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Background: Diffuse large B-cell lymphoma (DLBCL) is the predominant type of malignant B-cell lymphoma. Although various treatments have been developed, the limited efficacy calls for more and further exploration of its characteristics. Methods: Datasets from the Gene Expression Omnibus (GEO) database were used for identifying the tumor purity of DLBCL. Survival analysis was employed for analyzing the prognosis of DLBCL patients. Immunohistochemistry was conducted to detect the important factors that influenced the prognosis. Drug-sensitive prediction was performed to evaluate the value of the model. Results: VCAN, CD3G, and C1QB were identified as three key genes that impacted the outcome of DLBCL patients both in GEO datasets and samples from our center. Among them, VCAN and CD3G+ T cells were correlated with favorable prognosis, and C1QB was correlated with worse prognosis. The ratio of CD68 + macrophages and CD8 + T cells was associated with better prognosis. In addition, CD3G+T cells ratio was significantly correlated with CD68 + macrophages, CD4 + T cells, and CD8 +T cells ratio, indicating it could play an important role in the anti-tumor immunity in DLBCL. The riskScore model constructed based on the RNASeq data of VCAN, C1QB, and CD3G work well in predicting the prognosis and drug sensitivity. Conclusions: VCAN, CD3G, and C1QB were three key genes that influenced the tumor purity of DLBCL, and could also exert certain impact on drug sensitivity and prognosis of DLBCL patients. Funding: This work is supported by the Shenzhen High-level Hospital Construction Fund and CAMS Innovation Fund for Medical Sciences (CIFMS) (2022-I2M-C&T-B-062).

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Tumor microenvironment (TME) is closely associated with the progression and prognosis of head and neck squamous cell carcinoma (HNSCC). To investigate potential biomarkers for predicting therapeutic outcomes in HNSCC, we analyzed the immune and stromal status of HNSCC based on the genes associated with TME using the ESTIMATE algorithm. Immune and stromal genes were identified with differential gene expression and weighted gene co-expression network analysis (WGCNA). From these genes, 118 were initially selected through Cox univariate regression and then further input into least absolute shrinkage and selection operator (LASSO) regression analysis. As a result, 11 genes were screened out for the TME-related risk (TMErisk) score model which presented promising overall survival predictive potential. The TMErisk score was negatively associated with immune and stromal scores but positively associated with tumor purity. Individuals with high TMErisk scores exhibited decreased expression of most immune checkpoints and all human leukocyte antigen family genes, and reduced abundance of infiltrating immune cells. Divergent genes were mutated in HNSCC. In both high and low TMErisk score groups, the tumor protein P53 exhibited the highest mutation frequency. A higher TMErisk score was found to be associated with reduced overall survival probability and worse outcomes of immunotherapy. Therefore, the TMErisk score could serve as a valuable model for the outcome prediction of HNSCC in clinic.

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Background: While many molecular assays can detect mutations at low tumor purity and variant allele frequencies, complex biomarkers such as tumor mutational burden (TMB), microsatellite instability (MSI), and genomic loss of heterozygosity (gLOH) require higher tumor purity for accurate measurement. Scalable, quality-controlled, tissue-conserving methods to increase tumor nuclei percentage (TN%) from tumor specimens are needed for complex biomarkers and hence necessary to maximize patient matching to approved therapies or clinical trial enrollment. We evaluated the clinical utility and performance of precision needle-punch enrichment (NPE) compared with traditional razor blade macroenrichment of tumor specimens on molecular testing success. Methods: Pathologist-directed NPE was performed manually on formalin-fixed, paraffin embedded (FFPE) blocks. Quality control of target capture region and quantity of residual tumor in each tissue block was determined via a post-enrichment histologic slide recut. Resultant tumor purity and biomarker status were determined by the computational analysis pipeline component of the FDA-approved next-generation sequencing (NGS) assay, FoundationOne®CDx. Following NPE implementation for real-world clinical samples, assay performance and biomarker (MSI, TMB, gLOH) detection were analyzed. Results: In real-world clinical samples, enrichment rate via NPE was increased to ~50% over a 2.5-year period, exceeding the prior use of razor blade macro-enrichment (<30% of cases) prior to NPE implementation due to proven efficacy in generating high quality molecular results from marginal samples and the ease of use for both pathologist and histotechnologists. NPE was associated with lower test failures, higher computational tumor purity, and higher rates of successful TMB, MSI and gLOH determination when stratified by pre-enriched (incipient) tumor nuclei percentage. In addition, challenging cases in which tumor content was initially insufficient for testing were salvaged for analysis of biomarker status, gene amplification/deletion, and confident mutant or wild-type gene status determination. Conclusions: Pathologist-directed precision enrichment from tissue blocks (aka NPE) increases tumor purity, and consequently, yields a greater number of successful tests and complex biomarker determinations. Moreover, this process is rapid, safe, inexpensive, scalable, and conserves patient surgical pathology material. NPE may constitute best practice with respect to enriching tumor cells from low-purity specimens for biomarker detection in molecular laboratories.

