Colorectal cancer (CRC) is one of the most common malignancies worldwide. Approximately 10%-15% of the CRC cases have defective DNA mismatch repair (MMR) genes. Although the high level of microsatellite instability status is a predictor of favorable outcome in primary CRC, little is known about its frequency and importance in secondary CRC. Immunohistochemical staining (IHC) for MMR proteins (e.g., MLH1, MSH2, MSH6, and PMS2) has emerged as a useful technique to complement polymerase chain reaction (PCR) analyses. In this study, comparison between the MMR system of primary CRCs and paired liver and lung metastatic lesions was done using IHC and the correlation with clinical outcomes was also examined. Based on IHC, 7/61 primary tumors (11.4%) showed deficient MMR systems, while 13/61 secondary tumors (21.3%) showed deficiencies. In total, 44 cases showed proficient expression in both the primary and metastatic lesions. Three cases showed deficiencies in both the primary and paired metastatic lesions. In 10 cases, proficient expression was found only in the primary lesions, and not in the corresponding metastatic lesions. In four cases, proficient expression was detected in the secondary tumor, but not in the primary tumor. Although each IHC result and the likely defective genes were not exactly matched between the primary and the metastatic tumors, identical results for primary and metastatic lesions were obtained in 77% of the cases (47/61). These data are in agreement with the previous microsatellite detection studies that used PCR and IHC.

In this study, we aimed to investigate the molecular biomarkers that are pivotal for the development and progression of gastric cancer (GC). We analyzed clinical specimens using RNA sequencing to identify the target genes. We found that the expression of HOXC6 mRNA was upregulated with the progression of cancer, which was validated by quantitative real time PCR and RNA in-situ hybridization. To compare the protein expression of HOXC6, we evaluated GC and normal gastric tissue samples using western blot analysis and immunohistochemistry. We detected significantly higher levels of HOXC6 in the GC tissues than in the normal controls at both mRNA and protein levels. The expression levels of HOXC6 mRNA in patients with advanced gastric cancer (AGC) were significantly higher than those in patients with early gastric cancer (EGC). Kaplan-Meier curves showed that high expression of HOXC6 mRNA is significantly associated with poor clinical prognosis. Our findings suggest that HOXC6 mRNA may be a novel biomarker and can be potentially valuable in predicting the prognosis of GC patients. Especially, HOXC6 mRNA in-situ hybridization may be a diagnostic tool for predicting prognosis of individual GC patients.

Mycobacterial culture is the gold standard test for diagnosing tuberculosis (TB), but it is time-consuming. Polymerase chain reaction (PCR) is a highly sensitive and specific method that can reduce the time required for diagnosis. The diagnostic efficacy of PCR differs, so this study determined the actual sensitivity of TB-PCR in tissue specimens. We retrospectively reviewed 574 cases. The results of the nested PCR of the IS6110 gene, mycobacterial culture, TB-specific antigen-induced interferon-γ release assay (IGRA), acid-fast bacilli (AFB) staining, and histological findings were evaluated. The positivity rates were 17.6% for PCR, 3.3% for the AFB stain, 22.2% for mycobacterial culture, and 55.4% for IGRA. PCR had a low sensitivity (51.1%) and a high specificity (86.3%) based on the culture results of other studies. The sensitivity was higher (65.5%) in cases with necrotizing granuloma but showed the highest sensitivity (66.7%) in those with necrosis only. The concordance rate between the methods indicated that PCR was the best method compared to mycobacterial culture, and the concordance rate increased for the methods using positive result for PCR or histologic features. PCR of tissue specimens is a good alternative to detect tuberculosis, but it may not be as sensitive as previously suggested. Its reliability may also be influenced by some histological features. Our data showed a higher sensitivity when specimens contained necrosis, which indicated that only specimens with necrosis should be used for PCR to detect tuberculosis.

Large cell neuroendocrine carcinoma (LCNEC) of the gallbladder is extremely rare and usually combined with other type of malignancy, mostly adenocarcinoma. We report an unusual case of combined adenosquamous carcinoma and LCNEC of the gallbladder in a 54-year-old woman. A radical cholecystectomy specimen revealed a 4.3×4.0 cm polypoid mass in the fundus with infiltration of adjacent liver parenchyma. Microscopically, the tumor consisted of two distinct components. Adenosquamous carcinoma was predominant and abrupt transition from adenocarcinoma to squamous cell carcinoma was observed. LCNEC showed round cells with large, vesicular nuclei, abundant mitotic figures, and occasional pseudorosette formation. The patient received adjuvant chemotherapy. However, multiple liver metastases were identified at 3-month follow-up. Metastatic nodules were composed of LCNEC and squamous cell carcinoma components. Detecting LCNEC component is important in gallbladder cancer, because the tumor may require a different chemotherapy regimen and show early metastasis and poor prognosis.

Perineural invasion (PNI) is a well-established independent prognostic factor for poor outcomes in colorectal cancer (CRC). However, PNI detection in CRC is a cumbersome and time-consuming process, with low inter-and intra-rater agreement. In this study, a deep-learning-based approach was proposed for detecting PNI using histopathological images. We collected 530 regions of histology from 77 whole-slide images (PNI, 100 regions; non-PNI, 430 regions) for training. The proposed hybrid model consists of two components: a segmentation network for tumor and nerve tissues, and a PNI classifier. Unlike a “black-box” model that is unable to account for errors, the proposed approach enables false predictions to be explained and addressed. We presented a high performance, automated PNI detector, with the area under the curve (AUC) for the receiver operating characteristic (ROC) curve of 0.92. Thus, the potential for the use of deep neural networks in PNI screening was proved, and a possible alternative to conventional methods for the pathologic diagnosis of CRC was provided.