based on clinical prior knowledge to achieve prior feature enhancement. Then, a dual-path input model is constructed, and the dual-path module is used to analyze and integrate various features of different sub-types of intracranial hematoma. The model is validated on the clinical data set of the Second Affiliated Hospital of Dalian Medical University, and the corresponding accuracy can reach 0.993. This model can automatically recognize the sub-type of the clinical intracranial hematoma cases and improve diagnostic efficiency. Besides, it further aid in image analysis and enable specific treatment planning.

Development and Validation of Deep Learning Model for Diagnosis and Subtypes Differentiation of Myeloproliferative Neoplasms Using Clinical Data and Digital Pathology

Bone marrow histology was the indispensable tool for the differential diagnosis of classic myeloproliferative neoplasms (MPNs) and subtypes. However, the subjectivity of morphological assessment and markedly overlapping pathological features of different subtypes made accurate diagnosis challenging and controversial. In this study, we developed Clinical, deep learning (DL) and Fusion diagnosis models based on clinical parameters, whole slide images (WSI) based deep learning algorithm using hematoxylin-eosin (HE) staining bone marrow specimen and combination of both for the diagnosis and differentiation of MPNs (Figure 1). 1,051 MPN patients from seven medical centres were enrolled in this study and divided into training, internal testing, internal validation and two external validation cohorts (called combined validation cohort totally). In combined validation cohort, Fusion model performed best in distinguishing MPNs with non-MPN controls with the AUC 0.931 (95%CI: 0.891-0.971). For PV identification, Clinical model achieved the highest AUC with 0.975 (95%CI: 0.960-0.991). Fusion model made best performance in the identification of ET and prePMF, with the AUC 0.887 (95%CI: 0.850-0.925) for ET and 0.899 (95%CI: 0.851-0.947) for prePMF. Misclassified prePMF cases into ET group reduced from 26 (60.5%) in Clinical model to 5 (11.6%) in Fusion model. Consistently, the number of ET cases (N=70, 95.9%) who were misclassified into prePMF in Clinical model reduced to 4 (5.5%) in Fusion model. These results indicated that our Fusion model may have clinical utility in assisting to identify ET and prePMF. Moreover, Fusion model could distinguish overt PMF effectively with AUC 0.980 (95%CI: 0.961-0.999) even with prePMF, and only 3 (7.5%) prePMF cases misclassified into PMF group, suggesting that our machine learning model had high sensitivity in feature identification and extraction. Next, we compared the performances of the deep learning models with three junior hematopathologists with less than five years of clinical experience and three senior hematopathologists with more than 10 years of experience. 20 cases for each subtype and 20 non-MPN controls, in total 100 cases, were randomly selected from the pool of validation sets with truth label blinded. All the hematopathologists reviewed data and image of 100 patients independently, in parallel with model implementation. Clinical model achieved the highest AUC with 0.925 (0.843-1.000) for PV, which was equivalent with senior hematopathologists (0.929, 0.878-0.979) (difference, 0.004, P=0.8500), while higher than junior ones (0.850, 95%CI: 0.787-0.913) (difference, -0.075, P=0.0007). Fusion model (for ET, 0.806, 95%CI: 0.700-0.913; for prePMF, 0.860, 95%CI: 0.741-0.979) performed better than junior hematopathologists in ET and prePMF identification (for ET, 0.707, 95%CI: 0.539-0.876, P=0.0720; for prePMF, 0.694, 95%CI: 0.564-0.825, P=0.0203), and comparable with senior ones in prePMF and ET identification (for prePMF, 0.787, 95%CI: 0.591-0.984, P=0.2190; for ET, 0.877, 95%CI: 0.860-0.896, P=0.1719). In overt PMF diagnosis, Fusion model (0.952, 95%CI: 0.898-1.000) tended to achieve better performance than both junior (0.850, 95%: 0.774-0.926, P=0.1202) and senior observers (0.823, 95%CI: 0.581-1.000, P=0.0608). The effect sizes could inform future study design for validation. In conclusion, we developed and externally validated the deep learning models for MPNs diagnosis and subtype differentiation achieving the performances equivalent with senior hematopathologists and better than junior ones. Prospective validation and tool development were underwent to promote the accessibility and feasibility of the proposed models in clinical practice.

