Bioinformatics workflows for clinical applications in precision oncology

GABRP inhibits the progression of oesophageal cancer by regulating CFTR: Integrating bioinformatics analysis and experimental validation

Oesophageal cancer (EC) is a malignancy which accounts for a substantial number of cancer-related deaths worldwide. The molecular mechanisms underlying the pathogenesis of EC have not been fully elucidated. GSE17351 and GSE20347 data sets from the Gene Expression Omnibus (GEO) database were employed to screen differentially expressed genes (DEGs). Reverse transcription quantitative PCR (RT-qPCR) was used to examine hub gene expression. ECA-109 and TE-12 cells were transfected using the pcDNA3.1 expression vector encoding GABRP. The cell counting kit-8 (CCK-8), cell scratch and Transwell assays were performed to assess the effect of GABRP on EC cell proliferation, migration and invasion. Epithelial-mesenchymal transition (EMT)-associated protein levels were measured by Western blotting. Subsequently, CFTR was knocked down to verify whether GABRP affected biological events in EC cells by targeting CFTR. Seven hub genes were identified, including GABRP, FLG, ENAH, KLF4, CD24, ABLIM3 and ABLIM1, which all could be used as diagnostic biomarkers for EC. The RT-qPCR results indicated that the expression levels of GABRP, FLG, KLF4, CD24, ABLIM3 and ABLIM1 were downregulated, whereas the expression level of ENAH was upregulated. In vitro functional assays demonstrated that GABRP overexpression suppressed the proliferation, migration, invasion and EMT of EC cells. Mechanistically, GABRP promoted the expression of CFTR, and CFTR knockdown significantly counteracted the influence of GABRP overexpression on biological events in EC cells. Overexpression of GABRP inhibited EC progression by increasing CFTR expression, which might be a new target for EC treatment.

A Novel Affordable and Reliable Framework for Accurate Detection and Comprehensive Analysis of Somatic Mutations in Cancer

Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues as an end-to-end pipeline for detecting, classifying, and interpreting cancer mutations. Musta is based on a Python command-line tool designed to manage tumor-normal samples for precise somatic mutation analysis. The core is a Snakemake-based workflow that covers all key cancer genomics steps, including variant calling, mutational signature deconvolution, variant annotation, driver gene detection, pathway analysis, and tumor heterogeneity estimation. Musta is easy to install on any system via Docker, with a Makefile handling installation, configuration, and execution, allowing for full or partial pipeline runs. Musta has been validated at the CRS4-NGS Core facility and tested on large datasets from The Cancer Genome Atlas and the Beijing Institute of Genomics. Musta has proven robust and flexible for somatic variant analysis in cancer. It is user-friendly, requiring no specialized programming skills, and enables data processing with a single command line. Its reproducibility ensures consistent results across users following the same protocol.

Identification of prognostic RNA editing profiles for clear cell renal carcinoma

Objective

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer and currently lacks effective biomarkers. This research aims to analyze and identify RNA editing profile associated with ccRCC prognosis through bioinformatics and in vitro experiments.

Methods

Transcriptome data and clinical information for ccRCC were retrieved from the TCGA database, and RNA editing files were obtained from the Synapse database. Prognostic models were screened, developed, and assessed using consistency index analysis and independent prognostic analysis, etc. Internal validation models were also constructed for further evaluation. Differential genes were investigated using GO, KEGG, and GSEA enrichment analyses. Furthermore, qPCR was performed to determine gene expression in human renal tubular epithelial cells HK-2 and ccRCC cells A-498, 786-O, and Caki-2.

Results

An RNA editing-based risk score, that effectively distinguishes between high and low-risk populations, has been identified. It includes CHD3| chr17:7815229, MYO19| chr17:34853704, OIP5-AS1| chr15:41590962, MRI1| chr19:13883962, GBP4| chr1:89649327, APOL1| chr22:36662830, FCF1| chr14:75203040 edited sites or genes and could serve as an independent prognostic factor for ccRCC patients. qPCR results showed significant up-regulation of CHD3, MYO19, MRI1, APOL1, and FCF1 in A-498, 786-O, and Caki-2 cells, while the expression of OIP5-AS1 and GBP4 was significantly down-regulated.

Conclusion

RNA editing site-based prognostic models are valuable in differentiating between high and low-risk populations. The seven identified RNA editing sites may be utilized as potential biomarkers for ccRCC.

