Single-cell transcriptome analysis profiling lymphatic invasion-related TME in colorectal cancer.

Lymphatic invasion (LI) is extremely aggressive and induces worse prognosis among patients with colorectal cancer (CRC). Thus, it is critical to characterize the cellular and molecular mechanisms underlying LI in order to establish novel and efficacious therapeutic targets that enhance the prognosis of CRC patients. RNA-seq data, clinical and survival information of colon adenocarcinoma (COAD) patients were obtained from the TCGA database. In addition, three scRNA-seq datasets of CRC patients were acquired from the GEO database. Data analyses were conducted with the R packages. We assessed the tumor microenvironment (TME) differences between LI+ and LI- based scRNA-seq data, LI+ cells exhibited augmented abundance of immunosuppression and invasive subset. Marked extracellular matrix network activation was also observed in LI+ cells within SPP1+ macrophages. We revealed that an immunosuppressive and pro-angiogenic TME strongly enhanced LI, as was evidenced by the CD4+ Tregs, CD8+ GZMK+, SPP1+ macrophages, e-myCAFs, and w-myCAFs subcluster infiltrations. Furthermore, we identified potential LI targets that influenced tumor development, metastasis, and immunotherapeutic response. Finally, a novel LIRS model was established based on the expression of 14 LI-related signatures, and in the two testing cohorts, LIRS was also proved to have accurate prognostic predictive ability. In this report, we provided a valuable resource and extensive insights into the LI of CRC. Our conclusions can potentially benefit the establishment of highly efficacious therapeutic targets as well as diagnostic biomarkers that improve patient outcomes.

Identification of THSD7B and PRMT9 mutations as risk factors for familial lung adenocarcinoma: A case report.

RATIONALE: Lung tumors arise from the unrestrained malignant growth of pulmonary epithelial cells. Lung cancer cases include both small and non-small cell lung cancers, with lung adenocarcinoma (LUAD) accounting for roughly half of all non-small cell lung cancer cases. Research focused on familial cancers suggests that approximately 8% of lung cancer cases are linked to genetic susceptibility or heritability. The precise genetic factors that underlie the onset of lung cancer, however, remain to be firmly established. PATIENT CONCERNS: A 43-year-old presented with nodules in the lower left lung lobe. Following initial antibiotic treatment in a local hospital, these nodules remained present and the patient subsequently underwent the resection of the left lower lobe of the lung. The patient also had 4 family members with a history of LUAD. DIAGNOSIS: Immunohistochemical staining results including cytokeratin 7 (+), TTF-1 (+), new aspartic proteinase A (+), CK5/6 (-), P63 (-), and Ki-67 (5%+) were consistent with a diagnosis of LUAD. INTERVENTION: Whole exome sequencing analyses of 5 patients and 6 healthy family members were performed to explore potential mutations associated with familial LUAD. OUTCOMES: Whole exome sequencing was conducted, confirming that the proband and their 4 other family members with LUAD harbored heterozygous THSD7B (c.A4000G:p.S1334G) mutations and homozygous PRMT9 (c.G40T:p.G14C) mutations, as further confirmed via Sanger sequencing. These mutations were predicted to be deleterious using the SIFT, PolyPhen2, and MutationTaster algorithms. Protein structure analyses indicated that the mutation of the serine at amino acid position 1334 in THSD7B to a glycine would reduce the minimum free energy from 8.08 kcal/mol to 68.57 kcal/mol. The identified mutation in the PRMT9 mutation was not present in the predicted protein structure. I-Mutant2.0 predictions indicated that both of these mutations (THSD7B:p.S1334G and PRMT9: p.G14C) were predicted to reduce protein stability. LESSONS: Heterozygous THSD7B (c.A4000G:p.S1334G) and the homozygous PRMT9 (c.G40T:p.G14C) mutations were found to be linked to LUAD incidence in the analyzed family. Early analyses of these genetic loci and timely genetic counseling may provide benefits and aid in the early diagnosis of familial LUAD.

Identification of Biomarkers for Osteosarcoma Based on Integration Strategy.

