Multivariate logistic regression analysis of the correlation between five biomarkers and ovarian cancer in patients with intermediate-risk: A prospective cross-sectional study.

Objective: To find potential diagnostic biomarkers for ovarian cancer (OC), a prospective analysis of the expression of five biomarkers in patients with intermediate-risk and their correlation with the occurrence of OC was conducted. Method: A prospective observational study was carried out, patients who underwent surgical treatment with benign or malignant ovarian tumors in our hospital from January 2020 to February 2021 were included in this study, and a total of 263 patients were enrolled. Based on the postoperative pathological results, enrolled patients were divided into ovarian cancer group and benign tumor group (n = 135). The ovarian cancer group was further divided into a mid-stage group (n = 46) and an advanced-stage group (n = 82). The basic information of the three groups of patients was collected, the preoperative imaging data of the patients were collected to assess the lymph node metastasis, the preoperative blood samples were collected to examine cancer antigen 125 (CA125), carbohydrate antigen 19-9 (CA19-9), Neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and the postoperative pathological data were sorted and summarized. Result: The average during of disease in the advanced ovarian cancer group was 0.55 ± 0.18 years higher than the benign tumor group (0.43 ± 0.14 years), p < 0.001. In the advanced ovarian cancer group, the ratio of patients with the tumor, node, metastasis (TNM) stage IV (64.63%), with tumor Grade stage II and III (93.90%), and without lymph node metastasis (64.63%) was respectively more than that in the mid-stage group (accordingly 0.00, 36.96, 23.91%) (p < 0.001); The ratio of patients with TNM grade III in the mid-stage group (73.91%) was more than that in the advanced group (35.37%) (p < 0.001). The levels of the five biomarkers: CA19-9, CA125, NLR, PLR, and BDNF were different among the three groups (p < 0.001). Conclusion: CA19-9, CA125, NLR, PLR, BDNF are five biomarkers related to the occurrence of ovarian cancer and are risk factors for it. These five biomarkers and their Combined-Value may be suitable to apply in the diagnosis and the identification of ovarian cancer in patients with intermediate-risk.

Energy restriction causes metaphase delay and chromosome mis-segregation in cancer cells.

ATP metabolism during mitosis needs to be coordinated with numerous energy-demanding activities, especially in cancer cells whose metabolic pathways are reprogramed to sustain rapid proliferation in a nutrient-deficient environment. Although strategies targeting the energy metabolic pathways have shown therapeutic efficacy in preclinical cancer models, how normal cells and cancer cells differentially respond to energy shortage is unclear. In this study, using time-lapse microscopy, we found that cancer cells displayed unique mitotic phenotypes in a dose-dependent manner upon decreasing ATP (i.e. energy) supply. When reduction in ATP concentration was moderate, chromosome movements in mitosis were barely affected, while the metaphase-anaphase transition was significantly prolonged due to reduced tension between the sister-kinetochores, which delayed the satisfaction of the spindle assembly checkpoint. Further reduction in ATP concentration led to a decreased level of Aurora-B at the centromere, resulting in increased chromosome mis-segregation after metaphase delay. In contrast to cancer cells, ATP restriction in non-transformed cells induced cell cycle arrest in interphase, rather than causing mitotic defects. In addition, data mining of cancer patient database showed a correlation between signatures of energy production and chromosomal instability possibly resulted from mitotic defects. Together, these results reveal that energy restriction induces differential responses in normal and cancer cells, with chromosome mis-segregation only observed in cancer cells. This points to targeting energy metabolism as a potentially cancer-selective therapeutic strategy.

TWIRLS, a knowledge-mining technology, suggests a possible mechanism for the pathological changes in the human host after coronavirus infection via ACE2.

Faced with the current large-scale public health emergency, collecting, sorting, and analyzing biomedical information related to the "SARS-CoV-2" should be done as quickly as possible to gain a global perspective, which is a basic requirement for strengthening epidemic control capacity. However, for human researchers studying viruses and hosts, the vast amount of information available cannot be processed effectively and in a timely manner, particularly if our scientific understanding is also limited, which further lowers the information processing efficiency. We present TWIRLS (Topic-wise inference engine of massive biomedical literatures), a method that can deal with various scientific problems, such as liver cancer, acute myeloid leukemia, and so forth, which can automatically acquire, organize, and classify information. Additionally, this information can be combined with independent functional data sources to build an inference system via a machine-based approach, which can provide relevant knowledge to help human researchers quickly establish subject cognition and to make more effective decisions. Using TWIRLS, we automatically analyzed more than three million words in more than 14,000 literature articles in only 4 hr. We found that an important regulatory factor angiotensin-converting enzyme 2 (ACE2) may be involved in host pathological changes on binding to the coronavirus after infection. On triggering functional changes in ACE2/AT2R, the cytokine homeostasis regulation axis becomes imbalanced via the Renin-Angiotensin System and IP-10, leading to a cytokine storm. Through a preliminary analysis of blood indices of COVID-19 patients with a history of hypertension, we found that non-ARB (Angiotensin II receptor blockers) users had more symptoms of severe illness than ARB users. This suggests ARBs could potentially be used to treat acute lung injury caused by coronavirus infection.

A breast one-patient panel of heterogeneous genomes reveals genetic alterations driving DCIS into invasive lesions.

Aim: Utilize breast cancer samples in the same patient to indicate breast cancer development. Patients & methods: We performed whole-exome analysis of spatially independent ductal carcinoma in situ (DCIS) and invasive ductal carcinoma samples from the same breast. Results: In VEGF pathway, we observed two genes disrupted in DCIS, while another four (including ACTN2) mutated in invasive ductal carcinoma. When looked up TCGA database, we identified seven breast cancer patients with ACTN2 somatic mutations and observed a dramatic decrease in the overall survival time in ACTN2 mutant patients (p = 0.0182). A further finding in the TCGA database shows that breast cancer patients with ≥2 mutated genes in VEGF pathways showed worse prognosis (p = 0.0013). Conclusion: TCGA database and special case could inform each other to reveal DCIS developmental rules.

A detrimental mutation on USP40 unlocks the tumorigenesis in a rare case of lung cancer.

Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer. Previous research has shown heterogeneity in lung cancer, with the parallel existence of multiple subclones characterized by their own specific mutational landscape. The aim of our study was to gain insight into the evolutionary pattern of lung cancer by investigating the genomic heterogeneity between a nodule and its distant tumor. Luckily, we obtained nodule and tumor samples derived from surgery and a blood sample from a single patient. The samples are very unique, for tissues with the same genetic background from nodules to malignant tumors are rarely available and require precise micro-cutting. In this study, we performed whole-genome sequencing of these two samples, to identify novel candidate driver genes associated with LUAD. The nodule and tumor were found to have common significant ubiquitin-specific protease 40 (USP40) mutations, indicating an important driver role for the gene. Moreover, we also observed the two novel candidate driver genes ASCL5 and CAPNS1 in the LUAD sample. In summary, we pinpoint the predominant mutations in LUAD by WES, highlighting the substantial genetic alterations contributing to LUAD tumorigenesis. This may provide a better understanding of the clonal evolution during tumor development.