Unraveling the immune landscape and therapeutic biomarker PMEPA1 for oxaliplatin resistance in colorectal cancer: A comprehensive approach

Oxaliplatin (OXA) is a platinum-based chemotherapeutic agent with promising applications in the treatment of various malignancies, particularly colorectal cancer (CRC). However, the management of OXA resistance remains an ongoing obstacle in CRC therapy. This study aims to comprehensively investigate the immune landscape, targeted therapeutic biomarkers, and mechanisms that influence OXA resistance in CRC. Our results demonstrated that our OXA- resistant CRC prognostic model not only provides risk assessment for patients but also reflects the immune landscape of patients. Additionally, we identified prostate transmembrane protein, androgen-induced1 (PMEPA1) as a promising molecular targeted therapeutic biomarker for patients with OXA-resistant CRC. The mechanism of PMEPA1 may involve cell adhesion, pathways in cancer, and the TGF-β signaling pathway. Furthermore, analysis of CRC clinical samples indicated that patients resistant to OXA exhibited elevated serum levels of TGF-β1, increased expression of PMEPA1 in tumors, a lower proportion of CD8+ T cell positivity, and a higher proportion of M0 macrophage positivity, in comparison to OXA-sensitive individuals. Cellular experiments indicated that selective silencing of PMEPA1, alone or in combination with OXA, inhibited proliferation and metastasis in OXA-resistant CRC cells, HCT116R. Animal experiments further confirmed that PMEPA1 silencing suppressed subcutaneous graft tumor growth and liver metastasis in mice bearing HCT116R and synergistically enhanced the efficacy of OXA. These data highlight the potential of leveraging the therapeutic biomarker PMEPA1, CD8+ T cells, and M0 macrophages as innovative targets for effectively addressing the challenges associated with OXA resistance. Our findings hold promising implications for further clinical advancements in this field.

Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes

Background: Endometriosis (EM) is a long-lasting inflammatory disease that is difficult to treat and prevent. Existing research indicates the significance of immune infiltration in the progression of EM. Efferocytosis has an important immunomodulatory function. However, research on the identification and clinical significance of efferocytosis-related genes (EFRGs) in EM is sparse. Methods: The EFRDEGs (differentially expressed efferocytosis-related genes) linked to datasets associated with endometriosis were thoroughly examined utilizing the Gene Expression Omnibus (GEO) and GeneCards databases. The construction of the protein-protein interaction (PPI) and transcription factor (TF) regulatory network of EFRDEGs ensued. Subsequently, machine learning techniques including Univariate logistic regression, LASSO, and SVM classification were applied to filter and pinpoint diagnostic biomarkers. To establish and assess the diagnostic model, ROC analysis, multivariate regression analysis, nomogram, and calibration curve were employed. The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune cell infiltration, while the Comparative Toxicogenomics Database (CTD) was utilized for the identification of potential therapeutic drugs for endometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were utilized to quantify the expression levels of biomarkers in clinical samples of endometriosis. Results: Our findings revealed 13 EFRDEGs associated with EM, and the LASSO and SVM regression model identified six hub genes (ARG2, GAS6, C3, PROS1, CLU, and FGL2). Among these, ARG2, GAS6, and C3 were confirmed as diagnostic biomarkers through multivariate logistic regression analysis. The ROC curve analysis of GSE37837 (AUC = 0.627) and GSE6374 (AUC = 0.635), along with calibration and DCA curve assessments, demonstrated that the nomogram built on these three biomarkers exhibited a commendable predictive capacity for the disease. Notably, the ratio of nine immune cell types exhibited significant differences between eutopic and ectopic endometrial samples, with scRNA-seq highlighting M0 Macrophages, Fibroblasts, and CD8 Tex cells as the cell populations undergoing the most substantial changes in the three biomarkers. Additionally, our study predicted seven potential medications for EM. Finally, the expression levels of the three biomarkers in clinical samples were validated through RT-qPCR and IHC, consistently aligning with the results obtained from the public database. Conclusion: we identified three biomarkers and constructed a diagnostic model for EM in this study, these findings provide valuable insights for subsequent mechanistic research and clinical applications in the field of endometriosis.

The Clinical Laboratory Is an Integral Component to Health Care Delivery : An Expanded Representation of the Total Testing Process

OBJECTIVES

Developing an expanded representation of the total testing process that includes contemporary elements of laboratory practice can be useful to understanding and optimizing testing workflows across clinical laboratory and patient care settings.

METHODS

Published literature and meeting reports were used by the coauthors to inform the development of the expanded representation of the total testing process and relevant examples describing its uses.

