A CpG Methylation Classifier to Predict Relapse in Adults with T-Cell Lymphoblastic Lymphoma

Lack of SMARCB1 expression characterizes a subset of human and murine peripheral T-cell lymphomas

Peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS) is a heterogeneous group of malignancies with poor outcome. Here, we identify a subgroup, PTCL-NOSSMARCB1-, which is characterized by the lack of the SMARCB1 protein and occurs more frequently in young patients. Human and murine PTCL-NOSSMARCB1- show similar DNA methylation profiles, with hypermethylation of T-cell-related genes and hypomethylation of genes involved in myeloid development. Single-cell analyses of human and murine tumors revealed a rich and complex network of interactions between tumor cells and an immunosuppressive and exhausted tumor microenvironment (TME). In a drug screen, we identified histone deacetylase inhibitors (HDACi) as a class of drugs effective against PTCL-NOSSmarcb1-. In vivo treatment of mouse tumors with SAHA, a pan-HDACi, triggered remodeling of the TME, promoting replenishment of lymphoid compartments and reversal of the exhaustion phenotype. These results provide a rationale for further exploration of HDACi combination therapies targeting PTCL-NOSSMARCB1- within the TME.

Targeting P21-activated kinase suppresses proliferation and enhances chemosensitivity in T-cell lymphoblastic lymphoma

T-cell lymphoblastic lymphoma (T-LBL) is a highly aggressive non-Hodgkin lymphoma with a poor prognosis. P21-activated kinase (PAK) is a component of the gene expression-based classifier that can predict the prognosis of T-LBL. However, the role of PAK in T-LBL progression and survival remains poorly understood. Herein, we found that the expression of PAK1 was significantly higher in T-LBL cell lines (Jurkat, SUP-T1, and CCRF-CEM) compared to the human T-lymphoid cell line. Moreover, PAK2 mRNA level of 32 relapsed T-LBL patients was significantly higher than that of 37 cases without relapse (P = .012). T-LBL patients with high PAK1 and PAK2 expression had significantly shorter median RFS than those with low PAK1 and PAK2 expression (PAK1, P = .028; PAK2, P = .027; PAK1/2, P = .032). PAK inhibitors, PF3758309 (PF) and FRAX597, could suppress the proliferation of T-LBL cells by blocking the G1/S cell cycle phase transition. Besides, PF could enhance the chemosensitivity to doxorubicin in vitro and in vivo. Mechanistically, through western blotting and RNA sequencing, we identified that PF could inhibit the phosphorylation of PAK1/2 and downregulate the expression of cyclin D1, NF-κB and cell adhesion signaling pathways in T-LBL cell lines. These findings suggest that PAK might be associated with T-LBL recurrence and further found that PAK inhibitors could suppress proliferation and enhance chemosensitivity of T-LBL cells treated with doxorubicin. Collectively, our present study underscores the potential therapeutic effect of inhibiting PAK in T-LBL therapy.

Comprehensive analysis of basement membrane and immune checkpoint related lncRNA and its prognostic value in hepatocellular carcinoma via machine learning

Background

Hepatocellular carcinoma (HCC), which is characterized by its high malignancy, generally exhibits poor response to immunotherapy. As part of the tumor microenvironment, basement membranes (BMs) are involved in tumor development and immune activities. Presently, there is no integrated analysis linking the basement membrane with immune checkpoints, especially from the perspective of lncRNA.

Methods

Based on transcriptome data from The Cancer Genome Atlas, BMs-related and immune checkpoint-related lncRNAs were identified. By applying univariable Cox regression and Machine learning (LASSO and SVM-RFE algorithm), a 10-lncRNA prognosis signature was constructed. The prognostic significance of this signature was assessed by survival analysis. GSEA, ssGSEA, and drug sensitivity analysis were conducted to investigate potential functional pathways, immune status, and clinical implications of guiding individual treatments in HCC. Finally, the promoting migration effect of LINC01224 was validated via in vitro experiments.

Results

The multiple Cox regression, receiver operating characteristic curves, and stratified survival analysis of clinical subgroups exhibited the robust prognostic ability of the lncRNA signature. Results of the GSEA and drug sensitivity analysis revealed significant differences in potential functional pathways and response to drugs between the two risk groups. In addition, the risk level of HCC patients was distinctly correlated with immune cell infiltration status. More importantly, LINC01224 was independently associated with the OS of HCC patients (P < 0.05), suppressing the expression of LINC01224 inhibited the migration of HCC cells.

Conclusion

This study developed a reliable signature for the prognosis of HCC based on BM and immune checkpoint related lncRNA, revealing that LINC01224 might be a prognostic biomarker for HCC associated with the progression of HCC.

Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms

Background

COVID-19, a serious respiratory disease that has the potential to affect numerous organs, is a serious threat to the health of people around the world. The objective of this article is to investigate the potential biological targets and mechanisms by which SARS-CoV-2 affects benign prostatic hyperplasia (BPH) and related symptoms.

Methods

We downloaded the COVID-19 datasets (GSE157103 and GSE166253) and the BPH datasets (GSE7307 and GSE132714) from the Gene Expression Omnibus (GEO) database. In GSE157103 and GSE7307, differentially expressed genes (DEGs) were found using the "Limma" package, and the intersection was utilized to obtain common DEGs. Further analyses followed, including those using Protein-Protein Interaction (PPI), Gene Ontology (GO) function enrichment analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Potential hub genes were screened using three machine learning methods, and they were later verified using GSE132714 and GSE166253. The CIBERSORT analysis and the identification of transcription factors, miRNAs, and drugs as candidates were among the subsequent analyses.

Results

We identified 97 common DEGs from GSE157103 and GSE7307. According to the GO and KEGG analyses, the primary gene enrichment pathways were immune-related pathways. Machine learning methods were used to identify five hub genes (BIRC5, DNAJC4, DTL, LILRB2, and NDC80). They had good diagnostic properties in the training sets and were validated in the validation sets. According to CIBERSORT analysis, hub genes were closely related to CD4 memory activated of T cells, T cells regulatory and NK cells activated. The top 10 drug candidates (lucanthone, phytoestrogens, etoposide, dasatinib, piroxicam, pyrvinium, rapamycin, niclosamide, genistein, and testosterone) will also be evaluated by the P value, which is expected to be helpful for the treatment of COVID-19-infected patients with BPH.

Conclusion

Our findings reveal common signaling pathways, possible biological targets, and promising small molecule drugs for BPH and COVID-19. This is crucial to understand the potential common pathogenic and susceptibility pathways between them.

The β-catenin-LINC00183-miR-371b-5p-Smad2/LEF1 axis promotes adult T-cell lymphoblastic lymphoma progression and chemoresistance

BACKGROUND

High-intensity chemotherapy regimens are often used in adult T-cell lymphoblastic lymphoma (T-LBL) patients. Nevertheless, the response rate remains unsatisfactory due to emergence of chemoresistance. Growing evidence has shown that long non-coding RNAs (lncRNAs) are involved in tumor progression and chemoresistance. Herein, we investigated the potential role of lncRNAs in T-LBLs.

METHODS

RNAseq was used to screen and identify candidate lncRNAs associated with T-LBL progression and chemoresistance. Luciferase reporter assay was used to examine the binding of miR-371b-5p to the 3'UTR of Smad2 and LEF1, and the binding of TCF-4/LEF1 to the promoter of LINC00183. Chromatin immunoprecipitation assay was undertaken to analyze the connection between LEF1 and the LINC00183 promoter region. RNA immunoprecipitation assays were used to explore the mechanism whereby LINC00183 regulated miR-371b-5p. MTT and flow cytometry assays were used to measure apoptosis of T-LBL cells.

RESULTS

LINC00183 was upregulated in T-LBL progression and chemoresistant tissues in both the Sun Yat-sen University Cancer Center dataset and the First Affiliated Hospital of Anhui Medical University dataset. High expression of LINC00183 was correlated with poorer overall survival and progression-free survival of T-LBL patients compared to those with low expression of LINC00183. Furthermore, miR-371b-5p was negatively regulated by LINC00183. In vivo and in vitro assays showed that LINC00183-mediated T-LBL chemoresistance depended on miR-371b-5p expression. The direct binding of miR-371b-5p to Smad2 and LEF1 was verified by luciferase assays. It was shown that TCF4/LEF1 could bind to the LINC00183 promoter site and increase its transcript level. Downregulation of miR-371b-5p led to increased expression of Smad2/LEF1, and in turn increased LINC00183 expression. Additionally, phospho-Smad2 promotes nuclear translocation of β-catenin, LINC00183 downregulation decreased chemoresistance induced by β-catenin and TGF-β1 in T-LBL cells.

CONCLUSION

We unraveled a β-catenin-LINC00183-miR-371b-5p-Smad2/LEF1 feedback loop that promotes T-LBL progression and chemoresistance, indicating that LINC00183 may serve as a potential therapeutic target in T-LBLs.

Prediction of Genetic Alterations in Oncogenic Signaling Pathways in Squamous Cell Carcinoma of the Head and Neck: Radiogenomic Analysis Based on Computed Tomography Images

OBJECTIVE

This study investigated the role of radiomics in evaluating the alterations of oncogenic signaling pathways in head and neck cancer.