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Purpose: This study aimed to investigate the diagnostic and prognostic values of neuropilin-1 (NRP-1) in triple-negative breast cancer (TNBC) and analyze its immune function in the tumor microenvironment. Methods: Based on The Cancer Genome Atlas (TCGA), Gene Expression Omnibus, Genotype Tissue Expression, Immune Cell Abundance Identifier (ImmuCellAI), Reactome, and Genomics of Drug Sensitivity in Cancer databases, the cancer tissues from 50 patients with TNBC and corresponding adjacent noncancerous tissues from 10 patients (tissue microarrays were purchased from Shanghai Xinchao Biotechnology Co., Ltd.) were collected for validation. Bioinformatics combined with immunohistochemistry was used to analyze the relationship among NRP-1 expression, prognosis, tumor immune cell infiltration, immune genes, and drug resistance so as to investigate the role of NRP-1 in the development of TNBC. Results: A significant difference in NRP-1 gene expression was found between the cancerous and noncancerous tissues (p-value < 0.05); NRP-1 expression was high in carcinoma. No significant correlation was found between NRP-1 protein expression levels and each stage in the TCGA database. Prognostic expression survival analysis showed that the survival probability of patients with high NRP-1 expression was significantly lower than that of patients with low NRP-1 expression (p-value < 0.05), suggesting that the gene might be a pro-oncogene. The data from 50 clinical samples also confirmed that the NRP-1 expression was significantly higher in triple-negative breast cancer (TNBC) tissues than in adjacent noncancerous tissues. The NRP-1 expression significantly correlated with the tumor diameter and pathological grade (p-value < 0.05), but not with age, stage, and ki67 (p-value > 0.05). The Kaplan-Meier survival curves suggested that the median overall survival was significantly shorter in patients with high NRP-1 expression than in those with low NRP-1 expression (13.6 months vs 15.2 months, p-value < 0.05). The 300 genes most significantly positively associated with this gene were selected for Gene Ontology (including Biological Process, Molecular Function, and Cellular Component groups) and Kyoto Encyclopedia of Genes and Genomics enrichment analysis. The findings showed that NRP-1 was involved in immune regulation in TNBC. In addition, the NRP-1 expression in TNBC positively correlated with a variety of immune cells and checkpoints. Conclusion: NRP-1 can be used as a potential biomarker and therapeutic target in TNBC.

