The apelin‑apelin receptor signaling pathway in fibroblasts is involved in tumor growth via p53 expression of cancer cells

Cancer-associated fibroblasts (CAFs) are pivotal in tumor progression. TP53-deficiency in cancer cells is associated with robust stromal activation. The apelin-apelin receptor (APJ) system has been implicated in suppressing fibroblast-to-myofibroblast transition in non-neoplastic organ fibrosis. The present study aimed to elucidate the oncogenic role of the apelin-APJ system in tumor fibroblasts. APJ expression and the effect of APJ suppression in fibroblasts were investigated for p53 status in cancer cells using human cell lines (TP53-wild colon cancer, HCT116, and Caco-2; TP53-mutant colon cancer, SW480, and DLD-1; and colon fibroblasts, CCD-18Co), resected human tissue samples of colorectal cancers, and immune-deficient nude mouse xenograft models. The role of exosomes collected by ultracentrifugation were also analyzed as mediators of p53 expression in cancer cells and APJ expression in fibroblasts. APJ expression in fibroblasts co-cultured with p53-suppressed colon cancer cells (HCT116sh p53 cells) was significantly lower than in control colon cancer cells (HCT116sh control cells). APJ-suppressed fibroblasts treated with an antagonist or small interfering RNA showed myofibroblast-like properties, including increased proliferation and migratory abilities, via accelerated phosphorylation of Sma- and Mad-related protein 2/3 (Smad2/3). In addition, xenografts of HCT116 cells with APJ-suppressed fibroblasts showed accelerated tumor growth. By contrast, apelin suppressed the upregulation of phosphorylated Smad2/3 in fibroblasts. MicroRNA 5703 enriched in exosomes derived from HCT116sh p53 cells inhibited APJ expression, and inhibition of miR-5703 diminished APJ suppression in fibroblasts caused by cancer cells. APJ suppression from a specific microRNA in cancer cell-derived exosomes induced CAF-like properties in fibroblasts. Thus, the APJ system in fibroblasts in the tumor microenvironment may be a promising therapeutic target.Saiki H., Hayashi Y., Yoshii S., et al. The apelin‑apelin receptor signaling pathway in fibroblasts is involved in tumor growth via p53 expression of cancer cells. International Journal of Oncology 63, 139 (2023); https://doi.org/10.3892/ijo.2023.5587

Cardiac metastasis from colon cancer effectively treated with 5-fluorouracil, leucovorin, and oxaliplatin (modified FOLFOX6) plus panitumumab: a case report

BACKGROUND: Cardiac metastasis from colorectal cancer is rare. There is little evidence supporting the effectiveness of chemotherapy, and standard therapy for metastatic cardiac tumors has not been established. CASE PRESENTATION: A 76-year-old woman presented with a right ventricle tumor that was detected incidentally on screening cardiac ultrasonography. The initial computed tomography (CT) scan showed the cardiac tumor, which was approximately 40 mm in size, and multiple pulmonary nodules. Serum levels of tumor markers CEA and CA19-9 were elevated aberrantly. The suspected primary tumor, a well-differentiated adenocarcinoma of the transverse colon with wild-type KRAS was found by colonoscopy, and treatment with 5-fluorouracil, leucovorin, and oxaliplatin (modified FOLFOX6) plus panitumumab was initiated. After 4 courses of the therapy, a CT scan showed that the cardiac tumor size had markedly decreased and the pulmonary nodules had diminished. The serum levels of CEA and CA19-9 were also markedly decreased. After 12 courses of chemotherapy during 10 months of treatment, the patient continued to show a partial response, and she remained asymptomatic with continuation of the treatment through 15 courses. CONCLUSION: To the best of our knowledge, this is the first report of the efficacy of combination therapy using cytotoxic and molecular targeted agents against cardiac metastasis from colon cancer

p53 Deficiency in Colon Cancer Cells Promotes Tumor Progression Through the Modulation of Meflin in Fibroblasts

Kimura E., Hayashi Y., Nakagawa K., et al. p53 Deficiency in Colon Cancer Cells Promotes Tumor Progression Through the Modulation of Meflin in Fibroblasts. Cancer Science (2025); https://doi.org/10.1111/cas.70026.Cancer-associated fibroblasts (CAFs), a major component of the tumor microenvironment, play an important role in tumor progression. Colon cancer cells deficient in p53 activate fibroblasts and enhance fibroblast-mediated tumor growth. Meflin is a CAF marker capable of inhibiting tumor growth. In this study, we investigated the role of Meflin in fibroblasts using human cell lines (colon cancer HCT116 and fibroblasts CCD-18Co) and clinical specimens. TP53-suppressed HCT116 (HCT116ˢʰ ᵖ⁵³) cells cocultured with CCD-18Co cells showed significantly faster proliferation than HCT116ˢʰ ᶜᵒⁿᵗʳᵒˡ cells. In xenograft experiments, the volume of tumors induced by coinoculation with HCT116ˢʰ ᵖ⁵³ and CCD-18Co cells was significantly larger than that induced by HCT116ˢʰ ᶜᵒⁿᵗʳᵒˡ cells co-inoculated with CCD-18Co cells. HCT116sh p53 cells increased the levels of CAF-like phenotypic markers in CCD-18Co cells. Moreover, Meflin expression was significantly reduced in CCD-18Co cells cocultured with HCT116ˢʰ ᵖ⁵³ cells compared to that in CCD-18Co cells cocultured with HCT116ˢʰ ᶜᵒⁿᵗʳᵒˡ cells. si-RNA-mediated inhibition of Meflin activated CCD-18Co cells into tumor-promoting CAF-like cells, which significantly promoted xenograft tumor growth. Overexpression of Meflin in CCD-18Co cells using lentivirus suppressed fibroblast-mediated growth of HCT116ˢʰ ᵖ⁵³ tumor xenografts. The expression of Meflin in CCD-18Co cells was suppressed by TGF-β and enhanced by vitamin D. These results indicate that colon cancer cells deficient in p53 suppress Meflin expression in fibroblasts, which affects tumor growth by altering the properties of tumor growth-promoting CAFs. Our results suggest that targeting Meflin in fibroblasts may be a novel therapeutic strategy for colorectal cancer

A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer

The version of record of this article, first published in Journal of Gastroenterology, is available online at Publisher’s website: https://doi.org/10.1007/s00535-024-02102-1.Background: We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system. Methods: A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases). Results: The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796–0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743–0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable. Conclusions: Our AI model demonstrated a diagnostic performance equivalent to that of experts

Lamarckian GA with Genetic Supervision

The evolutionary theory advocated by Lamarck [3], focuses on the inheritance of characteristics acquired for self-adaptation to environment. In the domain of the purpose of acquiring adaptive strategies, it is important to make use of the information of experiences through adaptation. Therefore, the Lamarckian mechanism is an effective approach and is expected to augment the power of many kinds of evolving or learning algorithms. In this paper, we propose the Lamarckian Lookup-Table type Genetic Algorithm (LLT-GA). In general, the effectiveness of the characteristics useful for adaptation depends on a class or rather a landscape of problems to be applied. In order to demolish this barrier, the proposed LLT-GA is armed with a control mechanism for acquired characteristics based on a concept of Genetic Supervision. In this paper we discuss first Lamarckian effect and demonstrate that it is dependent on a landscape of a problem. Then, we develop an adaptive evaluation module for ..