Pathological examination is the gold standard for the diagnosis of cancer. Common pathological examinations include hematoxylin-eosin (H&E) staining and immunohistochemistry (IHC). In some cases, it is hard to make accurate diagnoses of cancer by referring only to H&E staining images. Whereas, the IHC examination can further provide enough evidence for the diagnosis process. Hence, the generation of virtual IHC images from H&E-stained images will be a good solution for current IHC examination hard accessibility issue, especially for some low-resource regions. However, existing approaches have limitations in microscopic structural preservation and the consistency of pathology properties. In addition, pixel-level paired data is hard available. In our work, we propose a novel adversarial learning method for effective Ki-67-stained image generation from corresponding H&E-stained image. Our method takes fully advantage of structural similarity constraint and skip connection to improve structural details preservation; and pathology consistency constraint and pathological representation network are first proposed to enforce the generated and source images hold the same pathological properties in different staining domains. We empirically demonstrate the effectiveness of our approach on two different unpaired histopathological datasets. Extensive experiments indicate the superior performance of our method that surpasses the state-of-the-art approaches by a significant margin. In addition, our approach also achieves a stable and good performance on unbalanced datasets, which shows our method has strong robustness. We believe that our method has significant potential in clinical virtual staining and advance the progress of computer-aided multi-staining histology image analysis.

The current World Health Organization (WHO) classification of nasopharyngeal carcinoma (NPC) conveys little prognostic information. This study aimed to propose an NPC histopathologic classification that can potentially be used to predict prognosis and treatment response. We initially developed a histopathologic classification based on the morphologic traits and cell differentiation of tumors of 2716 NPC patients who were identified at Sun Yat-sen University Cancer Center (SYSUCC) (training cohort). Then, the proposed classification was applied to 1702 patients (retrospective validation cohort) from hospitals outside SYSUCC and 1613 patients (prospective validation cohort) from SYSUCC. The efficacy of radiochemotherapy and radiotherapy modalities was compared between the proposed subtypes. We used Cox proportional hazards models to estimate hazard ratios (HRs) with 95% confidence intervals (CI) for overall survival (OS). The 5-year OS rates for all NPC patients who were diagnosed with epithelial carcinoma (EC; 3708 patients), mixed sarcomatoid-epithelial carcinoma (MSEC; 1247 patients), sarcomatoid carcinoma (SC; 823 patients), and squamous cell carcinoma (SCC; 253 patients) were 79.4%, 70.5%, 59.6%, and 42.6%, respectively (P < 0.001). In multivariate models, patients with MSEC had a shorter OS than patients with EC (HR = 1.44, 95% CI = 1.27–1.62), SC (HR = 2.00, 95% CI = 1.76–2.28), or SCC (HR = 4.23, 95% CI = 3.34–5.38). Radiochemotherapy significantly improved survival compared with radiotherapy alone for patients with EC (HR = 0.67, 95% CI = 0.56–0.80), MSEC (HR = 0.58, 95% CI = 0.49–0.75), and possibly for those with SCC (HR = 0.63; 95% CI = 0.40–0.98), but not for patients with SC (HR = 0.97, 95% CI = 0.74–1.28). The proposed classification offers more information for the prediction of NPC prognosis compared with the WHO classification and might be a valuable tool to guide treatment decisions for subtypes that are associated with a poor prognosis.

Computational pathology has advanced through foundation models, yet faces challenges in multimodal integration and capturing whole-slide context. Current approaches typically utilize either vision-only or image-caption data, overlooking distinct insights from pathology reports and gene expression profiles. Additionally, most models focus on patch-level analysis, failing to capture comprehensive whole-slide patterns. Here we present mSTAR (Multimodal Self-TAught PRetraining), the pathology foundation model that incorporates three modalities: pathology slides, expert-created reports, and gene expression data, within a unified framework. Our dataset includes 26,169 slide-level modality pairs across 32 cancer types, comprising over 116 million patch images. This approach injects multimodal whole-slide context into patch representations, expanding modeling from single to multiple modalities and from patch-level to slide-level analysis. Across oncological benchmark spanning 97 tasks, mSTAR outperforms previous state-of-the-art models, particularly in molecular prediction and multimodal tasks, revealing that multimodal integration yields greater improvements than simply expanding vision-only datasets.

RNA binding proteins (RBPs) contributes to cancer progression, but the underlying mechanism reminds unclear. Here, we find that DDX21, a representative RBP, is highly expressed in colorectal cancer (CRC), which leads to CRC cell migration and invasion in vitro, and CRC to liver metastasis and lung metastasis in vivo. This effect of DDX21 on CRC metastasis is correlated to the activation of Epithelial-mesenchymal transition (EMT) pathway. Moreover, we reveal that DDX21 protein is phase separated in vitro and in CRC cells, which controls CRC metastasis. Phase-separated DDX21 highly binds on MCM5 gene locus, which is markedly reduced when phase separation is disrupted by mutations on its intrinsically disordered region (IDR). The impaired metastatic ability of CRC upon DDX21 loss is restored by ectopic expression of MCM5, indicating MCM5 is a key downstream target of DDX21 for CRC metastasis. Furthermore, co-higher expressions of DDX21 and MCM5 is significantly correlated with poor survival outcomes of stage III and IV CRC patients, indicating the importance of this mechanism in CRC late and metastatic stage. Altogether, our results elucidate a new model of DDX21 in regulating CRC metastasis via phase separation.

The present study was aimed at investigating the expression of metastasis-associated in colon cancer 1 (MACC1) in nasopharyngeal carcinoma (NPC), its relationship with β-catenin, Met expression and the clinicopathological features of NPC, and its roles in carcinogenesis of NPC. Our results showed that MACC1 expression was higher in NPC cells and tissues than that in normal nasopharyngeal cells and chronic inflammation of the nasopharynx tissues, respectively. MACC1 expression was closely related to the clinical stage (p = 0.005) and the N classification (p<0.05) of NPC. Significant correlations between MACC1 expression and Met expression (p = 0.003), MACC1 expression and β-catenin abnormal expression (p = 0.033) were found in NPC tissues. MACC1 knockdown dramatically inhibited cellular proliferation, migration, invasion, and colony formation, but induced apoptosis in NPC cells compared with the control group. Furthermore, MACC1 down-regulation inhibited phosphorylated-Akt (Ser473) and β-catenin expression in NPC cells, but phosphorylated-Erk1/2 expression was not altered. Further study showed that phosphotidylinsitol-3-kinase inhibitor downregulated β-catenin and Met expression in NPC cells. There was a significant relationship between MACC1 expression and phosphorylated-Akt expression (p = 0.03), β-catenin abnormal expression and phosphorylated-Akt expression (p = 0.012) in NPC tissue, respectively. In addition, Epstein Barr virus-encoded oncogene latent membrane protein 1 upregulated MACC1 expression in NPC cells. Our results firstly suggest that MACC1 plays an important role in carcinogenesis of NPC through Akt/β-catenin signaling pathway. Targeting MACC1 may be a novel therapeutic strategy for NPC.

MiR-27a-3p targeting RXR promotes colorectal cancer progression by activating Wnt/-catenin pathway miR-27a-3p targeting RXRα promotes colorectal cancer progression by activating Wnt/β-catenin pathway