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Background: Acute Myeloid Leukemia (AML) is a hematological cancer characterized by heterogeneous hematopoietic cells. Through the use of multidimensional sequencing technologies, we previously identified a distinct myeloblast population, CD34+CD117dim, the proportion of which was strongly associated with the clinical outcome in t (8;21) AML. In this study, we explored the potential value of the CD34+CD117dim population signature (117DPS) in AML stratification.Methods: Based on the CD34+CD117dim gene signature, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct the 117DPS model using the gene expression data from Gene Expression Omnibus (GEO) database (GSE37642-GPL96 was used as training cohort; GSE37642-GPL570, GSE12417-GPL96, GSE12417-GPL570 and GSE106291 were used as validation cohorts). In addition, the RNA-seq data from The Cancer Genome Atlas (TCGA)-LAML and Beat AML projects of de-novo AML patients were also analyzed as validation cohorts. The differences of clinical features and tumor-infiltrating lymphocytes were further explored between the high-risk score group and low-risk score group.Results: The high-risk group of the 117DPS model exhibited worse overall survival than the low-risk group in both training and validation cohorts. Immune signaling pathways were significantly activated in the high-risk group. Patients with high-risk score had a distinct pattern of infiltrating immune cells, which were closely related to clinical outcome.Conclusion: The 117DPS model established in our study may serve as a potentially valuable tool for predicting clinical outcome of patients with AML. [ABSTRACT FROM AUTHOR]

Background: The identification of novel functional biomarkers is essential for recognizing high-risk patients, predicting recurrence, and searching for appropriate treatment. However, no prognostic biomarker has been applied for colorectal cancer (CRC) in the clinic.Methods: Integrated with transcriptomic data from public databases, multi-omics examinations were conducted to search prognostic biomarkers for CRC. Moreover, the potential biological functions and regulatory mechanism of these predictive genes were also explored.Results: In this study, we revealed that three mitochondrial genes were associated with the poor prognosis of CRC. Integrated analyses of transcriptome and proteome of CRC patients disclosed numerous down-regulated mitochondrial genes at both mRNA and protein levels, suggesting a vital role of mitochondria in carcinogenesis. Combined with the bioinformatics studies of transcriptomic datasets of 538 CRC patients, three mitochondrial prognostic genes were eventually selected out, including HIGD1A, SUCLG2, and SLC25A24. The expression of HIGD1A exhibited a significant reduction in two subtypes of adenoma and six subtypes of CRC, while the down-regulation of SUCLG2 and SLC25A24 showed more advantages in rectal mucinous adenocarcinoma. Moreover, we unveiled that these three genes had common expressions and might collaboratively participate in the synthesis of ribosomes. Our original multi-omics datasets, including DNA methylation, structural variants, chromatin accessibility, and phosphoproteome, further depicted the altered modifications on their potential transcriptional factors.Conclusions: In summary, HIGD1A, SUCLG2, and SLC25A24 might serve as predictive biomarkers for CRC. The biological activities they involved in and their upstream regulators we uncovered would provide a functional context for the further-in-depth mechanism study. [ABSTRACT FROM AUTHOR]

Background: Tissue-resident memory T (TRM) cells can reside in the tumor microenvironment and are considered the primary response cells to immunotherapy. Heterogeneity in functional status and spatial distribution may contribute to the controversial role of TRM cells but we know little about it. Methods: Through multiplex immunofluorescence (mIF) (CD8, CD103, PD-1, Tim-3, GZMB, CK), the quantity and spatial location of TRM cell subsets were recognized in the tissue from 274 patients with NSCLC after radical surgery. By integrating multiple machine learning methods, we constructed a TRM-based spatial immune signature (TRM-SIS) to predict the prognosis. Furthermore, we conducted a CD103-related gene set enrichment analysis (GSEA) and verified its finding by another mIF panel (CD8, CD103, CK, CD31, Hif-1α). Results: The density of TRM cells was significantly correlated with the expression of PD-1, Tim-3 and GZMB. Four types of TRM cell subsets was defined, including TRM1 (PD-1−Tim-3−TRM), TRM2 (PD-1+Tim-3−TRM), TRM3 (PD-1−Tim-3+TRM) and TRM4 (PD-1+Tim-3+TRM). The cytotoxicity of TRM2 was the strongest while that of TRM4 was the weakest. Compare with TRM1 and TRM2, TRM3 and TRM4 had better infiltration and stronger interaction with cancer cells. The TRM-SIS was an independent prognostic factor for disease-free survival [HR = 2.43, 95%CI (1.63–3.60), P < 0.001] and showed a better performance than the TNM staging system for recurrence prediction. Furthermore, by CD103-related GSEA and mIF validation, we found a negative association between tumor angiogenesis and infiltration of TRM cells. Conclusions: These findings reveal a significant heterogeneity in the functional status and spatial distribution of TRM cells, and support it as a biomarker for the prognosis of NSCLC patients. Regulating TRM cells by targeting tumor angiogenesis may be a potential strategy to improve current immunotherapy. [ABSTRACT FROM AUTHOR]

