Background: It has been suggested that p300 participates in the regulation of a wide range of cell biological processes and mutation of p300 has been identified in certain types of human cancers. However, the expression dynamics of p300 in hepatocellular carcinoma (HCC) and its clinical/prognostic significance are unclear.Methods: In this study, the methods of reverse transcription-polymerase chain reaction (RT-PCR), Western blotting and immunohistochemistry (IHC) were utilized to investigate protein/mRNA expression of p300 in HCCs. Receiver operating characteristic (ROC) curve analysis, spearman's rank correlation, Kaplan-Meier plots and Cox proportional hazards regression model were used to analyze the data.Results: Up-regulated expression of p300 mRNA and protein was observed in the majority of HCCs by RT-PCR and Western blotting, when compared with their adjacent non-malignant liver tissues. According to the ROC curves, the cutoff score for p300 high expression was defined when more than 60% of the tumor cells were positively stained. High expression of p300 was examined in 60/123 (48.8%) of HCCs and in 8/123 (6.5%) of adjacent non-malignant liver tissues. High expression of p300 was correlated with higher AFP level, larger tumor size, multiplicity, poorer differentiation and later stage (P< 0.05). In univariate survival analysis, a significant association between overexpression of p300 and shortened patients' survival was found (P= 0.001). In different subsets of HCC patients, p300 expression was also a prognostic indicator in patients with stage II (P= 0.007) and stage III (P= 0.011). Importantly, p300 expression was evaluated as an independent prognostic factor in multivariate analysis (P= 0.021). Consequently, a new clinicopathologic prognostic model with three poor prognostic factors (p300 expression, AFP level and vascular invasion) was constructed. The model could significantly stratify risk (low, intermediate and high) for overall survival (P< 0.0001).Conclusions: Our findings provide a basis for the concept that high expression of p300 in HCC may be important in the acquisition of an aggressive phenotype, suggesting that p300 overexpression, as examined by IHC, is an independent biomarker for poor prognosis of patients with HCC. The combined clinicopathologic prognostic model may become a useful tool for identifying HCC patients with different clinical outcomes.

For patients with early-stage breast cancers, neoadjuvant treatment is recommended for non-luminal tumors instead of luminal tumors. Preoperative distinguish between luminal and non-luminal cancers at early stages will facilitate treatment decisions making. However, the molecular immunohistochemical subtypes based on biopsy specimens are not always consistent with final results based on surgical specimens due to the high intra-tumoral heterogeneity. Given that, we aimed to develop and validate a deep learning radiopathomics (DLRP) model to preoperatively distinguish between luminal and non-luminal breast cancers at early stages based on preoperative ultrasound (US) images, and hematoxylin and eosin (H&E)-stained biopsy slides. This multicentre study included three cohorts from a prospective study conducted by our team and registered on the Chinese Clinical Trial Registry (ChiCTR1900027497). Between January 2019 and August 2021, 1809 US images and 603 H&E-stained whole slide images (WSIs) from 603 patients with early-stage breast cancers were obtained. A Resnet18 model pre-trained on ImageNet and a multi-instance learning based attention model were used to extract the features of US and WSIs, respectively. An US-guided Co-Attention module (UCA) was designed for feature fusion of US and WSIs. The DLRP model was constructed based on these three feature sets including deep learning US feature, deep learning WSIs feature and UCA-fused feature from a training cohort (1467 US images and 489 WSIs from 489 patients). The DLRP model's diagnostic performance was validated in an internal validation cohort (342 US images and 114 WSIs from 114 patients) and an external test cohort (279 US images and 90 WSIs from 90 patients). We also compared diagnostic efficacy of the DLRP model with that of deep learning radiomics model and deep learning pathomics model in the external test cohort. The DLRP yielded high performance with area under the curve (AUC) values of 0.929 (95% CI 0.865–0.968) in the internal validation cohort, and 0.900 (95% CI 0.816–0.953) in the external test cohort. The DLRP also outperformed deep learning radiomics model based on US images only (AUC 0.815 [0.719–0.889], p < 0.027) and deep learning pathomics model based on WSIs only (AUC 0.802 [0.704–0.878], p < 0.013) in the external test cohort. The DLRP can effectively distinguish between luminal and non-luminal breast cancers at early stages before surgery based on pretherapeutic US images and biopsy H&E-stained WSIs, providing a tool to facilitate treatment decision making in early-stage breast cancers. 10.13039/501100003453 Natural Science Foundation of Guangdong Province (No. 2023A1515011564), and 10.13039/501100001809 National Natural Science Foundation of China (No. 91959127; No. 81971631).

Topoisomerase II alpha (TOP2A) protein has been shown to be a proliferation marker associated with tumor grade and Ki67 index. The prognostic effect of TOP2A seems different among different subtypes of breast cancer. The current study evaluated the prognostic impact of TOP2A protein on luminal breast cancer. Altogether 434 stage I-II luminal breast cancer patients who underwent curative surgery in Sun Yat-Sen University Cancer Center between 2007 and 2009 were enrolled. TOP2A protein expression was assessed by immunohistochemistry. Clinical and pathological data were retrospectively collected. With a cut-off value of 30%, 127 (29.3%) patients were classified as TOP2A overexpression. TOP2A overexpression was associated with a higher tumor grade and Ki67 index. Patients with TOP2A high expression showed a significantly higher rate of distant metastasis and shorter distant metastasis free survival (DMFS) compared with patients with low TOP2A expression. The prognostic influence of TOP2A expression was more significant in years 5–8 after diagnosis, and more pronounced in stage II patients, luminal B disease, and patients treated with adjuvant endocrine therapy alone. Multivariate survival analysis revealed TOP2A overexpression was an independent fact for worse DMFS. TOP2A protein showed a time dependent influence on prognosis in stage I-II luminal breast cancer, suggesting it might be a potential predictor of late recurrence for this group of patients.

