Cancer cell lines are widely used in basic research to study cancer development, growth, invasion, or metastasis. They are also used for the development and screening of anticancer drugs. However, there are no clear criteria for choosing the most suitable cell lines among the wide variety of cancer cell lines commercially available for research, and the choice is often based on previously published reports. Here, we investigated the characteristics of liver cancer cell lines by analyzing the gene expression data available in the Cancer Cell Line Encyclopedia. Unsupervised clustering analysis of 28 liver cancer cell lines yielded two main clusters. One cluster showed a gene expression pattern similar to that of hepatocytes, and the other showed a pattern similar to that of fibroblasts. Analysis of hepatocellular carcinoma gene expression profiles available in The Cancer Genome Atlas showed that the gene expression patterns in most hepatoma tissues were similar to those in the hepatocyte-like cluster. With respect to liver cancer research, our findings may be useful for selecting an appropriate cell line for a specific study objective. Furthermore, our approach of utilizing a public database for comparing the properties of cell lines could be an attractive cell line selection strategy that can be applied to other fields of research.

Esophageal squamous cell carcinoma (ESCC) is a disease characterized by a high mutation rate of the TP53 gene, which plays pivotal roles in the DNA damage response (DDR) and is regulated by checkpoint kinase (CHK) 2. CHK1 is another key DDR-related protein, and its selective inhibition is suggested to be particularly sensitive to TP53-mutated cancers, because a loss of both pathways (CHK1 and/or CHK2–p53) is lethal due to the serious impairment of DDR. Such a therapeutic strategy is termed synthetic lethality. Here, we propose a novel therapeutic strategy based on synthetic lethality combining trifluridine/tipiracil and prexasertib (CHK1 inhibitor) as a treatment for ESCC. Trifluridine is a key component of the antitumor drug combination with trifluridine/tipiracil (an inhibitor of trifluridine degradation), also known as TAS-102. In this study, we demonstrate that trifluridine increases CHK1 phosphorylation in ESCC cells combined with a reduction of the S-phase ratio as well as the induction of ssDNA damage. Because CHK1 phosphorylation is considered to be induced as DDR for trifluridine-mediated DNA damage, we examined the effects of CHK1 inhibition on trifluridine treatment. Consequently, CHK1 inhibition by short hairpin RNA or treatment with the CHK1 inhibitor, prexasertib, markedly enhanced trifluridine-mediated DNA damage, represented by an increase of γH2AX expression. Moreover, the combination of trifluridine/tipiracil and CHK1 inhibition significantly suppressed tumor growth of ESCC-derived xenograft tumors. Furthermore, the combination of trifluridine and prexasertib enhanced radiosensitivity both in vitro and in vivo. Thus, the combination of trifluridine/tipiracil and a CHK1 inhibitor exhibits effective antitumor effects, suggesting a novel therapeutic strategy for ESCC.

A lack of caudal-type homeobox transcription factor 2 (CDX2) protein expression has been proposed as a prognostic biomarker for colorectal cancer (CRC). However, the relationship between CDX2 levels and the survival of patients with stage II/III CRC along with the relationship between microRNAs (miRs) and CDX2 expression are unclear. Tissue samples were collected from patients with stage II/III CRC surgically treated at Kyoto University Hospital. CDX2 expression was semi-quantitatively evaluated by immunohistochemistry (IHC). The prognostic impacts of CDX2 expression on overall survival (OS) and relapse-free survival (RFS) were evaluated by multivariable statistical analysis. The expression of miRs regulating CDX2 expression and their prognostic impacts were analyzed using The Cancer Genome Atlas Program for CRC (TCGA-CRC). Eleven of 174 CRC tissues lacked CDX2 expression. The five-year OS and RFS rates of patients with CDX2-negative CRC were significantly lower than those of CDX2-positive patients. Multivariate analysis of clinicopathological features revealed that CDX2-negative status is an independent marker of poor prognosis in stage II/III CRC. miR-9-5p was shown to regulate CDX2 expression. TCGA-CRC analysis showed that high miR-9-5p expression was significantly associated with poor patient prognosis in stage II/III CRC. In conclusion, CDX2, the post-transcriptional target of microRNA-9-5p, is a useful prognostic biomarker in patients with stage II/III CRC.

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Background: Predicting individual prognosis is required for patients with colorectal cancer in the era of precision medicine. However, this may be challenging for the conventional survival analysis such as the Cox proportional hazards model. This study aims to develop a personalized prognostic prediction that incorporates longitudinal data to improve predictions for colorectal cancer patients. Methods: Patients with advanced or recurrent colorectal cancer, who received treatment at Kyoto University Hospital between April 2015 and December 2021, were retrospectively analyzed. The Joint model is one of the dynamic prediction models. Using longitudinal clinical data, a carcinoembryonic antigen (CEA) prediction equation was developed for each patient. Additionally, a personalized prognostic prediction model was created using the Joint model. The prediction accuracy of the Joint model was compared with one of the Cox proportional hazards model. Results: Among the 1010 patients, 614 patients were enrolled. The median frequency of tumor marker measurement (per patient) was 20 times (range: 3–117 times). CEA values could be predicted accurately and the Pearson’s correlation coefficient between measured CEA and predicted CEA was 0.931. In the Joint model, the significant prognostic factors were baseline age (HR, 1.039; 95% CI, 1.025–1.054), poor-differentiated tumor (HR, 2.600; 95% CI 1.446–4.675) and log₂ (predicted CEA) (HR, 1.551; 95% CI 1.488–1.617). The areas under the curve at 2, 3, 4, and 5 were significantly higher for the Joint model than for the Cox proportional hazards model, respectively. Conclusion: The Joint model may accurately predict personalized prognosis that reflects changes in longitudinal tumor marker values.

Purpose: The coronavirus disease 2019 (COVID-19) pandemic exhibited several different waves threatening global health care. During this pandemic, medical resources were depleted. However, the kind of medical resources provided to each wave was not clarified. This study aimed to examine the characteristics of medical care provision at COVID-19 peaks in preparation for the next pandemic. Methods: Using medical insurance claim records in Japan, we examined the presence or absence of COVID-19 infection and the use of medical resources for all patients monthly by age group. Results: The wave around August 2021 with the Delta strain had the strongest impact on the working population in terms of hospital admission and respiratory support. For healthcare providers, this peak had the highest frequency of severely ill patients. In the subsequent wave, although the number of patients with COVID-19 remained high, they were predominantly older adults, with relatively fewer patients receiving intensive care. Conclusions: In future pandemics, we should refer to the wave around August 2021 as a situation of medical resource shortage resulting from the COVID-19 pandemic.