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The function of Vasohibin-1 (VASH1) in human cancer has not been thoroughly or comprehensively examined. Here, we identified the tumor suppressor part of VASH1 across cancers, including epithelial ovarian tumors. Our study carefully contrasted the expression of VASH1 in pancancer and nontumorous tissues in a public database to explore its regulatory role in clinical prognosis, diagnosis, tumor purity, and immune cell infiltration. Next, we explored the antitumor mechanism of VASH1 through drug sensitivity, functional enrichment, and phenotypic experiments in ovarian cancer. Research suggests that the expression of VASH1 in neoplastic tissues is lower than that in normal tissues. VASH1 affects the OS and RFS of several tumor types. In addition, VASH1 expression resulted in a high OS and RFS in the diagnosis of tumor and nontumor tissues and negatively regulated tumor purity. Moreover, VASH1 controls the tumor microenvironment by regulating immunocyte infiltration. In ovarian cancer, VASH1 can serve as a biomarker to estimate the efficacy of chemotherapy. Functional enrichment analysis suggests that VASH1 plays a tumor suppressor role by regulating the extracellular matrix receptor pathway. VASH1 inhibition of the malignant phenotype of ovarian cancer cells was further confirmed by in vivo experiments. These results indicate that VASH1 acts as a cancer-inhibiting factor and potential therapeutic target in ovarian cancer.

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BACKGROUND: umor cells, immune cells and stromal cells jointly modify tumor development and progression. We aim to explore the potential effects of tumor purity on the immune microenvironment, genetic landscape and prognosis in prostate cancer (PCa). METHODS: Tumor purity of prostate cancer patients was extracted from The cancer genome atlas (TCGA). Immune cellular proportions were calculated by the CIBERSORT. To identify critical modules related to tumor purity, we used weighted gene co-expression network analysis (WGCNA). Using STRING and Cytoscape, protein-protein interaction (PPI) networks were constructed and analyzed. A Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA) enrichment analysis of identified modules was conducted. To identify the expression of key genes at protein levels, we used the Human Protein Atlas (HPA) platform. RESULTS: A model of tumor purity score (TPS) was constructed in the gene expression omnibus series (GSE) 116,918 cohort. TCGA cohort served as a validation set and was employed to validate the TPS. TPS model, as an independent prognostic factor of distant metastasis-free survival (DMFS) in PCa. Patients had higher tumor purity and better prognosis in the low-TPS group. Tumor purity was related to the infiltration of mast cells and macrophage cells positively, whereas related to the infiltration of dendritic cells, T cells and B cells negatively in PCa. The nomogram based on TPS, Age, Gleason score and T stage had a good predictive value and could evaluate the prognosis of PCa metastasis. GO and KEGG enrichment analyses showed that hub genes mainly participate in T cell activation and T-helper lymphocytes (TH) differentiation. Hub genes were mainly enriched in primary immunodeficiency disease, according to DO analysis. SLAMF8 was identified as the most critical gene by Cytoscape and HPA analysis. CONCLUSIONS: Dynamic changes in the immune microenvironment associated with tumor purity could correlate with a poor DMFS of low-purity PCa. The TPS can predict the DMFS of PCa. In addition, prostate cancer metastases may be related to immunosuppression caused by a disorder of the immune microenvironment.

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OBJECTIVES: This article aims to establish the relationship between pathologic diagnosis and the rate of success in cancer next-generation sequencing testing. METHODS: Clinical next-generation sequencing results performed for solid tumors were reviewed. The rate of success was analyzed in the context of tumor type and accompanying variables. RESULTS: Out of 683 total specimens, 533 (78.0%) underwent successful sequencing. The rate of success was 91.8% for ovarian carcinomas, 87.5% for lung non-small cell carcinomas, 82.0% for colorectal adenocarcinomas, 78.3% for melanomas, 75.9% for breast carcinomas, and 64.7% for pancreatic adenocarcinomas. For specimens that successfully underwent sequencing, pancreatic adenocarcinomas had the lowest median tumor proportion and somatic RAS and TP53 mutation allele fractions compared with other tumor types. Cytology specimens had a 33.3% success rate for pancreatic adenocarcinomas (5 of 15) and a 93.3% success rate for lung carcinomas (14 of 15). Compared with tissue from primary sites, tissue from metastatic sites showed a higher success rate for pancreatic adenocarcinomas and lower success rates for colorectal adenocarcinomas and melanomas. CONCLUSIONS: The success rate of cancer next-generation sequencing testing is dependent on pathologic diagnosis, tissue site, and diagnostic procedure. Understanding which specimens are at higher risk for failing molecular testing may help pathologists and clinical care teams optimize tissue acquisition and usage for patient care.