Inflammatory Bowel Disease Treatment in Cancer Patients—A Comprehensive Review

Inflammatory bowel disease (IBD) is a chronic disease for which medical treatment with immunomodulating drugs is increasingly used earlier to prevent disability. Additionally, cancer occurrence in IBD patients is increased for several reasons, either IBD-related or therapy-associated. Doctors are therefore facing the challenge of managing patients with IBD and a past or current malignancy and the need to balance the risk of cancer recurrence associated with immunosuppressive drugs with the potential worsening of IBD activity if they are withdrawn. This review aims to explore the features of different subtypes of cancer occurring in IBD patients to present current evidence on malignancy recurrence risk associated with IBD medical therapy along with the effects of cancer treatment in IBD and finally to discuss current recommendations on the management of these patients. Due to sparse data, a case-by-case multidisciplinary discussion is advised, including inputs from the gastroenterologist, oncologist, and patient.

Amide proton transfer-weighted MRI features of acute ischemic stroke subtypes.

Stroke is a highly heterogeneous disorder with distinct subtypes, and the stroke subtype influences the outcome. Amide proton transfer-weighted (APTW) MRI has been demonstrated to be promising in stroke patients, but the image characteristics of stroke subtypes have not been sufficiently investigated. The purpose of this study was to investigate the APTW MRI features of different subtypes of acute ischemic stroke (AIS). Ninety-two AIS patients presenting within 96 h of symptom onset were enrolled and examined with a 3.0-T MRI system. Patients were grouped into four subtypes: lacunar circulation infarcts (LACI, n = 33); total anterior circulation infarcts (TACI, n = 9); partial anterior circulation infarcts (PACI, n = 28); and posterior circulation infarcts (POCI, n = 22). APTW values in the lesion (APTWlesion ) and the contralateral normal-appearing region (APTWcontral ) were measured. The change in APTW values between the acute ischemic lesion and the contralateral normal-appearing region (APTWles-con ) was calculated. A two-sample t-test, one-way ANOVA, and the Chi-square method were used. There were significant differences between APTWlesion and APTWcontral in the three categories of nonlacunar strokes (TACI, PACI, and POCI, all p < 0.01), but not for lacunar strokes (LACI, p = 0.080). TACI patients had the lowest APTWlesion and APTWles-con in all groups (p < 0.05). In the POCI group, patients with supratentorial infarcts showed significant differences between APTWlesion and APTWcontral (p = 0.001), while the differences were not significant for infratentorial infarcts (p = 0.135). Our results suggest that the APT effect was heterogeneous in different stroke subtypes, and that APTW MRI gave an excellent performence in depicting nonlacunar AIS in supratentorial locations.

Macular vascular features of different types of diabetic macular edema using ocular coherence tomography angiography- a comparative study

BackgroundTo compare the microvascular features of different subtypes of diabetic macular edema (DME) by optical coherence tomography angiography (OCTA).MethodsA cross-sectional study including treatment-naive patients with DME. Eyes were divided according to optical coherence tomography determined morphology into two groups: cystoid macular edema (CME) and diffuse retinal thickening (DRT), with further subdivision according to the presence of subretinal fluid. All patients underwent 3 × 3 and 6 × 6 mm OCTA scans of the macula to compare the foveal avascular zone (FAZ) area, vascular density (VD) of the superficial (SCP) and deep (DCP) capillary plexus and choriocapillaris flow (CF). Laboratory findings (HbA1C and triglyceride levels) were also correlated with the OCTA findings.ResultsThe study included 52 eyes, 27 had CME and 25 had DRT. There were no significant differences between the VD of the SCP (p = 0.684) and DCP (p = 0.437), FAZ of SCP (p = 0.574), FAZ of DCP (p = 0.563) and CF (p = 0.311). Linear regression analysis revealed that DME morphology was the strongest predictor for BCVA. Other significant predictors included HbA1C and triglyceride levels.ConclusionThe morphology of DME, irrespective of SRF, was most significantly correlated with BCVA in treatment-naive patients and CME subtype could be an independent predictor of poor BCVA in patients with DME.

