Most Variable Genes and Transcription Factors in Acute Lymphoblastic Leukemia Patients

Profiling of urinary extracellular vesicle protein signatures from patients with cribriform and intraductal prostate carcinoma in a cross-sectional study

Prognostic tests and treatment approaches for optimized clinical care of prostatic neoplasms are an unmet need. Prostate cancer (PCa) and derived extracellular vesicles (EVs) proteome changes occur during initiation and progression of the disease. PCa tissue proteome has been previously characterized, but screening of tissue samples constitutes an invasive procedure. Consequently, we focused this study on liquid biopsies, such as urine samples. More specifically, urinary small extracellular vesicle and particles proteome profiles of 100 subjects were analyzed using liquid chromatography coupled to high-resolution mass spectrometry (LC-MS/MS). We identified 171 proteins that were differentially expressed between intraductal prostate cancer/cribriform (IDC/Crib) and non-IDC/non-Crib after correction for multiple testing. However, the strong correlation between IDC/Crib and Gleason Grade complicates the disentanglement of the underlying factors driving this association. Nevertheless, even after accounting for multiple testing and adjusting for ISUP (International Society of Urological Pathology) grading, two proteins continued to exhibit significant differential expression between IDC/Crib and non-IDC/non-Crib. Functional enrichment analysis based on cancer hallmark proteins disclosed a clear pattern of androgen response down-regulation in urinary EVs from IDC/Crib compared to non-IDC/non-Crib. Interestingly, proteome differences between IDC and cribriform were more subtle, suggesting high proteome heterogeneity. Overall, the urinary EV proteome reflected partly the prostate pathology.

Identification of oxidative stress signatures of lung adenocarcinoma and prediction of patient prognosis or treatment response with single-cell RNA sequencing and bulk RNA sequencing data

Lung adenocarcinoma (LUAD), the most common subtype of lung cancer globally, has seen improved prognosis with advancements in diagnostic, surgical, radiotherapy, and molecular therapy techniques, while its 5-year survival rate remains low. Molecular biomarkers provide prognostic value. Oxidative stress factors, such as reactive nitrogen species and ROS, are crucial in various stages of tumor progression, influencing cell transformation, proliferation, angiogenesis, and metastasis. ROS demonstrate dual roles, affecting tumor cells, hypoxia sensitivity, and the microenvironment. Comprehensive analysis of oxidative stress in LUAD has not been conducted to date. Therefore, we systematically investigated the regulatory patterns of oxidative stress in LUAD based on oxidative stress-related genes and correlated these patterns with cellular infiltration characteristics of the tumor immune microenvironment. The model utilizes single-factor Cox analysis to screen key differential genes with prognostic value and employs least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis to construct a prognostic-related prediction model. Ten candidate genes were selected based on this model. The risk score was constructed using the coefficients and expression levels of these ten genes. Furthermore, the impact of this risk score on overall survival (OS) was determined. Two genes with the most significant differential expression, SFTPB and S100P, were selected through qRT-PCR. Cell experiments including CCK-8, Edu, transwell assays confirmed their effects on lung cancer cells growth, consistent with the results of bioinformatics analysis. These findings suggested that this model held potential clinical value for evaluating the prognosis of lung adenocarcinoma.

Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning

BCR-ABL1–positive acute leukemia can be classified into three disease categories: B-lymphoblastic leukemia (B-ALL), acute myeloid leukemia (AML), and mixed-phenotype acute leukemia (MPAL). We conducted an integrative analysis of RNA sequencing (RNA-seq) data obtained from 12 BCR-ABL1–positive B-ALL, AML, and MPAL samples to evaluate its diagnostic utility. RNA-seq facilitated the identification of all p190 BCR-ABL1 with accurate splicing sites and a new gene fusion involving MAP2K2. Most of the clinically significant mutations were also identified including single-nucleotide variations, insertions, and deletions. In addition, RNA-seq yielded differential gene expression profile according to the disease category. Therefore, we selected 368 genes differentially expressed between AML and B-ALL and developed two differential diagnosis models based on the gene expression data using 1) scoring algorithm and 2) machine learning. Both models showed an excellent diagnostic accuracy not only for our 12 BCR-ABL1–positive cases but also for 427 public gene expression datasets from acute leukemias regardless of specific genetic aberration. This is the first trial to develop models of differential diagnosis using RNA-seq, especially to evaluate the potential role of machine learning in identifying the disease category of acute leukemia. The integrative analysis of gene expression data by RNA-seq facilitates the accurate differential diagnosis of acute leukemia with successful detection of significant gene fusion and/or mutations, which warrants further investigation.

A signature of estimate-stromal-immune score-based genes associated with the prognosis of lung adenocarcinoma

Background

Immune and stromal component evaluation is necessary to establish accurate prognostic markers for the prediction of clinical outcomes in lung adenocarcinoma (LUAD). We aimed to develop a gene signature based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE)-stromal-immune score in LUAD.