METHODS

Radiomics features were extracted from 106 enhanced computed tomography images with head and neck squamous cell carcinoma. Support vector machine-recursive feature elimination was used for feature selection. Support vector machine algorithm was used to develop radiomics scores to predict genetic alterations in oncogenic signaling pathways. The performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

The alterations of the Cell Cycle, HIPPO, NOTCH, PI3K, RTK RAS, and TP53 signaling pathways were predicted by radiomics scores. The AUC values of the training cohort were 0.94, 0.91, 0.94, 0.93, 0.87, and 0.93, respectively. The AUC values of the validation cohort were all greater than 0.7.

CONCLUSIONS

Radiogenomics is a new method for noninvasive acquisition of tumor molecular information at the genetic level.

Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ

BACKGROUND

The molecular biomarkers of breast ductal carcinoma in situ (DCIS) have important guiding significance for individualized precision treatment. This study was intended to explore the significance of radiomics based on ultrasound images to predict the expression of molecular biomarkers of mass type of DCIS.

METHODS

116 patients with mass type of DCIS were included in this retrospective study. The radiomics features were extracted based on ultrasound images. According to the ratio of 7:3, the data sets of molecular biomarkers were split into training set and test set. The radiomics models were developed to predict the expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), Ki67, p16, and p53 by using combination of multiple feature selection and classifiers. The predictive performance of the models were evaluated using the area under the curve (AUC) of the receiver operating curve.

RESULTS

The investigators extracted 5234 radiomics features from ultrasound images. 12, 23, 41, 51, 31 and 23 features were important for constructing the models. The radiomics scores were significantly (P < 0.05) in each molecular marker expression of mass type of DCIS. The radiomics models showed predictive performance with AUC greater than 0.7 in the training set and test set: ER (0.94 and 0.84), PR (0.90 and 0.78), HER2 (0.94 and 0.74), Ki67 (0.95 and 0.86), p16 (0.96 and 0.78), and p53 (0.95 and 0.74), respectively.

CONCLUSION

Ultrasonic-based radiomics analysis provided a noninvasive preoperative method for predicting the expression of molecular markers of mass type of DCIS with good accuracy.

A composite single-nucleotide polymorphism prediction signature for extranodal natural killer/T-cell lymphoma

Current prognostic scoring systems based on clinicopathologic variables are inadequate in predicting the survival and treatment response of extranodal natural killer/T-cell lymphoma (ENKTL) patients undergoing non-anthracyline-based treatment. We aimed to construct a classifier based on single-nucleotide polymorphisms (SNPs) for improving predictive accuracy and guiding clinical decision-making. The data of 722 patients with ENKTL from international multicenters were analyzed. A 7-SNP-based classifier was constructed using LASSO Cox regression in the training cohort (n=336) and further validated in the internal testing (n=144) and two external validation cohorts (n=142; n=100). The 7-SNP-based classifier showed good prognostic predictive efficacy in the training cohort and the three validation cohorts. Patients with high and low risk scores calculated by the classifier exhibited significantly different progression-free survival (PFS) and overall survival (OS) (all p<0.001). The 7-SNP-based classifier was further proved to be an independent prognostic factor by multivariate analysis, and its predictive accuracy was significantly better than clinicopathological risk variables. The application of the 7-SNP-based classifier was not affected by sample types. Notably, chemotherapy combined with radiotherapy significnalty improved PFS and OS versus radiotherapy alone in high risk Ann Anbor stage I patients, while there was no statistical difference between the two therapeutic modalities among low risk patients. A nomogram was constructed comprised of the classifier and clinicopathological variables, and showed remarkably better predictive accuracy than that of each variable alone. The 7-SNP-based classifier is a complement to existing risk stratification systems in ENKTL, which could have significant implications for clinical decision-making for ENKTL patients.

Females and Males Show Differences in Early-Stage Transcriptomic Biomarkers of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

The incidence and mortality rates of lung cancers are different between females and males. Therefore, sex information should be an important part of how to train and optimize a diagnostic model. However, most of the existing studies do not fully utilize this information. This study carried out a comparative investigation between sex-specific models and sex-independent models. Three feature selection algorithms and five classifiers were utilized to evaluate the contribution of the sex information to the detection of early-stage lung cancers. Both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) showed that the sex-specific models outperformed the sex-independent detection of early-stage lung cancers. The Venn plots suggested that females and males shared only a few transcriptomic biomarkers of early-stage lung cancers. Our experimental data suggested that sex information should be included in optimizing disease diagnosis models.