Background: Patients with critical illness due to infection with the 2019 coronavirus disease (COVID-19) show rapid disease progression to acute respiratory failure. The study aimed to screen the most useful predictive factor for critical illness caused by COVID-19.Methods: The study prospectively involved 61 patients with COVID-19 infection as a derivation cohort, and 54 patients as a validation cohort. The predictive factor for critical illness was selected using LASSO regression analysis. A nomogram based on non-specific laboratory indicators was built to predict the probability of critical illness.Results: The neutrophil-to-lymphocyte ratio (NLR) was identified as an independent risk factor for critical illness in patients with COVID-19 infection. The NLR had an area under receiver operating characteristic of 0.849 (95% confidence interval [CI], 0.707 to 0.991) in the derivation cohort and 0.867 (95% CI 0.747 to 0.944) in the validation cohort, the calibration curves fitted well, and the decision and clinical impact curves showed that the NLR had high standardized net benefit. In addition, the incidence of critical illness was 9.1% (1/11) for patients aged ≥ 50 and having an NLR < 3.13, and 50% (7/14) patients with age ≥ 50 and NLR ≥ 3.13 were predicted to develop critical illness. Based on the risk stratification of NLR according to age, this study has developed a COVID-19 pneumonia management process.Conclusions: We found that NLR is a predictive factor for early-stage prediction of patients infected with COVID-19 who are likely to develop critical illness. Patients aged ≥ 50 and having an NLR ≥ 3.13 are predicted to develop critical illness, and they should thus have rapid access to an intensive care unit if necessary. [ABSTRACT FROM AUTHOR]

Background: As the most common form of lymphoma, diffuse large B-cell lymphoma (DLBCL) is a clinical highly heterogeneous disease with variability in therapeutic outcomes and biological features. It is a challenge to identify of clinically meaningful tools for outcome prediction. In this study, we developed a prognosis model fused clinical characteristics with drug resistance pharmacogenomic signature to identify DLBCL prognostic subgroups for CHOP-based treatment.Methods: The expression microarray data and clinical characteristics of 791 DLBCL patients from two Gene Expression Omnibus (GEO) databases were used to establish and validate this model. By using univariate Cox regression, eight clinical or genetic signatures were analyzed. The elastic net-regulated Cox regression analysis was used to select the best prognosis related factors into the predictive model. To estimate the prognostic capability of the model, Kaplan-Meier curve and the area under receiver operating characteristic (ROC) curve (AUC) were performed.Results: A predictive model comprising 4 clinical factors and 2 pharmacogenomic gene signatures was established after 1000 times cross validation in the training dataset. The AUC of the comprehensive risk model was 0.78, whereas AUC value was lower for the clinical only model (0.68) or the gene only model (0.67). Compared with low-risk patients, the overall survival (OS) of DLBCL patients with high-risk scores was significantly decreased (HR = 4.55, 95% CI 3.14-6.59, log-rank p value = 1.06 × 10-15). The signature also enables to predict prognosis within different molecular subtypes of DLBCL. The reliability of the integrated model was confirmed by independent validation dataset (HR = 3.47, 95% CI 2.42-4.97, log rank p value = 1.53 × 10-11).Conclusions: This integrated model has a better predictive capability to ascertain the prognosis of DLBCL patients prior to CHOP-like treatment, which may improve the clinical management of DLBCL patients and provide theoretical basis for individualized treatment. [ABSTRACT FROM AUTHOR]

