Serum tumour associated trypsin inhibitor, as a biomarker for survival in renal cell carcinoma

Performance of SPINK1 and SPINK1-based diagnostic model in detection of hepatocellular carcinoma

OBJECTIVES

To identify the SPINK1 or SPINK1-based model as a more reliable biomarker for the diagnosis of hepatocellular carcinoma (HCC).

METHODS

Serum samples and related laboratory parameters were collected from 540 subjects (119 healthy donors, 113 patients with chronic hepatitis B, 122 patients with liver cirrhosis, and 186 patients with HCC). SPINK1 was determined by ELISA assay. Differences in each variable were compared by one-way ANOVA or Kruskal-Wallis test. ROC (receiver operating characteristic) curve analysis was conducted to compare the diagnostic efficiency of alpha-fetoprotein (AFP), SPINK1, and a SPINK1-based combine model constructed by binary Logistic regression.

RESULTS

In detecting HCC using the other three groups as control, ROC curve analysis revealed that SPINK1 alone reached AUC of 0.899 (0.866-0.933), with the sensitivity of 0.812 of and specificity of 0.953. The combined model increased the AUC to 0.945 (0.926-0.964) with the sensitivity and specificity of 0.860 and 0.910, respectively. For AFP, significantly lower AUC (p < 0.0001) was shown, which was 0.695 (0.645-0.745) with the sensitivity and specificity of 0.634 and 0.718, respectively. In discriminating HCC from liver disease control, AUC of SPINK1 was 0.863(0.826-0.894), the sensitivity and specificity were 0.823 and 0.906, respectively. For combined model, the AUC, sensitivity, and specificity were 0.915 (0.884-0.940), 0.863, and 0.916, respectively. For detecting early-stage HCC, SPINK1 and combined model achieved the sensitivity of 0.788 and 0.818, respectively, much higher than AFP of 0.485 (p < 0.05); however, the difference between SPINK1 and combined model was not statistically significant (p = 1).

CONCLUSION

We provided solid evidence for SPINK1 as a robust serological tool for HCC diagnosis.

Tumor-Associated Trypsin Inhibitor (TATI) as a Biomarker of Poor Prognosis in Oropharyngeal Squamous Cell Carcinoma Irrespective of HPV Status

BACKGROUND

We studied the role of tumor-associated trypsin inhibitor (TATI) in serum and in tumor tissues among human papillomavirus (HPV)-positive and HPV-negative OPSCC patients.

MATERIALS AND METHODS

The study cohort included 90 OPSCC patients treated at the Helsinki University Hospital (HUS), Helsinki, Finland, in 2012-2016. TATI serum concentrations (S-TATIs) were determined by an immunofluorometric assay. Immunostaining was used to assess tissue expression. HPV status was determined with a combination of p16 immunohistochemistry and HPV DNA PCR genotyping. The survival endpoints were overall survival (OS) and disease-specific survival (DSS).

RESULTS

A significant correlation was found between S-TATI positivity and poor OS (p < 0.001) and DSS (p = 0.04) in all patients. In HPV-negative cases, S-TATI positivity was linked to poor OS (p = 0.01) and DSS (p = 0.05). In HPV-positive disease, S-TATI positivity correlated with poor DSS (p = 0.01). S-TATI positivity was strongly associated with HPV negativity. TATI serum was negatively linked to a lower cancer stage. TATI expression in peritumoral lymphocytes was associated with favorable OS (p < 0.025) and HPV positivity. TATI expression in tumor and in peritumoral lymphocytes correlated with lower cancer stages.

CONCLUSION

Our results suggest that S-TATI positivity may be a biomarker of poor prognosis in both HPV-positive and HPV-negative OPSCC.

Genomic Fabric Remodeling in Metastatic Clear Cell Renal Cell Carcinoma (ccRCC): A New Paradigm and Proposal for a Personalized Gene Therapy Approach

Simple Summary

We applied the genomic fabric principles for personalized gene therapy to a case of clear cell renal cell carcinoma (ccRCC). Despite decades of research, the process of finding the molecular mechanisms responsible for the disease and, more importantly, the therapeutic solution is still a work in progress. We analyzed the transcriptomes of the chest wall metastasis, two distinct cancer nodules, and the cancer-free surrounding tissue in the surgically removed right kidney of a Fuhrman grade 3 metastatic ccRCC patient. The studies revealed that even histopathologically equally classified cancer nodules from the same kidney have different transcriptomic topologies, requiring tailored therapeutic solutions not only for each patient but even for each cancer nodule. We identified death-associated protein kinase 3 (DAPK3); transcription activation suppressor (TASOR); family with sequence similarity 27, member C, long non-coding RNA (FAM27C); and UDP-N-acetylglucosaminyltransferase subunit (ALG13) as the gene master regulators of the four profiled regions and proposed molecular mechanisms by which expression manipulation of TASOR and ALG13 may selectively destroy the cancer cells without affecting many of the normal cells.

Abstract

Published transcriptomic data from surgically removed metastatic clear cell renal cell carcinoma samples were analyzed from the genomic fabric paradigm (GFP) perspective to identify the best targets for gene therapy. GFP considers the transcriptome as a multi-dimensional mathematical object constrained by a dynamic set of expression controls and correlations among genes. Every gene in the chest wall metastasis, two distinct cancer nodules, and the surrounding normal tissue of the right kidney was characterized by three independent measures: average expression level, relative expression variation, and expression correlation with each other gene. The analyses determined the cancer-induced regulation, control, and remodeling of the chemokine and vascular endothelial growth factor (VEGF) signaling, apoptosis, basal transcription factors, cell cycle, oxidative phosphorylation, renal cell carcinoma, and RNA polymerase pathways. Interestingly, the three cancer regions exhibited different transcriptomic organization, suggesting that the gene therapy should not be personalized only for every patient but also for each major cancer nodule. The gene hierarchy was established on the basis of gene commanding height, and the gene master regulators DAPK3,TASOR, FAM27C and ALG13 were identified in each profiled region. We delineated the molecular mechanisms by which TASOR overexpression and ALG13 silencing would selectively affect the cancer cells with little consequences for the normal cells.