Blood analysis is crucial for early cancer screening and improving patient survival rates. However, developing an effective strategy for early cancer detection using high-throughput blood analysis is still challenging. Herein, a novel automatic super-hydrophobic platform is developed together with a deep learning (DL)-based label-free serum and surface-enhanced Raman scattering (SERS), along with an automatic high-throughput Raman spectrometer to build an effective point-of-care diagnosis system. A total of 695 high-quality serum SERS spectra are obtained from 203 healthy volunteers, 77 leukemia M5, 94 hepatitis B virus, and 321 breast cancer patients. Serum SERS signals from the normal (n = 183) and patient (n = 443) groups are used to assess the DL model, which classify them with a maximum accuracy of 100%. Furthermore, when SERS is combined with DL, it exhibits excellent diagnostic accuracy (98.6%) for the external held-out test set, indicating that this method can be used to develop a high throughput, rapid, and label-free tool for screening diseases.

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Background: Breast cancer is one of the most common tumors for women globally. Various miRNAs have been reported to play a crucial role in breast cancer, however the clinical significance of miR-1908-3p in breast cancer remains unclear. The present study aimed to explore the role of miR-1908-3p in breast cancer. Methods: The expression of miR-1908-3p was detected in 50 pairs of breast cancer tissues and adjacent normal tissues, 60 breast cancer patient serum and 60 healthy volunteer serum. The functional roles of miR-1908-3p in breast cancer cells such as proliferation, migration and invasion were evaluated using CCK8, SRB, wound healing and transwell chambers. In addition, bioinformatics tools were used to identify potential targets of miR-1908-3p. Results: The results showed that the expression of miR-1908-3p were increased in breast cancer tissues and serum compared with normal breast tissues and serum of healthy volunteers respectively. Furthermore, the young breast cancer patients and HER2-positive patients had a higher level of tissues' miR-1908-3p than elder breast cancer patients and HER2-negative patients, respectively. The young breast cancer patients had a higher level of serum miR-1908-3p than elder breast cancer patients, ROC analysis suggested that miR-1908-3p had the potential as a promising serum diagnostic biomarker of breast cancer. Up-regulation of miR-1908-3p promoted the cells proliferation, migration and invasion while knockdown of miR-1908-3p inhibited these processes in breast cancer cell MCF-7 and MDA-MB-231. The potential target genes of miR-1908-3p in breast cancer included ID4, LTBP4, GPM6B, RGMA, EFCAB1, ALX4, OSR1 and PPARA. Higher expression of these eight genes correlated with a better prognosis for breast cancer patients. Conclusions: These results suggest that miR-1908-3p may exert its oncogenic functions via suppression of these eight genes in breast cancer.

Cyclin‐D1 (CCND1) belongs to the highly conserved cyclin family whose members are characterized by abundant expression during the cell cycle. As an oncogene, high level of CCND1 was observed and related to poor prognosis and tumor recurrence in many cancers. In this study, we focused on the role of CCND1 in the clinical outcome of clear cell renal cell carcinoma (ccRCC). Gene Expression Omnibus database, The Cancer Genome Atlas database, and immunohistochemical staining were used. The mRNA and protein levels of CCND1 were significantly enhanced in ccRCC tumor tissues. However, the low level of CCND1, but not high level of CCND1, was related to poor prognosis and tumor recurrence in ccRCC. Further analysis showed that CCND1 mRNA level decreased with increasing ccRCC tumor grades and the rate of recurrence in ccRCC patients. In a nomogram model, the CCND1 mRNA level was shown to help predict ccRCC patient recurrence. CCND1 is a strong determinant for prediction of recurrence. The patients with high CCND1 level appear to have a more favorable prognosis together with more frequent low‐grade tumors and low rate of recurrence. This is the first study to investigate the prognostic roles of CCND1 in ccRCC and discovered that CCND1 had an unconventional positive impact on the clinical outcome of ccRCC patients.

Breast cancer is a malignant tumor of breast epithelial tissue. Early diagnosis and postoperative evaluation of breast cancer are critical to improve the survival rate. The current main screening methods are mammography and computerized tomography (CT), however, these methods suffer from false positives, over-diagnosis and radiation risk. Herein, unlabeled surface-enhanced Raman spectroscopy (SERS) technology combined with silver nanoparticles that was used to measure and analyze peripheral serum protein samples from patients of breast cancer for preoperation, postoperation and normal subjects. Results showed that there were significant differences in the serum protein SERS spectra among three groups due to changes in certain biochemical compositions related to breast cancer transformation. Moreover, diagnostic sensitivity, based on principal component analysis combined with linear discriminant analysis (PCA-LDA) for pre-surgery versus post-surgery, post-surgery versus normal and pre-surgery versus normal were 96.7%, 53.3%, and 100%, respectively, and the diagnostic specificities were 96.7%, 46.7%, and 96.7%, respectively. Therefore, serum protein SERS combined with PCA-LDA analysis holds promising potential as a novel strategy for early screening and postoperative evaluation of breast cancer.