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Introduction: Tumor purity takes on critical significance to the progression of solid tumors. The aim of this study was at exploring potential prognostic genes correlated with tumor purity in hepatocellular carcinoma (HCC) by bioinformatics analysis. Methods: The ESTIMATE algorithm was applied for determining the tumor purity of HCC samples from The Cancer Genome Atlas (TCGA). The tumor purity-associated genes with differential expression (DEGs) were identified based on overlap analysis, weighted gene co-expression network analysis (WGCNA), and differential expression analysis. The prognostic genes were identified in terms of the prognostic model construction based on the Kaplan-Meier (K-M) survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses. The expression of the above-described genes was further validated by the GSE105130 dataset from the Gene Expression Omnibus (GEO) database. We also characterized the clinical and immunophenotypes of prognostic genes. Gene set enrichment analysis (GSEA) was carried out for exploring the biological signaling pathway. Results: A total of 26 tumor purity-associated DEGs were identified, which were involved in biological processes such as immune/inflammatory responses and fatty acid elongation. Ultimately, we identified ADCK3, HK3, and PPT1 as the prognostic genes for HCC. Moreover, HCC patients exhibiting higher ADCK3 expression and lower HK3 and PPT1 expressions had a better prognosis. Furthermore, high HK3 and PPT1 expressions and low ADCK3 expression resulted in high tumor purity, high immune score, high stromal score, and high ESTIMATE score. GSEA showed that the abovementioned prognostic genes showed a significant correlation with immune-inflammatory response, tumor growth, and fatty acid production/degradation. Discussion: In conclusion, this study identified novel predictive biomarkers (ADCK3, HK3, and PPT1) and studied the underlying molecular mechanisms of HCC pathology initially.

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Assessment of homologous recombination deficiency (HRD) status is now essential for ovarian cancer patient management. The aim of our study was to analyze the influence of ethnic variations, tumor purity, and neoadjuvant chemotherapy (CT) on the determination of HRD scores as well as to evaluate feasibility of HRD testing with the Amoy HRD Focus Assay in routine clinical practice. The HRD status, including the BRCA status and genomic scar score (GSS), was analyzed in 452 ovarian cancer specimens. The successful rate of HRD testing was 86% (388/452). The BRCA mutational rate was 29% (114/388); 252 samples (65%) were classified as HRD-positive. Our data demonstrate the feasibility of internal HRD testing by the AmoyDx HRD Focus Panel for high-grade serous ovarian cancer (HGSOC), showing results similar to other methods. The HRD rate in the Russian population is very similar to those of other European populations, as is the BRCA mutation frequency. The most substantial contribution to HRD level diversity is testing criteria depending on intrahospital arrangements. The analysis shows that biallelic BRCA alterations had higher GSS compared with those with monoallelic inactivation, consistent with positive HRD status. The study indicates that grades 1-2 of the pathological response caused by chemotherapy affect HRD scores and suggests controlling for tumor purity of 40% or more as a critical factor for GSS measurement.