Establishing a metastasis-related diagnosis and prognosis model for lung adenocarcinoma through CRISPR library and TCGA database.

Existing biomarkers for diagnosing and predicting metastasis of lung adenocarcinoma (LUAD) may not meet the demands of clinical practice. Risk prediction models with multiple markers may provide better prognostic factors for accurate diagnosis and prediction of metastatic LUAD. An animal model of LUAD metastasis was constructed using CRISPR technology, and genes related to LUAD metastasis were screened by mRNA sequencing of normal and metastatic tissues. The immune characteristics of different subtypes were analyzed, and differentially expressed genes were subjected to survival and Cox regression analyses to identify the specific genes involved in metastasis for constructing a prediction model. The biological function of RFLNA was verified by analyzing CCK-8, migration, invasion, and apoptosis in LUAD cell lines. We identified 108 differentially expressed genes related to metastasis and classified LUAD samples into two subtypes according to gene expression. Subsequently, a prediction model composed of eight metastasis-related genes (RHOBTB2, KIAA1524, CENPW, DEPDC1, RFLNA, COL7A1, MMP12, and HOXB9) was constructed. The areas under the curves of the logistic regression and neural network were 0.946 and 0.856, respectively. The model effectively classified patients into low- and high-risk groups. The low-risk group had a better prognosis in both the training and test cohorts, indicating that the prediction model had good diagnostic and predictive power. Upregulation of RFLNA successfully promoted cell proliferation, migration, invasion, and attenuated apoptosis, suggesting that RFLNA plays a role in promoting LUAD development and metastasis. The model has important diagnostic and prognostic value for metastatic LUAD and may be useful in clinical applications.

Identification of the cuproptosis-related molecular subtypes and an immunotherapy prognostic model in hepatocellular carcinoma

BackgroundCuproptosis, a newly discovered mode of cell death, has been less studied in hepatocellular carcinoma (HCC). Exploring the molecular characteristics of different subtypes of HCC based on cuproptosis-related genes (CRGs) is meaningful to HCC. In addition, immunotherapy plays a pivotal role in treating HCC. Exploring the sensitivity of immunotherapy and building predictive models are critical for HCC.MethodsThe 357 HCC samples from the TCGA database were classified into three subtypes, Cluster 1, Cluster 2, and Cluster 3, based on the expression levels of ten CRGs genes using consensus clustering. Six machine learning algorithms were used to build models that identified the three subtypes. The molecular features of the three subtypes were analyzed and compared from some perspectives. Moreover, based on the differentially expressed genes (DEGs) between Cluster 1 and Cluster 3, a prognostic scoring model was constructed using LASSO regression and Cox regression, and the scoring model was used to predict the efficacy of immunotherapy in the IMvigor210 cohort.ResultsCluster 3 had the worst overall survival compared to Cluster 1 and Cluster 2 (P = 0.0048). The AUC of the Catboost model used to identify Cluster 3 was 0.959. Cluster 3 was significantly different from the other two subtypes in gene mutation, tumor mutation burden, tumor microenvironment, the expression of immune checkpoint inhibitor genes and N6-methyladenosine regulatory genes, and the sensitivity to sorafenib. We believe Cluster 3 is more sensitive to immunotherapy from the above analysis results. Therefore, based on the DEGs between Cluster 1 and Cluster 3, we obtained a 7-gene scoring prognostic model, which achieved meaningful results in predicting immunotherapy efficacy in the IMvigor210 cohort (P = 0.013).ConclusionsOur study provides new ideas for molecular characterization and immunotherapy of HCC from machine learning and bioinformatics. Moreover, we successfully constructed a prognostic model of immunotherapy.