BACKGROUND Osteosarcoma (OS) is the most common primary malignant tumor of bone. The identification of novel biomarkers is necessary for the diagnosis and treatment of osteosarcoma. MATERIAL AND METHODS We obtained 11 paired fresh-frozen OS samples and normal controls from patients between September 2015 and February 2017. We used an integration strategy that analyzes next-generation sequencing data by bioinformatics methods based on the pathogenesis of osteosarcoma. RESULTS One susceptibility lncRNA and 7 susceptibility genes regulated by the lncRNA for osteosarcoma were effectively identified, and real-time PCR and clinical index ALP data were used to test their effectiveness. CONCLUSIONS The results showed that the expression levels of the 7 genes were highly consistent in the training and test sample sets, especially between the expression value of the gene ALPL and the plasma detection value of its encoded protein ALP. In particular, both the expression of gene ALPL and the plasma detection values of protein ALP encoded by gene ALPL showed a high degree of consistency among different data types. The identified lncRNA and genes effectively classified the samples proved so that they could be used as potential biomarkers of osteosarcoma. Our strategy may also be helpful for the identification of biomarkers for other diseases.

A susceptibility biomarker identification strategy based on significantly differentially expressed ceRNA triplets for ischemic cardiomyopathy.

Ischemic cardiomyopathy (ICM) is a common human heart disease that causes death. No effective biomarkers for ICM could be found in existing databases, which is detrimental to the in-depth study of this disease. In the present study, ICM susceptibility biomarkers were identified using a proposed strategy based on RNA-Seq and miRNA-Seq data of ICM and normal samples. Significantly differentially expressed competing endogenous RNA (ceRNA) triplets were constructed using permutation tests and differentially expressed mRNAs, miRNAs and lncRNAs. Candidate ICM susceptible genes were screened out as differentially expressed genes in significantly differentially expressed ceRNA triplets enriched in ICM-related functional classes. Finally, eight ICM susceptibility genes and their significantly correlated lncRNAs with high classification accuracy were identified as ICM susceptibility biomarkers. These biomarkers would contribute to the diagnosis and treatment of ICM. The proposed strategy could be extended to other complex diseases without disease biomarkers in public databases.

Integration of SNP Disease Association, eQTL, and Enrichment Analyses to Identify Risk SNPs and Susceptibility Genes in Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is a complex disease caused by the disturbance of genetic and environmental factors. Single-nucleotide polymorphisms (SNPs) play a vital role in the genetic dissection of complex diseases. In-depth analysis of SNP-related information could recognize disease-associated biomarkers and further uncover the genetic mechanism of complex diseases. Risk-related variants might act on the disease by affecting gene expression and gene function. Through integrating SNP disease association study and expression quantitative trait loci (eQTL) analysis, as well as functional enrichment of containing known causal genes, four risk SNPs and four corresponding susceptibility genes were identified utilizing next-generation sequencing (NGS) data of COPD. Of the four risk SNPs, one could be found in the SNPedia database that stored disease-related SNPs and has been linked to a disease in the literature. Four genes showed significant differences from the perspective of normal/disease or variant/nonvariant samples, as well as the high performance of sample classification. It is speculated that the four susceptibility genes could be used as biomarkers of COPD. Furthermore, three of our susceptibility genes have been confirmed in the literature to be associated with COPD. Among them, two genes had an impact on the significance of expression correlation of known causal genes they interact with, respectively. Overall, this research may present novel insights into the diagnosis and pathogenesis of COPD and susceptibility gene identification of other complex diseases.

Candidate gene prioritization for non-communicable diseases based on functional information: Case studies.

Candidate gene prioritization for complex non-communicable diseases is essential to understanding the mechanism and developing better means for diagnosing and treating these diseases. Many methods have been developed to prioritize candidate genes in protein-protein interaction (PPI) networks. Integrating functional information/similarity into disease-related PPI networks could improve the performance of prioritization. In this study, a candidate gene prioritization method was proposed for non-communicable diseases considering disease risks transferred between genes in weighted disease PPI networks with weights for nodes and edges based on functional information. Here, three types of non-communicable diseases with pathobiological similarity, Type 2 diabetes (T2D), coronary artery disease (CAD) and dilated cardiomyopathy (DCM), were used as case studies. Literature review and pathway enrichment analysis of top-ranked genes demonstrated the effectiveness of our method. Better performance was achieved after comparing our method with other existing methods. Pathobiological similarity among these three diseases was further investigated for common top-ranked genes to reveal their pathogenesis.