RESULTS

A visual representation of the total testing process was developed and contextualized to patient care scenarios using a number of examples covering the detection of blood culture contamination, use of next-generation sequencing, and pharmacogenetic testing.

CONCLUSIONS

The expanded representation of the total testing process can serve as a model and framework to document and improve the use of clinical testing within the broader context of health care delivery. This representation recognizes increased engagement among clinical laboratory professionals with patients and other health care providers as essential to making informed decisions. The increasing use of data is highlighted as important to ensuring quality, appropriate test utilization, and sustaining an efficient workflow across clinical laboratory and patient care settings. Maintaining a properly resourced and competent workforce is also featured as an essential component to the testing process.

Identification of thioredoxin-1 as a biomarker of lung cancer and evaluation of its prognostic value based on bioinformatics analysis

Background

Thioredoxin-1 (TXN), a redox balance factor, plays an essential role in oxidative stress and has been shown to act as a potential contributor to various cancers. This study evaluated the role of TXN in lung cancer by bioinformatics analyses.

Materials and methods

Genes differentially expressed in lung cancer and oxidative stress related genes were obtained from The Cancer Genome Atlas, Gene Expression Omnibus and GeneCards databases. Following identification of TXN as an optimal differentially expressed gene by bioinformatics, the prognostic value of TXN in lung cancer was evaluated by univariate/multivariate Cox regression and Kaplan-Meier survival analyses, with validation by receiver operation characteristic curve analysis. The association between TXN expression and lung cancer was verified by immunohistochemical analysis of the Human Protein Atlas database, as well as by western blotting and qPCR. Cell proliferation was determined by cell counting kit-8 after changing TXN expression using lentiviral transfection.

Results

Twenty differentially expressed oxidative stress genes were identified. Differential expression analysis identified five genes (CASP3, CAT, TXN, GSR, and HSPA4) and Kaplan-Meier survival analysis identified four genes (IL-6, CYCS, TXN, and BCL2) that differed significantly in lung cancer and normal lung tissue, indicating that TXN was an optimal differentially expressed gene. Multivariate Cox regression analysis showed that T stage (T3/T4), N stage (N2/N3), curative effect (progressive diseases) and high TXN expression were associated with poor survival, although high TXN expression was poorly predictive of overall survival. TXN was highly expressed in lung cancer tissues and cells. Knockdown of TXN suppressed cell proliferation, while overexpression of TXN enhanced cell proliferation.

Conclusion

High expression of TXN plays an important role in lung cancer development and prognosis. Because it is a prospective prognostic factor, targeting TXN may have clinical benefits in the treatment of lung cancer.

Identification and functional analysis of lncRNAs and mRNAs between tumorigenesis and metastasis in CRC

The role of long non-coding RNAs (lncRNAs) in colorectal cancer (CRC) tumorigenesis and metastasis remains poorly characterized. The aim of this study was to identify novel lncRNAs and their functions in CRC progression. Through microarray analysis of paired normal colorectal mucosa (NM), primary tumor (PT), and metastatic lymph node (MLN) tissues, lncRNA and mRNA expression patterns were identified. Further bioinformatic analyses were performed to compare the biological functions of lncRNAs between tumorigenesis and metastasis of CRC, which was further verified by TCGA-COAD and GSE82236. The expression of lncRNA MIR29B2CHG93 in paired CRC tissues was detected in a cohort of CRC patients. The effects of lncRNA MIR29B2CHG93 on proliferation, migration, and invasion were determined by in vitro experiments. We found that tumorigenesis-associated lncRNAs predominantly participated in the regulation of the EMT/P53/PI3K-Akt/KRAS signaling pathway as well as the processes related to cell cycle and cell mitosis, while metastasis-associated lncRNAs mainly regulated blood vessel morphogenesis and immune-related biological processes. Compared to the TCGA and GSE datasets, seven tumorigenesis-associated lncRNAs and eight metastasis-associated lncRNAs were identified. LncRNA MIR29B2CHG93 knockdown remarkably suppressed tumor growth and metastasis in vitro, which acted as a tumor promoter in CRC. The lncRNA MIR29B2CHG93 was significantly upregulated in CRC tissues and was indicator of unfavorable clinical outcome in CRC. These results revealed novel lncRNAs that provide new insights for an in-depth understanding of CRC progression. In particular, this study identified a novel lncRNA MIR29B2CHG93 in CRC progression, which might be a potential biomarker for diagnosis, prognosis and metastasis-prediction in CRC.