A National, Multicenter, Retrospective Study of Castleman Disease Implementing Cdcn Criteria: 'severe Imcd' Is Indeed Severe

Objective: To delineate the distribution, clinical characteristics and prognostic features of different subtypes of Castleman disease based on the CDCN criteria. To describe the first-line treatment choice for iMCD in China before the era of IL-6-directed therapy. Methods: This was a national, observational, retrospective study which enrolled consecutive patients with CD diagnosed at 40 large Chinese institutions from 2000 to 2021. Patients were classified into different CD subtypes according to the CDCN criteria. The primary outcomes were the distribution of different CD subtypes, descriptions of demographic and clinical characteristics, treatment options and overall survival. Results: A total of 1634 CD patients (unicentric CD(UCD), n=903; multicentric CD(MCD), n=731) were enrolled. Among UCDs, hyaline-vascular (HV) was the major pathological subtype, and there were 162(17.9%) patients with an MCD-like inflammatory state (UCD-MIS). Among the MCD patients, there were 12 HHV-8-related MCD patients and 719 HHV-8-negative MCD patients (asymptomatic MCD (aMCD), n=139; iMCD, n=580). Of the 580 iMCD patients, 41(7.1%) were classified as having iMCD-TAFRO, while the others were classified as having iMCD-NOS. Among the iMCD patients with first-line treatment information, a trend from pulse combination chemotherapy toward a continuous approach was noticed (Fig 1A). Survival analysis revealed significant differences between different subtypes, and the estimated 3-year overall survival rates for UCD, iMCD-NOS and iMCD-TAFRO patients were 98.0%, 89.6% and 65.7%, respectively. Among the iMCD-NOS patients, the multivariate Cox regression model identified 'severe iMCD' (HR=4.540; 95% CI 1.888-10.915, p=0.001) as the only independent risk factor for death (Fig 1B). The estimated 3-year overall survival rates for severe and nonsevere iMCD-NOS patients were 93.8% and 75.6%, respectively. Conclusion: This study depicts a whole picture of CD subtype distribution, treatment options and survival information in China and validates the rationality of the concept of severe iMCD proposed by the CDCN. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Single-cell sequencing analysis and transcriptome analysis constructed the macrophage related gene-related signature in lung adenocarcinoma and verified by an independent cohort

BackgroundRecent studies have emphasized the close relationship between macrophages and tumor immunity, and the prognosis of lung adenocarcinoma (LUAD) patients is intimately linked to this. Nonetheless, the prognostic signature and classification of different immune patterns in LUAD patients based on the macrophages is largely unexplored. MethodsTwo sc-RNAseq datasets of LUAD patients were collected and reprocessed. The differentially expressed genes (DEGs) related to macrophages between LUAD tissues and normal lung tissues were then identified. Based upon the above genes, three distinct immune patterns in the TCGA-LUAD cohort were identified. The ssGSEA and CIBERSORT were applied for immune profiling and characterization of different subtypes. A four-gene prognostic signature for LUAD patients was established based on the DEGs between the subtypes using stepwise multi-Cox regression. TCGA-LUAD cohort was used as training set. Five GEO-LUAD datasets and an independent cohort containing 112 LUAD samples were used for validation. TIDE (tumor immune dysfunction and exclusion) and drug sensitivity analyses were also performed. ResultsMacrophage-related differentially expressed genes were found out using the publicly available scRNA-seq data of LUAD. Three different immune patterns which were proved to have distinct immune infiltration characteristics in the TCGA-LUAD cohort were recognized based on the above macrophage-related genes. Thereafter, 174 DEGs among the above three different immune patterns were figured out; on the basis of this, a four-gene prognostic signature was constructed. This signature distinguished the prognosis of LUAD patients well in various GSE datasets as well as our independent cohort. Further analyses revealed that patients which had a higher risk score also accompanied with a lower immune infiltration level and a worse response to several immunotherapy biomarkers. ConclusionThis study highlighted that macrophage were significantly associated with TME diversity and complexity. The four-gene prognostic signature could be used for predicting outcomes and immune landscapes for patients with LUAD.