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Homologous recombination deficiency (HRD) is a predictive marker for response to poly (ADP-ribose) polymerase inhibitors (PARPi) in ovarian carcinoma. HRD scores have entered routine diagnostics, but the influence of algorithms, parameters and confounders has not been analyzed comprehensively. A series of 100 poorly differentiated ovarian carcinoma samples was analyzed using whole exome sequencing (WES) and genotyping. Tumor purity was determined using conventional pathology, digital pathology, and two bioinformatic methods. HRD scores were calculated from copy number profiles determined by Sequenza and by Sclust either with or without fixed tumor purity. Tumor purity determination by digital pathology combined with a tumory purity informed variant of Sequenza served as reference method for HRD scoring. Seven tumors had deleterious mutations in BRCA1/2, 12 tumors had deleterious mutations in other homologous recombination repair (HRR) genes, 18 tumors had variants of unknown significance (VUS) in BRCA1/2 or other HRR genes, while the remaining 63 tumors had no relevant alterations. Using the reference method for HRD scoring, 68 tumors were HRD-positive. HRDsum determined by WES correlated strongly with HRDsum determined by single nucleotide polymorphism (SNP) arrays (R = 0.85). Conventional pathology systematically overestimated tumor purity by 8% compared to digital pathology. All investigated methods agreed on classifying the deleterious BRCA1/2-mutated tumors as HRD-positive, but discrepancies were observed for some of the remaining tumors. Discordant HRD classification of 11% of the tumors was observed comparing the tumor purity uninformed default of Sequenza and the reference method. In conclusion, tumor purity is a critical factor for the determination of HRD scores. Assistance by digital pathology helps to improve accuracy and imprecision of its estimation.

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Radiotherapy is the main treatment for cervical cancer. It is usually applied alone or in combination with surgery and/or chemotherapy. To explore the association between immune microenvironment of cervical cancer and radiotherapy response, we collected 20 paired cervical cancer tumor samples before and after radiotherapy and partial clinical information. With paired-end RNA-seq, we quantified the immune infiltration and tumor purity of these samples, and obtained 6350 differentially expressed genes before and after radiotherapy. With the help of R language, the function enrichment analysis and 22 immune cells infiltration analysis were carried out. Moreover, we built a random forest model based on the immune microenvironment to predict the short-term efficacy of radiotherapy. We found that the effect of radiotherapy on the immune microenvironment of stage III and IV cervical cancer patients was weaker than that of stage I and II cervical cancer patients. Radiotherapy can significantly reduce the tumor purity and increase immune infiltration. The proportions of the immune infiltrating cells are predictive of the radiotherapy efficacy. In addition, the local mucositis caused by radiotherapy can improve the curative effect of radiotherapy (AU)

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Background: The signal transducer and activator of transcription (STAT) gene family have been widely found to regulate cell proliferation, differentiation, apoptosis, and angiogenesis through complex signaling pathways, and thus impacting tumor formation and development in different types of tumor. However, the roles of STATs on prognostic prediction and therapeutic guidance in pan-cancer remain unexplored. Materials and Methods: The dataset of 33 types of TCGA tumor, para-carcinoma and normal tissues, was obtained from the UCSC Xena database, including the gene expression profiles in the formats of FPKM value, demographic characteristics, clinical information, and survival data of STATs. Differential expression and co-expression analyses, WGCNA, clinical relevance analysis, immune subtype analysis, tumor stemness analysis, tumor purity analysis, immune infiltration analysis, immunotherapy related analysis, tumor mutation related analysis, and drug sensitivity analysis were performed by R software. Results: Differential expression of STAT1 was found between normal and BRCA tissues (p < 0.001, log2FC = 0.895). Additionally, the strongest correlation among STATs lied between STAT1 and STAT2 (correlation coefficient = 0.6). Moreover, high expression levels of STAT1 (p = 0.031) were revealed to be notably correlated with poor prognosis in KIRP. In addition, STAT1 expressed the highest value in immune subtypes C1, C2, C3, and C6 in LUAD. What's more, strong negative correlations were demonstrated between expression of STAT6 and mDNAss and mRNAss of TGCT. Additionally, STAT4 expression was characterized to be significantly negatively correlated with tumor purity of the majority of cancer types. Moreover, STAT1 and STAT3 were shown to be generally high-expressed in pan-cancer myeloid cells, and STATs all had positive correlation with the infiltration of the majority of immune cells. In addition, STATs were revealed to be closely linked with immunotherapy response. What's more, STAT4 expression was identified to have a strong negative correlation with TMB value in DLBC. Last but not least, positive correlations were accessed between STAT5 and sensitivity of Nelarabine (cor = 0.600, p < 0.001). Conclusion: In the present study, we identified STATs as biomarkers for prognostic prediction and therapeutic guidance in pan-cancer. Hopefully our findings could provide a valuable reference for future STATs research and clinical applications.