Methods

The transcriptomic profiles of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA), and the immune and stromal scores were derived using the ESTIMATE algorithm. The prognostic signature genes were selected from the differentially expressed genes (DEGs) using the robust partial likelihood-based cox proportional hazards regression method. The negative log-likelihood and the Akaike Information Criterion (AIC) were used to identify the optimal gene signature. The validation was carried out in 2 independent datasets from the Gene Expression Omnibus (GSE68571 and GSE72094).

Results

Patients with high ESTIMATE, stromal, and immune scores had better overall survivals (P=0.0035, P=0.066, and P=0.0077). The expression of thirty-seven genes was related to ESTIMATE-stromal-immune score. A risk stratification model was developed based on a gene signature containing CD74, JCHAIN, and PTGDS. The ESTIMATE-stromal-immune risk score was revealed to be a prognostic factor (P=0.009) after multivariate analysis. Four groups were classified based on this risk stratification model, yielding increasing survival outcomes (log-rank test, P=0.0051). This risk stratification model and other clinicopathological factors were combined to generate a nomogram. The calibration curves showed perfect agreement between the nomogram-predicted outcomes and those actually observed. Similar observations were made in 2 independent cohorts.

Conclusions

The gene signature based on the ESTIMATE-stromal-immune score could predict the prognosis of patients with LUAD.

Mapping the amelogenin protein expression during porcine molar crown development

INTRODUCTION

Amelogenin (AMEL) plays critical roles during enamel and dentin matrix deposition and mineralization. Most studies focused on the expression patterns of AMEL through the bud, cap, and bell stages. The spatial-temporal expression of AMEL protein during different mineralization stages, especially from presence of crypts to crown completed stages, remains unknown. Thus, the distribution pattern of AMEL in tooth crown formation from Nolla Stage 1 to 6 was investigated.

METHODS

Porcine mandibular molar tooth germs from Nolla Stage 1 to 6 were obtained. The dynamic morphologic changes of tooth germs were examined by X-ray and surgical operating microscope. The AMEL protein expression was evaluated immunohistochemically, then analyzed semi-quantitatively, and further visualized via heat map.

RESULTS

Tooth germs continuously increased in size from Nolla Stage 1 to 6. AMEL expression in the newly formed enamel kept negative, but presented intensively positive in the previously formed enamel from Stage 1 to 3. The adjacent enamel-dentin junction (EDJ) was strongly positive during the whole process. In predentin, AMEL was weakly seen at Stage 1 and then dramatically up-regulated from Stage 2 to Stage 3, then down-regulated but was still apparently seen in the whole process. AMEL expression in dentin was decreased during dentin matrix secretion and mineralization.

CONCLUSIONS

This study identified the dynamic distribution of AMEL during porcine tooth crown formation. Semi-quantitative analysis and heat map emerged as reliable indicators in demonstrating AMEL distribution pattern.

Bioinformatics Approaches for Anti-cancer Drug Discovery

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

Identification of Tumor Microenvironment-Related Prognostic Biomarkers in Luminal Breast Cancer

Background: The tumor microenvironment (TME) has been reported to have significant value in the diagnosis and prognosis of cancers. This study aimed to identify key biomarkers in the TME of luminal breast cancer (BC).

Methods: We obtained immune scores (ISs) and stromal scores (SSs) for The Cancer Genome Atlas (TCGA) luminal BC cohort from the online ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) portal. The relationships between ISs and SSs and the overall survival of luminal BC patients were assessed by the Kaplan-Meier method. The differentially expressed messenger RNAs (DEmRNAs) related to the ISs and SSs were subjected to functional enrichment analysis. Additionally, a competing endogenous RNA (ceRNA) network was constructed with differentially expressed microRNAs (DEmiRNAs) and long noncoding RNAs (DElncRNAs). Furthermore, a protein–protein interaction (PPI) network was established to analyze the DEmRNAs in the ceRNA network. Then, survival analysis of biomarkers involved in the ceRNA network was carried out to explore their prognostic value. Finally, these biomarkers were validated using the luminal BC dataset from the Gene Expression Omnibus (GEO) database.

Results: The results showed that ISs were significantly associated with longer survival times of luminal BC patients. Functional enrichment analysis showed that the DEmRNAs were mainly associated with immune response, antigen binding, and the extracellular region. In the PPI network, the top 10 DEmRNAs were identified as hub genes that affected the TME of luminal BC. Finally, two DEmiRNAs, two DElncRNAs, and 17 DEmRNAs of the ceRNA network associated with the TME were shown to have prognostic value. Subsequently, the expression of 15 prognostic biomarkers was validated in one additional dataset (GSE81002). In particular, one lncRNA (GVINP1) and five mRNAs (CCDC69, DOCK2, IKZF1, JCHAIN, and NCKAP1L) were novel biomarkers.

Conclusions: Our studies demonstrated that ISs were associated with the survival of luminal BC patients, and a set of novel biomarkers that might play a prognostic role in the TME of luminal BC was identified.