Clinical value of classification in the treatment of children with suprasellar arachnoid cysts.

The aim of this study is to investigate the clinical characteristics and prognostic effects of different subtypes of suprasellar arachnoid cysts (SAC) in children. Fifty patients with primary SAC who had undergone endoscopic treatment in our department between January 2010 and December 2020 were studied retrospectively. All patients underwent endoscopic treatment after diagnosis, including ventriculocystostomy (VC) in 23 cases and ventriculocystocisternostomy (VCC) in 27 cases. All patients were followed up regularly after the operation, including head computed tomography (CT)/magnetic resonance imaging (MRI), and Evans index (EI) and frontal and occipital horn ratio (FOHR) index were measured to assess changes in cyst volume and hydrocephalus. The prognosis was evaluated comprehensively on the data of the improvement of clinical symptoms, child growth and development correlation score, and reduction of cyst volume 12months after surgery. According to the new classification of SAC, 50 cases of children were classified into three groups in which we compared the clinical characteristics of different subtypes of the three groups. Logistic regression was used to analyze the influencing factors of prognosis. Completed success was achieved in 50 cases, including 31 cases with cyst volume reduction of more than 50% and 19 cases with cyst volume reduction of less than 50%. The median follow-up time was 55.3months (22 ~ 113months). According to the new classification criteria of SAC, there were 21 cases of SAC-1, 16 cases of SAC-2, and 13 cases of SAC-3. There were no statistically significant differences among the three groups in gender, birth weight, prenatal diagnosis, hydrocephalus, endocrine abnormalities, relief of postoperative symptoms, cyst wall texture, and surgical methods (P > 0.05). There was a statistically significant difference among the three groups in the change of the cyst volume and the maximum cyst diameter (P < 0.05), in which SAC-1 had the largest volume reduction, SAC-2 was more likely to cause endocrine symptoms and SAC-3 was inclined to lie in between. Multivariate logistic analysis showed that SAC classification and cyst wall texture were independent risk factors for the prognosis. The clinical characteristics of different SAC subtypes are different, and SAC classification is one of the independent risk factors affecting prognosis.

M7G regulator-mediated molecular subtypes and tumor microenvironment in kidney renal clear cell carcinoma

Background: RNA methylation modification plays an important role in immune regulation. m7G RNA methylation is an emerging research hotspot in the RNA methylation field. However, its role in the tumor immune microenvironment of kidney renal clear cell carcinoma (KIRC) is still unclear. Methods: We analyzed the expression profiles of 29 m7G regulators in KIRC, integrated multiple datasets to identify a novel m7G regulator-mediated molecular subtype, and developed the m7G score. We evaluated the immune tumor microenvironments in m7G clusters and analyzed the correlation of the m7G score with immune cells and drug sensitivity. We tested the predictive power of the m7G score for prognosis of patients with KIRC and verified the predictive accuracy of the m7G score by using the GSE40912 and E-MTAB-1980 datasets. The genes used to develop the m7G score were verified by qRT-PCR. Finally, we experimentally analyzed the effects of WDR4 knockdown on KIRC proliferation, migration, invasion, and drug sensitivity. Results: We identified three m7G clusters. The expression of m7G regulators was higher in cluster C than in other clusters. m7G cluster C was related to immune activation, low tumor purity, good prognosis, and low m7G score. Cluster B was related to drug metabolism, high tumor purity, poor survival, and high m7G score. Cluster A was related to purine metabolism. The m7G score can well-predict the prognosis of patients with KIRC, and its prediction accuracy based on the m7G score nomogram was very high. Patients with high m7G scores were more sensitive to rapamycin, gefitinib, sunitinib, and vinblastine than other patients. Knocking down WDR4 can inhibit the proliferation, migration, and invasion of 786-0 and Caki-1 cells and increase sensitivity to sorafenib and sunitinib. Conclusion: We proposed a novel molecular subtype related to m7G modification and revealed the immune cell infiltration characteristics of different subtypes. The developed m7G score can well-predict the prognosis of patients with KIRC, and our research provides a basis for personalized treatment of patients with KIRC.