Background: Leukemia stem cell (LSC)-enriched genes have been shown to be highly prognostic in acute myeloid leukemia (AML). However, the prognostic value of tumor suppressor genes (TSGs) that are repressed early in LSC remains largely unknown.Methods: We compared the public available expression/methylation profiling data of LSCs with that of hematopoietic stem cells (HSCs), in order to identify potential tumor suppressor genes in LSC. The prognostic relevance of PCDH17 was analyzed on a cohort of 173 AML patients from The Cancer Genome Atlas (TCGA), and further validated in three independent cohorts (n = 339).Results: We identified protocadherin17 (PCDH17) and demonstrated that it was significantly down-regulated and hypermethylated in LSCs compared with HSCs. Our analyses of primary AML patient samples also confirmed these deregulations. Clinically, low PCDH17 expression was associated with female sex (P = 0.01), higher WBC (P < 0.0001), higher percentages of blasts in bone marrow (BM) and peripheral blood (PB) (P = 0.04 and < 0.001, respectively), presence of FLT3-internal tandem duplications (P = 0.002), mutated NPM1 (P = 0.02), and wild-type TP53 (P = 0.005). Moreover, low PCDH17 expression predicted worse overall survival (OS) in four independent cohorts as well as in the molecularly defined subgroups of AML patients. In multivariable analyses, low PCDH17 expression retained independent prognostic value for OS. Biologically, PCDH17 expression-associated gene signatures were characterized by deregulations of EMT- and Wnt pathway-related genes.Conclusions: PCDH17 gene was silenced by DNA methylation in AML. Low PCDH17 expression is associated with distinct clinical and biological features and improves risk stratification in patients with AML. [ABSTRACT FROM AUTHOR]

Background: Deregulated inflammation and immune deficit both intricately associate with cancer initiation and progression, which have been increasingly exploited as prognostic biomarkers and therapeutic targets. Recently, systemic immune-inflammation index (SII) based on peripheral neutrophil, lymphocyte and platelet counts serves as a novel and powerful cancer biomarker with prognostic significance in multiple types of malignancies. Here, we sought to evaluate the prognostic value of preoperative SII in patients with primary oral squamous cell carcinoma (OSCC) after curative resection.Methods: Two independent cohorts with a total number of 309 patients with OSCC from two tertiary referral hospitals were included and defined as training (Nanjing, 138) and validation (Wuxi, 171) cohort, respectively. Preoperative SII in both cohorts was calculated and its optimal cutoff value was initially determined by X-tile software in the training cohort and then verified in the validation cohort.Results: Our data indicated that high SII (≥ 484.5) was significantly associated with larger tumor size (P < 0.05, Chi square test), reduced overall and disease-free survival (Kaplan-Meir, P < 0.05, Log-rank test). Univariate and multivariate analyses further revealed that SII was an independent prognostic predictor for patient survival. Moreover, the area under receiver operating characteristic curve of SII for survival was significantly greater or comparable to other well-established prognostic parameters, indicative of its satisfactory prediction accuracy and specificity.Conclusions: Our findings reveal that high preoperative SII associates with poor outcome and serves as a non-invasive, low-cost and powerful prognostic predictor for patients with OSCC. This inflammation/immune-related biomarker holds translational potentials to supplement currently prognostic regime to better stratification of patients and treatment planning. [ABSTRACT FROM AUTHOR]

Background: Increasing studies showed that miR-200 family (miR-200s) clusters are aberrantly expressed in multiple human cancers, and miR-200s clusters function as tumor suppressor genes by affecting cell proliferation, self-renewal, differentiation, division and apoptosis. Herein, we aimed to investigate the expression and clinical implication of miR-200s clusters in acute myeloid leukemia (AML).Methods: RT-qPCR was performed to detect expression of miR-200s clusters in 19 healthy donors, 98 newly diagnosed AML patients, and 35 AML patients achieved complete remission (CR).Results: Expression of miR-200a/200b/429 cluster but not miR-200c/141 cluster was decreased in newly diagnosed AML patients as compared to healthy donors and AML patients achieved CR. Although no significant differences were observed between miR-200s clusters and most of the features, low expression of miR-200s clusters seems to be associated with higher white blood cells especially for miR-200a/200b. Of the five members of miR-200s clusters, low expression of miR-200b/429/200c was found to be associated with lower CR rate. Logistic regression analysis further revealed that low expression of miR-429 acted as an independent risk factor for CR in AML. Based on Kaplan-Meier analysis, low expression of miR-200b/429/200c was associated with shorter OS, whereas miR-200a/141 had a trend. Moreover, multivariate analysis of Cox regression models confirmed the independently prognostic value of miR-200b expression for OS in AML.Conclusions: Expression of miR-200a/200b/429 cluster was frequently down-regulated in AML, and low expression of miR-429 as an independent risk factor for CR, whereas low expression of miR-200b as an independent prognostic biomarker for OS. [ABSTRACT FROM AUTHOR]