Background: DNA polymerase η (pol η) is capable of bypassing DNA adducts produced by cisplatin or oxaliplatin and is associated with cellular tolerance to platinum. Previous studies showed that defective pol η resulted in enhanced cisplatin or oxaliplatin sensitivity in some cell lines. The purpose of the present study was to investigate the role of pol η protein expression in metastatic gastric adenocarcinoma. Methods: Four gastric adenocarcinoma cell lines were chosen to explore the relationship between pol η protein expression and oxaliplatin sensitivity by western blotting and MTT assay. Eighty metastatic gastric adenocarcinoma patients treated with FOLFOX or XELOX regimen as first-line chemotherapy were analyzed, corresponding pretreatment formalin-fixed paraffin-embedded tumor tissues were used to detect pol η protein expression by immunohistochemistry. Relationship between pol η protein expression and clinical features and outcome of these patients was analyzed. Results: A positive linear relationship between pol η protein expression and 48 h IC50 values of oxaliplatin in four gastric cancer cell lines was observed. Positivity of pol η protein expression was strongly associated with poor treatment response, as well as shorter survival at both univariate (8 versus 14 months; P < 0.001) and multivariate (hazard ratio, 4.555; 95% confidence interval, 2.461-8.429; P < 0.001) analysis in eighty metastatic gastric adenocarcinoma patients. Conclusions: Our study indicates that polη is a predictive factor of treatment response and survival of metastatic gastric adenocarcinoma patients treated with FOLFOX or XELOX as first-line chemotherapy. Therefore confirming the value of polη in studies with prospective design is mandatory.

Perineural invasion (PNI) is correlated with adverse survival in several malignancies, but its significance in esophageal squamous cell carcinoma (ESCC) remains to be clearly defined. The objective of this study was to determine the association between PNI status and clinical outcomes. We retrospectively evaluated the PNI of 433 patients with ESCC treated with surgery between 2000 and 2007 at a single academic center. The resulting data were analyzed using Spearman’s rank correlation, the Kaplan-Meier method, Cox proportional hazards regression modeling and Harrell’s concordance index (C-index). PNI was identified in 209 of the 433 (47.7%) cases of ESCC. The correlation analysis demonstrated that PNI in ESCC was significantly correlated with tumor differentiation, infiltration depth, pN classification and stage (P < 0.05). The five-year overall survival rate was 0.570 for PNI-negative tumors versus 0.326 for PNI-positive tumors. Patients with PNI-negative tumors exhibited a 1.7-fold increase in five-year recurrence-free survival compared with patients with PNI-positive tumors (0.531 v 0.305, respectively; P < 0.0001). In the subset of patients with node-negative disease, PNI was evaluated as a prognostic predictor as well (P < 0.05). In the multivariate analysis, PNI was an independent prognostic factor for overall survival (P = 0.027). The C-index estimate for the combined model (PNI, gender and pN status) was a significant improvement on the C-index estimate of the clinicopathologic model alone (0.739 v 0.706, respectively). PNI can function as an independent prognostic factor of outcomes in ESCC patients, and the PNI status in primary ESCC specimens should be considered for therapy stratification.

The “macrotrabecular-massive” (MTM) pattern of hepatocellular carcinoma (HCC) has been suggested to represent a distinct HCC subtype and is associated with specific molecular features. Since the immune microenvironment is heterogenous in HCC, it is important to evaluate the immune microenvironment of this novel variant. CMTM6, a key regulator of PD-L1, is an important immunocheckpoint inhibitor. This study aimed to evaluate the prognostic effect of CMTM6/PD-L1 coexpression and its relationship with inflammatory cells in HCC. We analyzed 619 HCC patients and tumors were classified into MTM and non-MTM HCC subtypes. The expression levels of CMTM6 and PD-L1 in tumor and inflammatory cells were evaluated by immunohistochemistry. The density of inflammatory cells in the cancer cell nest was calculated. Tumoral PD-L1 expression and inflammatory cell density were higher in the MTM type than in the non-MTM type. CMTM6-high expression was significantly associated with shorter OS and DFS than CMTM6-low expression in the whole HCC patient population and the MTM HCC patient population. Moreover, MTM HCC patients with CMTM6/PD-L1 coexpression experienced a higher risk of HCC progression and death. In addition, CMTM6/PD-L1 coexpression was shown to be related to a high density of inflammatory cells. Notably, a new immune classification, based on CMTM6/PD-L1 coexpression and inflammatory cells, successfully stratified OS and DFS in MTM HCC. CMTM6/PD-L1 coexpression has an adverse effect on the prognosis of HCC patients, especially MTM HCC patients. Our study provides evidence for the combination of immune status assessment with anti-CMTM6 and anti-PD-L1 therapy in MTM HCC patients.