Characterization of Different Subtypes of Immune Cell Infiltration in Glioblastoma to Aid Immunotherapy.

Glioblastoma multiforme (GBM) has been identified as a frequently occurring adult primary brain cancer that is highly aggressive. Currently, the prognostic outcome for GBM patients is dismal, even with intensive treatment, and the median overall survival (OS) is 14.6 months. Immunotherapy, which is specific at the cellular level and can generate persistent immunosurveillance, is now becoming a promising tool to treat diverse cancers. However, the complicated nature of the tumor microenvironment (TME) makes it challenging to develop anti-GBM immunotherapy because several cell types, cytokines, and signaling pathways are involved in generating the immunosuppressive environment. Novel immunotherapies can illustrate novel tumor-induced immunosuppressive mechanisms. Here, we used unsupervised clustering analysis to identify different subtypes of immune cell infiltration that actuated different prognoses, biological actions, and immunotherapy responses. Gene cluster A, with a hot immune cell infiltration phenotype, had high levels of immune-related genes (IRGs), which were associated with immune pathways including the interferon-gamma response and interferon-alpha response, and had low IDH1 and ATRX mutation frequencies. Gene cluster B, a cold immune cell infiltration subtype, exhibited a high expression of the KCNIP2, SCRT1, CPLX2, JPH3, UNC13A, GABRB3, ARPP21, DLGAP1, NRXN1, DLL3, CA10, MAP2, SEZ6L, GRIA2, and GRIA4 genes and a low expression of immune-related genes, i.e., low levels of immune reactivity. Our study highlighted the complex interplay between immune cell infiltration and genetic mutation in the establishment of the tumor immune phenotype. Gene cluster A was identified as an important subtype with a better prognosis and improved immunotherapy response.

Abstract 5128: Identification and characterization of nasopharyngeal carcinoma prognostic subtypes based on a circRNA classifier: Implications of stratified treatment

Abstract Accurate risk stratification for patients with locoregionally advanced nasopharyngeal carcinoma (LA-NPC) is crucial for treatment decisions and prognosis. Here, we introduce a circRNA-based classifier that could improve the prognostic stratification of LA-NPC patients. The circRNAs are identified by microarray profiling, followed by qRT-PCR validation and selection using a machine learning method in the training cohort (n = 170). Based on the proposed classifier, two prognostic-distinctive NPC subtypes are identified, namely the high-risk subtype (subtype H) and low-risk subtype (subtype L), which have significantly different disease-free survival (HR 2.99, 95% CI 1.58-5.67, P &amp;lt; 0.001). Validation in the internal cohort (n = 170) and external cohort (n = 150) yield similar results and the circRNA-based classifier is an independent risk factor of posttreatment relapse. Meanwhile, multiple interdependent features of different subtypes are observed, including relatively higher immune cell infiltration and immune-related pathways activation in subtype L; and predominant stromal, immunosuppressive and tumor-promoting components with moderate immune activation in subtype H. With a less aggressive and more immune-active tumor microenvironment, patients of subtype L could benefit from the addition of induction chemotherapy. In contrast, patients of subtype H, who are vulnerable to chemoresistance, display a strong tendency to benefit from additional immunotherapy. Overall, we define two NPC prognostic subtypes based on a circRNA classifier across multiple cohorts, which exhibit distinct biological ecosystems and suggest opportunities for the personalization of therapeutic regimens. Citation Format: Yelin Liang, Yuheng Zhao, Qian Li, Wei Jiang, Jun Ma, Na Liu, Yingqin Li. Identification and characterization of nasopharyngeal carcinoma prognostic subtypes based on a circRNA classifier: Implications of stratified treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5128.

Evolving classification and role of muscle biopsy in diagnosis of inflammatory myopathies.

