Screening of differentially expressed genes related to esophageal squamous cell carcinoma and functional analysis with DNA microarrays

Lysosomal peptidases – Intriguing roles in cancer progression and neurodegeneration

Lysosomal peptidases are hydrolytic enzymes capable of digesting waste proteins that are targeted to lysosomes via endocytosis and autophagy. Besides intracellular protein catabolism, they play more specific roles in several other cellular processes and pathologies, either within lysosomes, upon secretion into the cell cytoplasm or extracellular space, or bound to the plasma membrane. In cancer, lysosomal peptidases are generally associated with disease progression, as they participate in crucial processes leading to changes in cell morphology, signaling, migration, and invasion, and finally metastasis. However, they can also enhance the mechanisms resulting in cancer regression, such as apoptosis of tumor cells or antitumor immune responses. Lysosomal peptidases have also been identified as hallmarks of aging and neurodegeneration, playing roles in oxidative stress, mitochondrial dysfunction, abnormal intercellular communication, dysregulated trafficking, and the deposition of protein aggregates in neuronal cells. Furthermore, deficiencies in lysosomal peptidases may result in other pathological states, such as lysosomal storage disease. The aim of this review was to highlight the role of lysosomal peptidases in particular pathological processes of cancer and neurodegeneration and to address the potential of lysosomal peptidases in diagnosing and treating patients.

Identification of Differentially Expressed Genes and miRNAs Associated with Esophageal Squamous Cell Carcinoma by Integrated Analysis of Microarray Data

To identify candidate key genes and miRNAs associated with esophageal squamous cell carcinoma (ESCC) development and prognosis, the gene expression profiles and miRNA microarray data including GSE20347, GSE38129, GSE23400, and GSE55856 were downloaded from the Gene Expression Omnibus (GEO) database. Clinical and survival data were retrieved from The Cancer Genome Atlas (TCGA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes (DEGs) was analyzed via DAVID, while the DEG-associated protein-protein interaction network (PPI) was constructed using the STRING database. Additionally, the miRNA target gene regulatory network and miRNA coregulatory network were constructed, using the Cytoscape software. Survival analysis and prognostic model construction were performed via the survival (version 2.42-6) and rbsurv R packages, respectively. The results showed a total of 2575, 2111, and 1205 DEGs, and 226 differentially expressed miRNAs (DEMs) were identified. Pathway enrichment analyses revealed that DEGs were mainly enriched in 36 pathways, such as the proteasome, p53, and beta-alanine metabolism pathways. Furthermore, 448 nodes and 1144 interactions were identified in the PPI network, with MYC having the highest random walk score. In addition, 7 DEMs in the microarray data, including miR-196a, miR-21, miR-205, miR-194, miR-103, miR-223, and miR-375, were found in the regulatory network. Moreover, several reported disease-related miRNAs, including miR-198a, miR-103, miR-223, miR-21, miR-194, and miR-375, were found to have common target genes with other DEMs. Survival analysis revealed that 85 DEMs were related to prognosis, among which hsa-miR-1248, hsa-miR-1291, hsa-miR-421, and hsa-miR-7-5p were used for a prognostic survival model. Taken together, this study revealed the important roles of DEGs and DEMs in ESCC development, as well as DEMs in the prognosis of ESCC. This will provide potential therapeutic targets and prognostic predictors for ESCC.

A five-gene signature to predict the overall survival time of patients with esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the six most commonly diagnosed tumor types in the Chinese population. Gene expression profiles help to predict the prognosis of patients with ESCC. Disease recurrence as the survival endpoint has been analyzed in the majority of previous studies; therefore, the aim of the present study was to construct a robust gene signature in order to determine the overall survival (OS) of patients with ESCC. The gene expression and clinical data of patients with ESCC were downloaded from The Cancer Genome Atlas (TCGA) database. Of the selected data (172 samples from surviving patients), 72 samples were randomly selected as modeling data, and verification was conducted using the entire dataset. Data from the Gene Expression Omnibus was analyzed simultaneously, and a venn diagram was constructed to determine the intersection between these two sets of results; a total of 97 genes were found to be associated with OS. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that these genes were primarily associated with specific pathways (Homo sapiens), including DNA replication, protein processing in endoplasmic reticulum and influenza A. A five-gene signature was identified with a robust likelihood-based survival modeling approach. Using regression coefficient modeling, a prognostic model consisting of the C-X-C motif chemokine ligand 8, DNA damage inducible transcript 3, RAB27A, member RAS oncogene family, replication factor C subunit 2 and elongation factor for RNA polymerase II 2 genes was constructed and validated. Based on these results, patients were subdivided into high and low-risk groups. Compared with the high-risk group, the OS time of patients in the low-risk group was significantly increased. Furthermore, it was determined that the five genes were all differentially expressed in ESCC tissues compared with normal tissues, indicating the potential role of these genes in ESCC initiation and progression. In another independent cohort, this five-gene signature was further confirmed and was considered as an independent prognostic biomarker for OS prediction in patients with ESCC. In conclusion, the OS of patients with ESCC may be predicted using this five-gene signature, which may be useful in identifying patients with high-risk ESCC.

Cathepsins: Potent regulators in carcinogenesis

Cathepsins (CTS) are mainly lysosomal acid hydrolases extensively involved in the prognosis of different diseases, and having a distinct role in tumor progression by regulating cell proliferation, autophagy, angiogenesis, invasion, and metastasis. As all these processes conjunctively lead to cancer progression, their site-specific regulation might be beneficial for cancer treatment. CTS regulate activation of the proteolytic cascade and protein turnover, while extracellular CTS is involved in promoting extracellular matrix degradation and angiogenesis, thereby stimulating invasion and metastasis. Despite cancer regulation, the involvement of CTS in cellular adaptation toward chemotherapy and radiotherapy augments their therapeutic potential. However, lysosomal permeabilization mediated cytosolic translocation of CTS induces programmed cell death. This complex behavior of CTS generates the need to discuss the different aspects of CTS associated with cancer regulation. In this review, we mainly focused on the significance of each cathepsin in cancer signaling and their targeting which would provide noteworthy information in the context of cancer biology and therapeutics.

Metabolism-related enzyme alterations identified by proteomic analysis in human renal cell carcinoma

The renal cell carcinoma (RCC) is one of the most common types of kidney neoplasia in Western countries; it is relatively resistant to conventional chemotherapy and radiotherapy. Metabolic disorders have a profound effect on the degree of malignancy and treatment resistance of the tumor. However, the molecular characteristics related to impaired metabolism leading to the initiation of RCC are still not very clear. In this study, two-dimensional electrophoresis (2-DE) and mass spectra (MS) technologies were utilized to identify the proteins involved in energy metabolism of RCC. A total of 73 proteins that were differentially expressed in conventional RCC, in comparison with the corresponding normal kidney tissues, were identified. Bioinformatics analysis has shown that these proteins are involved in glycolysis, urea cycle, and the metabolic pathways of pyruvate, propanoate, and arginine/proline. In addition, some were also involved in the signaling network of p53 and FAS. These results provide some clues for new therapeutic targets and treatment strategies of RCC.