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This study develops a method combining a convolutional neural network model, INSIGHT, with a self-attention model, WiseMSI, to predict microsatellite instability (MSI) based on the tiles in colorectal cancer patients from a multicenter Chinese cohort. After INSIGHT differentiates tumor tiles from normal tissue tiles in a whole slide image, features of tumor tiles are extracted with a ResNet model pre-trained on ImageNet. Attention-based pooling is adopted to aggregate tile-level features into slide-level representation. INSIGHT has an area under the curve (AUC) of 0.985 for tumor patch classification. The Spearman correlation coefficient of tumor cell fraction given by expert pathologist and INSIGHT is 0.7909. WiseMSI achieves a specificity of 94.7% (95% confidence interval [CI] 93.7%-95.7%), a sensitivity of 84.7% (95% CI 82.6%-86.9%), and an AUC of 0.954 (95% CI 0.948-0.960). Comparative analysis shows that this method has better performance than the other five classic deep learning methods.

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AIMS: This study was launched to identify the SHMT2 associated Human Cancer subtypes. BACKGROUND: Cancer is the 2nd leading cause of death worldwide. Previous reports revealed the limited involvement of SHMT2 in human cancer. In the current study, we comprehensively analyzed the role of SHMT2 in 24 major subtypes of human cancers using in silico approach and identified a few subtypes that are mainly associated with SHMT2. OBJECTIVE: We aim to comprehensively analyze the role of SHMT2 in 24 major subtypes of human cancers using in silico approach and identified a few subtypes that are mainly associated with SHMT2. Earlier, limited knowledge exists in the medical literature regarding the involvement of Serine Hydroxymethyltransferase 2 (SHMT2) in human cancer. METHODS: In the current study, we comprehensively analyzed the role of SHMT2 in 24 major subtypes of human cancers using in silico approach and identified a few subtypes that are mainly associated with SHMT2. Pan-cancer transcriptional expression profiling of SHMT2 was done using UALCAN while further validation was performed using GENT2. For translational profiling of SHMT2, we utilized Human Protein Atlas (HPA) platform. Promoter methylation, genetic alteration, and copy number variations (CNVs) profiles were analyzed through MEXPRESS and cBioPortal. Survival analysis was carried out through Kaplan-Meier (KM) plotter platform. Pathway enrichment analysis of SHMT2 was performed using DAVID, while the gene-drug network was drawn through CTD and Cytoscape. Furthermore, in the tumor microenvironment, a correlation between tumor purity, CD8+ T immune cells infiltration, and SHMT2 expression was accessed using TIMER. RESULTS: SHMT2 was found overexpressed in 24 different subtypes of human cancers and its overexpression was significantly associated with the reduced Overall survival (OS) and Relapse-free survival durations of Breast cancer (BRCA), Kidney renal papillary cell carcinoma (KIRP), Liver hepatocellular carcinoma (LIHC), and Lung adenocarcinoma (LUAD) patients. This implies that SHMT2 plays a significant role in the development and progression of these cancers. We further noticed that SHMT2 was also up-regulated in BRCA, KIRP, LIHC, and LUAD patients of different clinicopathological features. Pathways enrichment analysis revealed the involvement of SHMT2 enriched genes in five diverse pathways. Furthermore, we also explored some interesting correlations between SHMT2 expression and promoter methylation, genetic alterations, CNVs, tumor purity, and CD8+ T immune cell infiltrates. CONCLUSION: Our results suggested that overexpressed SHMT2 is correlated with the reduced OS and RFS of the BRCA, KIRP, LIHC, and LUAD patients and can be a potential diagnostic and prognostic biomarker for these cancers.