Idiopathic inflammatory myopathy (IIM) is a broad term that includes dermatomyositis, polymyositis, overlap myositis, sporadic inclusion body myositis, and immune-mediated necrotizing myopathy. The understanding of the pathogenesis of IIM is ever-evolving with regular updates in the classification schema. With the recognition of autoantibodies and their detection, the diagnostic algorithms are changing in favor of non-invasive diagnoses. However, muscle biopsy has immensely contributed to our understanding of the pathogenesis of inflammatory myopathies, and the pathologic features of different subtypes are well established. The biopsy also aids in distinguishing myopathies with overlapping clinical features, particularly dystrophies, which can show inflammation on biopsy in some cases. In this article, the various classification schemes of the IIM are reviewed. Also, the pathogenesis and pathology of each type of IIM have been highlighted. This article emphasizes the role of muscle biopsy in the diagnosis of inflammatory myopathies.

Identification of glycolysis-associated long non-coding RNA regulatory subtypes and construction of prognostic signatures by transcriptomics for bladder cancer.

Glycolysis-targeted cancer therapy based on long non-coding RNAs (lncRNAs), owing to its high specificity and less toxicity, is at the preclinical stages. Our study aimed to examine the roles of the core glycolysis-associated lncRNAs in bladder cancer (BC). Glycolysis scores of BC were computed by single-sample gene set enrichment analysis (ssGSEA). Glycolysis-associated lncRNAs were screened by Pearson's correlation analysis. Unsupervised consensus clustering using ConsensusClusterPlus assessed the glycolysis-associated lncRNAs for the identification of molecular subtypes of BC. The Kaplan-Meier survival analysis, genomic mutations, and tumor microenvironment (TME) analysis were used to compare the characteristics of different subtypes. Key glycolysis-associated lncRNAs were screened by first-order partial correlation and univariate Cox proportional-hazards model analyses; finally, the lncRNA signature was constructed. Four glycolysis-associated lncRNA-regulated subtypes having differential overall survival (OS), clinical features, genomic mutation profiles, and TME profiles along with nuclear immunotherapeutic responses were identified. Nine lncRNAs localized in the nucleus were identified and transcription factors (TFs) significantly negatively associated with these were found to be enriched in multiple oncogenic signaling pathways. Among them, three lncRNAs (AC093673.5, AC034220.3, and RP11-250B2.3) exerted the most profound effects on glycolysis and constituted the lncRNA signature, which could substantially distinguish the risk levels among different BC patients. Four glycolysis-associated lncRNA-regulated subtypes were identified in this study, reflective of the biological characteristics and heterogeneity of BC. Three key glycolysis-associated lncRNA constituting a signature could predict the risk levels in BC, provide a reference for stratification, and be used as prognostic markers for BC diagnosis and treatment.

Clinical Features, Diagnostic Test Performance, and Prognosis in Different Subtypes of Chronic Pulmonary Aspergillosis

