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Abstract Background & aims Tertiary lymphoid structures (TLSs) are predictive biomarkers of favorable clinical outcomes and immunotherapy response in several solid malignancies, including non-small cell lung cancer (NSCLC). However, the relationship between TLSs and NSCLC prognosis has not been eludicated from the aspects of location, density, and maturity. This study aimed to investigate the clinicopathological and prognostic significance of TLSs in NSCLC. Methods A collection of 151 resected pulmonary nodules in patients with NSCLC was retrospectively analyzed. Two experienced pathologists reviewed hematoxylin-eosin (H&E) slides and assessed TLS scores at different anatomic subregions. Then, we analyzed their correlation with clinicopathologic parameters and CD8 staining intensity and assessed multiple clinicopathological factors affecting patient prognosis. Results CD8 expression was correlated with total (TLS-CT) (P = 0.000), aggregates (Agg) (TLS-CT) (P = 0.001), follicles (FOL)-I (TLS-CT) (P = 0.025), and TLS(overall) (P = 0.013). TLS scores in the central tumor (CT) and invasion margin (IM) areas were negatively correlated with distant metastasis and Union for International Cancer Control (UICC) stage in NSCLC patients, while TLS score in the CT area was positively correlated with CD8 expression. TLS (overall), Agg (TLS-CT), and FOL-I (TLS-CT) were positively correlated with distant metastasis, UICC stage, and CD8 expression in NSCLC patients. Agg (TLS-IM) was positively correlated with distant metastasis and UICC stage. FOL-I (TLS-IM) was positively correlated with UICC stage. FOL-II (TLS-IM) was positively correlated with distant metastasis (P

High-grade squamous intraepithelial lesions (HSILs) are regarded as precancerous lesions that can progress to cervical carcinoma; however, it is very difficult to effectively differentiate these precancerous cells from cancerous cells based on morphology alone. Additionally, the difference between precancerous cells and cancerous cells in regard to biological behaviour remains unclear. We previously cultured primary normal uterine cervical keratinocytes from human normal cervical tissue and cervical precancerous cells that were naturally infected with human papillomavirus from small-sized neoplastic cervical tissues. Here, we extended our study to further observe the in vitro proliferative characteristics of cervical precancerous cells at the cellular and molecular levels. In this study, we found that the growth rate of precancerous cells was significantly faster than that of normal cervical cells and slower than that of Caski cells. However, the proliferative capacity of such precancerous cells was similar to that of cancerous cells of the cervix at the molecular level. These results suggest that the surrounding environment of the cells may play an important role in the development of cervical cancer, which provides an important basis for the further study of precancerous and cancerous lesions of the cervix.

BACKGROUND: Colposcopy is one of the common methods of cervical cancer screening. The type of cervical transformation zone is considered one of the important factors for grading colposcopic findings and choosing treatment. OBJECTIVE: This study aims to develop a deep learning-based method for automatic classification of cervical transformation zone from colposcopy images. METHODS: We proposed a multiscale feature fusion classification network to classify cervical transformation zone, which can extract features from images and fuse them at multiple scales. Cervical regions were first detected from original colposcopy images and then fed into our multiscale feature fusion classification network. RESULTS: The results on the test dataset showed that, compared with the state-of-the-art image classification models, the proposed classification network had the highest classification accuracy, reaching 88.49%, and the sensitivity to type 1, type 2 and type 3 were 90.12%, 85.95% and 89.45%, respectively, higher than the comparison methods. CONCLUSIONS: The proposed method can automatically classify cervical transformation zone in colposcopy images, and can be used as an auxiliary tool in cervical cancer screening. [ABSTRACT FROM AUTHOR]

Tumor necrosis factor receptor-associated protein 1 (TRAP1 or Hsp75) is a member of the mitochondrial heat shock proteins. TRAP1 expression is associated with drug and apoptosis resistance in various human cancers. This study assessed TRAP1 expression in tissue specimens of epithelial ovarian cancer (EOC) and its association with clinicopathological features and survival of EOC patients. Tissue samples from 356 patients were collected for immunohistochemical analysis of TRAP1 expression. TRAP1 levels in EOC tissues were significantly higher than that in noncancerous tissues. TRAP1 expression was associated with poor EOC differentiation, advanced pT stages, lymph node and distant metastasis, and advanced FIGO stages ( p < 0.05). Kaplan-Meier survival analysis showed that TRAP1 expression was associated with poor prognosis of EOC patients and multivariate analysis revealed that TRAP1 expression (relative risk 6.720, CI 4.100-11.015, p < 0.001), tumor grade ( p = 0.001), pathology stages ( p = 0.001), and FIGO stage ( p = 0.017) were all independent predictors of patients' overall survival. These data demonstrate for the first time that increased TRAP1 expression was significantly associated with EOC stages and poor prognosis. Future studies are needed to confirm the role of TRAP1 as a prognostic tumor marker in EOC. [ABSTRACT FROM AUTHOR]

Tumor necrosis factor receptor-associated protein 1 (TRAP1 or Hsp75) is a member of the mitochondrial heat shock proteins. TRAP1 expression is associated with drug and apoptosis resistance in various human cancers. This study assessed TRAP1 expression in tissue specimens of epithelial ovarian cancer (EOC) and its association with clinicopathological features and survival of EOC patients. Tissue samples from 356 patients were collected for immunohistochemical analysis of TRAP1 expression. TRAP1 levels in EOC tissues were significantly higher than that in noncancerous tissues. TRAP1 expression was associated with poor EOC differentiation, advanced pT stages, lymph node and distant metastasis, and advanced FIGO stages (p < 0.05). Kaplan-Meier survival analysis showed that TRAP1 expression was associated with poor prognosis of EOC patients and multivariate analysis revealed that TRAP1 expression (relative risk 6.720, CI 4.100-11.015, p < 0.001), tumor grade (p = 0.001), pathology stages (p = 0.001), and FIGO stage (p = 0.017) were all independent predictors of patients' overall survival. These data demonstrate for the first time that increased TRAP1 expression was significantly associated with EOC stages and poor prognosis. Future studies are needed to confirm the role of TRAP1 as a prognostic tumor marker in EOC.