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Radiotherapy is the main treatment for cervical cancer. It is usually applied alone or in combination with surgery and/or chemotherapy. To explore the association between immune microenvironment of cervical cancer and radiotherapy response, we collected 20 paired cervical cancer tumor samples before and after radiotherapy and partial clinical information. With paired-end RNA-seq, we quantified the immune infiltration and tumor purity of these samples, and obtained 6350 differentially expressed genes before and after radiotherapy. With the help of R language, the function enrichment analysis and 22 immune cells infiltration analysis were carried out. Moreover, we built a random forest model based on the immune microenvironment to predict the short-term efficacy of radiotherapy. We found that the effect of radiotherapy on the immune microenvironment of stage III and IV cervical cancer patients was weaker than that of stage I and II cervical cancer patients. Radiotherapy can significantly reduce the tumor purity and increase immune infiltration. The proportions of the immune infiltrating cells are predictive of the radiotherapy efficacy. In addition, the local mucositis caused by radiotherapy can improve the curative effect of radiotherapy.

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High-grade gliomas (HGG) have high malignancy, high heterogeneity, and a poor prognosis. Tumor purity is an intrinsic feature of the HGG microenvironment and an independent prognostic factor. The purpose of this study was to analyze the correlation of tumor purity with clinicopathological, molecular, and imaging features. We performed a retrospective analysis of 112 patients diagnosed with HGG (grades III and IV) in our center. Eleven regions of interest (ROI) were randomly selected on whole-slide images (WSI, 40 × magnification) based on HGG tissue paraffin sections and hematoxylin-eosin (H&E) staining. Of these 11 ROIs, five ROIs were visually estimated by pathologists and six ROIs were automatically analyzed using ImageJ software. Last, the average tumor purity (%) of the 11 ROIs was calculated. Correlation analysis of tumor purity with clinicopathological, molecular, and imaging features was conducted. Of the 112 patients included in the study, the mean tumor purity of HGG was 70.96%. There were differences in tumor purity between WHO grades III and IV; the tumor purity of grade IV patients (67.59%) was lower than that of grade III patients (76.00%) (p < 0.001). There were also differences in tumor purity between IDH1 mutant and wild type, and the tumor purity of IDH1 mutant patients was higher than that of IDH1 wild-type patients (p = 0.006). The average range of peritumoral edema was about 19.18 mm, and the diameter of edema, ADCmean, and ADCmin were negatively correlated with tumor purity(r = - 0.236, r = - 0.306, and r = - 0.242; p < 0.05). The grade of HGG, IDH1 mutant/wild type, peritumoral edema, and ADC value were correlated with tumor purity. HGG grade, IDH1 mutant/wild type, peritumoral edema, and ADC value can predict tumor purity and indirectly reflect patient prognosis.

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BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the digestive tract with a poor prognosis. The tumor microenvironment (TME) is mainly composed of tumor cells, stromal cells, and immune cells and plays an important role in ESCC development. There are substantial differences in tumor purity among different parts of ESCC tissues, consisting of distinct immune and stromal cells and variations in the status of hypoxia. Thus, prognostic models of ESCC based on bioinformatic analysis of tumor tissues are unreliable. METHOD: Differentially expressed genes (DEGs) independent of tumor purity and hypoxia were screened by Spearman correlation analysis of public ESCC cohorts. Subsequently, the DEGs were subjected to Cox regression analysis. Then, we constructed a protein-protein interaction (PPI) network of the DEGs using Cytoscape. Intersection analysis of the univariate Cox and PPI results indicated that heparanase (HPSE), an endo-ß-D-glucuronidase capable of cleaving heparan sulfate side chains, was a predictive factor. Gene set enrichment analysis (GSEA) was used to reveal the potential function of HPSE, and single-cell sequencing data were analyzed to evaluate the distribution of HPSE in immune cells. Furthermore, a human ESCC tissue microarray was used to validate the expression and prognostic value of HPSE. RESULT: We found that HPSE was downregulated in ESCC tissues and was not correlated with tumor purity or hypoxia status. HPSE is involved in multiple biological processes. ESCC patients with low HPSE expression in cancerous tissues exhibited poor prognosis. CONCLUSIONS: These results indicate that low HPSE expression in cancerous tissues correlates with poor prognosis in patients with ESCC. HPSE is a novel prognostic biomarker independent of tumor purity and hypoxia status in ESCC.