ObjectiveThe aim of this study was to describe clinical features in different subtypes of chronic pulmonary aspergillosis (CPA)-simple aspergilloma (SA), chronic cavitary pulmonary aspergillosis (CCPA), chronic fibrosing pulmonary aspergillosis (CFPA), aspergillus nodule (AN), and subacute invasive aspergillosis (SAIA), respectively, and identify long-term prognosis of CPA.MethodsWe reviewed patients diagnosed with different subtypes of CPA from 2002 to 2020 at Nanjing Jinling Hospital, China. We analyzed the clinical and survival information of five different subgroups. A Cox regression model was used to explore proper antifungal duration and long-term survival factors of CCPA and SAIA.ResultsA total of 147 patients with CPA were included, consisting of 11 SA, 48 CCPA, 5 CFPA, 12 AN, and 71 SAIA. The most common underlying pulmonary disease was pulmonary tuberculosis (n = 49, 33%), followed by bronchiectasis (n = 46, 31.3%) and chronic obstructive pulmonary disease (COPD) or emphysema (n = 45, 30.6%), while in SAIA and CFPA groups, the most common was COPD or emphysema (45.1 and 100%). Cough (85%), expectoration (70.7%), hemoptysis (54.4%), and fever (29.9%) were common symptoms, especially in CCPA, CFPA, and SAIA groups. The common imaging manifestations included cavitation (n = 94, 63.9%), fungal ball (n = 54, 36.7%), pleural thickening (n = 47, 32.0%), and bronchiectasis (n = 46, 31.3%). SAIA and CFPA groups had a lower value of hemoglobin (HB) and serum albumin (ALB) with higher C-reactive protein and erythrocyte sedimentation rate. The positive rate of sputum culture, serum galactomannan (GM), and bronchoalveolar lavage fluid GM was 32.7% (36/110), 18.4% (18/98), and 48.7% (19/39), respectively. There were 64.6% (31/48) patients with CCPA and 25.4% (18/71) patients with SAIA who received surgery and the 5-year cumulative survival rate was 92.1 and 66.6%, respectively. SAIA, old age, male, low body mass index (BMI), COPD or emphysema, multiple distribution, low serum ALB, and positive sputum culture were adverse prognosis factors for SAIA and CCPA group, and BMI ≤ 20.0 kg/m2 was independently associated with increased mortality (hazard ratio (HR) 5.311, 95% CI 1.405–20.068, p = 0.014). Multivariable Cox regression indicated that surgery (HR 0.093, 95% CI 0.011–0.814, p = 0.032) and antifungal duration >6 months (HR 0.204, 95% CI 0.060–0.696 p = 0.011) were related to improved survival.ConclusionThe clinical features and laboratory test performance are different among SA, CCPA, CFPA, AN, and SAIA. Low BMI was an independent risk factor for survival. Selective surgery and antifungal duration over 6 months were associated with improved survival.

Rapid typing diagnosis and clinical analysis of subtypes A and B of human respiratory syncytial virus in children

BackgroundHuman respiratory syncytial virus (HRSV) is the leading pathogens causing acute respiratory infections (ARI) in children under five years old. We aimed to investigate the distribution of HRSV subtypes and explore the relationship between viral subtypes and clinical symptoms and disease severity.MethodsFrom November 2016 to April 2017, 541 children hospitalized because of ARI were included in the study. Throat swabs were collected for analysis and all samples were tested by multiplex one-step qRT-PCR for quantitative analysis and typing of HRSV. Patients’ demographics, clinical symptoms as well as laboratory and imaging results were retrieved from medical records.ResultsHRSV was detected in 19.6% of children hospitalized due to ARI. HRSV-positive children were younger (P < 0.001), had a higher frequency of wheezing and pulmonary rales (P < 0.001; P = 0.003), and were more likely to develop bronchopneumonia (P < 0.001). Interleukin (IL) 10、CD4/CD8 (below normal range) and C-reactive protein levels between subtypes A and B groups were significantly different (P = 0.037; P = 0.029; P = 0.007), and gender differences were evident. By age-stratified analysis between subtypes A and B, we found significant differences in fever frequency and lymphocyte ratio (P = 0.008; P = 0.03) in the 6–12 months age group, while the 12. 1–36 months age group showed significant differences in fever days and count of leukocytes, platelets, levels aspartate aminotransferase, IL-6, lactate dehydrogenase and proportion CD4 positive T cells(P = 0.013; P = 0.018; P = 0.016; P = 0.037; P = 0.049; P = 0.025; P = 0.04). We also found a positive correlation between viral load and wheezing days in subtype A (P < 0.05), and a negative correlation between age, monocyte percentage and LDH concentration in subtype B (P < 0.05).ConclusionsHRSV is the main causative virus of bronchopneumonia in infants and children. The multiplex one-step qRT-PCR not only provides a rapid and effective diagnosis of HRSV infection, but also allows its typing. There were no significant differences in the severity of HRSV infection between subtypes A and B, except significant gender-specific and age-specific differences in some clinical characteristics and laboratory results. Knowing the viral load of HRSV infection can help understanding the clinical features of different subtypes of HRSV infection.