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BACKGROUND: Estimating tumor purity is especially important in the age of precision medicine. Purity estimates have been shown to be critical for correction of tumor sequencing results, and higher purity samples allow for more accurate interpretations from next-generation sequencing results. Molecular-based purity estimates using computational approaches require sequencing of tumors, which is both time-consuming and expensive. METHODS: Here we propose an approach, weakly-supervised purity (wsPurity), which can accurately quantify tumor purity within a digitally captured hematoxylin and eosin (H&E) stained histological slide, using several types of cancer from The Cancer Genome Atlas (TCGA) as a proof-of-concept. FINDINGS: Our model predicts cancer type with high accuracy on unseen cancer slides from TCGA and shows promising generalizability to unseen data from an external cohort (F1-score of 0.83 for prostate adenocarcinoma). In addition we compare performance of our model on tumor purity prediction with a comparable fully-supervised approach on our TCGA held-out cohort and show our model has improved performance, as well as generalizability to unseen frozen slides (0.1543 MAE on an independent test cohort). In addition to tumor purity prediction, our approach identified high resolution tumor regions within a slide, and can also be used to stratify tumors into high and low tumor purity, using different cancer-dependent thresholds. INTERPRETATION: Overall, we demonstrate our deep learning model's different capabilities to analyze tumor H&E sections. We show our model is generalizable to unseen H&E stained slides from data from TCGA as well as data processed at Weill Cornell Medicine. FUNDING: Starr Cancer Consortium Grant (SCC I15-0027) to Iman Hajirasouliha.

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Tumor purity refers to the proportion of tumor cells in tumor tissue samples. This value plays an important role in understanding the mechanisms of the tumor microenvironment. Although various attempts have been made to predict tumor purity, attempts to predict tumor purity using miRNAs are still lacking. We predicted tumor purity using miRNA expression data for 16 TCGA tumor types using random forest regression. In addition, we identified miRNAs with high feature-importance scores and examined the extent of the change in predictive performance using informative miRNAs. The predictive performance obtained using only 10 miRNAs with high feature importance was close to the result obtained using all miRNAs. Furthermore, we also found genes targeted by miRNAs and confirmed that these genes were mainly related to immune and cancer pathways. Therefore, we found that the miRNA expression data could predict tumor purity well, and the results suggested the possibility that 10 miRNAs with high feature importance could be used as potential markers to predict tumor purity and to help improve our understanding of the tumor microenvironment.

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This study was initiated to explore the expression variation, clinical significance, and biological importance of the GINS complex subunit 4 (GINS4) in different human cancers as a shared biomarker via pan-cancer analysis through different platforms including UALCAN, Kaplan Meier (KM) plotter, TNMplot, GENT2, GEPIA, DriverDBv3, Human Protein Atlas (HPA), MEXPRESS, cBioportal, STRING, DAVID, MuTarge, Enrichr, TIMER, and CTD. Our findings have verified the up-regulation of GINS4 in 24 major subtypes of human cancers, and its overexpression was found to be substantially associated with poor overall survival (OS), relapse-free survival (RFs), and metastasis in ESCA, KIRC, LIHC, LUAD, and UCEC. This suggested that GINS4 plays a significant role in the development and progression of these five cancers. Furthermore, we noticed that GINS4 is also overexpressed in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics. Enrichment analysis revealed the involvement of GINS4 associated genes in a variety of diverse GO and KEGG terms. We also explored few significant correlations between GINS4 expression and promoter methylation, genetic alterations, CNVs, other mutant genes, tumor purity, and immune cells infiltration. In conclusion, our results elucidated that GINS4 can serve as a shared diagnostic, prognostic biomarker, and a potential therapeutic target in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics.