The modified Glasgow prognostic score (mGPS), based on C‑reactive protein and albumin levels, is an inflammation‑based prognostic tool used in various cancers. However, related research in breast cancer is limited. The present study evaluated the prognostic value of the preoperative mGPS in predicting overall survival (OS) of patients with breast cancer undergoing surgery. A retrospective cohort study was conducted involving 300 patients with breast cancer with up to 10 years of follow‑up. Patients were categorized into three groups based on mGPS scores of 0, 1 and 2, and their clinical and pathological data were collected. Kaplan‑Meier survival analysis and Cox proportional hazards models were used to assess survival outcomes and identify risk factors associated with higher mGPS scores. A prognostic nomogram was developed based on multivariate analysis to predict 5‑ and 10‑year OS. Patients with high mGPS scores showed significantly poor survival outcomes. The 5‑ and 10‑year survival rates for mGPS 0, 1 and 2 were 80, 70 and 55%, and 71, 55 and 22%, respectively (P<0.001). Multivariate Cox analysis identified the mGPS, age, smoking, PAM50 and TNM stage as independent predictors of OS. The nomogram based on the mGPS demonstrated good predictive accuracy (concordance index: 0.81) and calibration. The preoperative mGPS is an independent prognostic factor for OS of patients with breast cancer. It is a simple, cost‑effective tool that can aid in risk stratification and guide treatment strategies. Further validation in larger cohorts is recommended.

GINS subunits, a protein complex composed of GINS1, GINS2, GINS3 and GINS4 in the human genome and the expression level of each GINS subunits plays an important role in different human cancers. As one of the most common malignancies after lung cancer in the world, precise biomarkers for early diagnosis and treatment in breast cancer are important. The purpose of our study was to elucidate the expression and prognostic value of GINS subunits in breast cancer. The purpose of present study was to explore the expression level of GINS subunits in breast cancer patients. In the present study, we investigated the gene alteration, gene expression and potential prognostic value of GINS subunits by using the Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, cBioPortal, and bc-GenExMiner databases. Then, the GeneMANIA database was used to show the genes that associated with GINS subunits. Furthermore, gene ontology pathway analysis was conducted by using the Metascape database. Finally, immune infiltration analysis in GINS subunits were evaluated using the Tumor Immune Estimation Resource (TIMER) database. Our analyses demonstrated that the expression levels of different GINS subunits were different between breast cancer and normal breast tissues. The expression levels of GINS1, GINS2, and GINS4 were significantly higher in breast cancer tissues than in normal tissues. Survival analysis revealed that increased the expression levels of GINS subunits were associated with poor prognoses in all patients with breast cancer. Gene ontology pathway enrichment analysis of the GINS subunits suggested that GINS subunits involved in pathways including the cell cycle checkpoint, DNA replication and other meaningful signaling pathways. We systemically analyzed the expression, prognostic, clinicopathologic values, and potential functional networks of GINS subunits in breast cancer. Our findings showed that individual GINS subunits could be new potential prognostic biomarkers for breast cancer. However, further verification studies are still needed to prove the clinical value of GINS subunits in breast cancer patients.

The diagnosis of cervical lymph node metastasis from thyroid cancer is an essential stage in the progression of thyroid cancer. The metastasis of cervical lymph nodes directly affects the prognosis and survival rate of patients. Therefore, timely and early diagnosis is crucial for effective treatment and can significantly improve patients’ survival rate and quality of life. Traditional diagnostic methods, such as ultrasonography and radionuclide scanning, have limitations, such as complex operations and high missed diagnosis rates. Raman spectroscopy and FTIR spectroscopy can well reflect the molecular information of samples, have characteristics such as sensitivity and specificity, and are simple to operate. They have been widely used in clinical research in recent years. With the development of intelligent medical diagnosis technology, medical data shows a multi-modal trend. Compared with single-modal data, multi-modal data fusion can achieve complementary information, provide more comprehensive and valuable diagnostic information, significantly enhance the richness of data features, and improve the modeling effect of the model, helping to achieve better results. Accurate disease diagnosis. Existing research mostly uses cascade processing, ignoring the important correlations between multi-modal data, and at the same time not making full use of the intra-modal relationships that are also beneficial to prediction. We developed a new multi-modal separation cross-fusion network (MSCNet) based on deep learning technology. This network fully captures the complementary information between and within modalities through the feature separation module and feature cross-fusion module and effectively integrates Raman spectrum and FTIR spectrum data to diagnose thyroid cancer cervical lymph node metastasis accurately. The test results on the serum vibrational spectrum data set of 99 cases of cervical lymph node metastasis showed that the accuracy and AUC of a single Raman spectrum reached 63.63% and 63.78% respectively, and the accuracy and AUC of a single FTIR spectrum reached 95.84% respectively and 96%. The accuracy and AUC of Raman spectroscopy combined with FTIR spectroscopy reached 97.95% and 98% respectively, which is better than existing diagnostic technology. The omics correlation verification obtained correlation pairs of 5 Raman frequency shifts and 84 infrared spectral bands. This study provides new ideas and methods for the early diagnosis of cervical lymph node metastasis of thyroid cancer.

Introduction. Clinicopathologic and prognostic significance of body mass index (BMI) in breast cancer (BC) patients remained conflicting. We aimed to investigate and modify the impact of BMI on clinicopathological significance and survival in western Chinese BC patients.Materials and Methods. 8,394 female BC patients from Western China Clinical Cooperation Group (WCCCG) between 2005 and 2015 were identified. Multivariable logistic regression and Cox proportion hazard regressions were used to examine the difference of clinicopathologic and survival characteristics between BMI categories.Results. For the premenopausal, overweight and obese (OW) patients tended to have large tumor size (>5cm) (odds ratio [OR], 1.30, P5cm) (OR, 1.46; P=0.01), nuclear grade III (OR, 1.24; P=0.04), and lymphovascular invasion (OR, 1.46; P=0.01) compared with NW patients. An “U” shaped relationship between BMI and DFS was found (UW versus NW, adjusted hazard ratio (HR), 2.80, P<0.001; OW versus NW, adjusted HR, 1.40, P=0.02), whereas no significant difference of disease-free survival (DFS) between OW and NW premenopausal patients (adjusted HR=1.34, P=0.18) was revealed.Conclusion. We concluded that UW and OW were associated with aggressively clinicopathological characteristics, regardless of menopausal status. An “U” shaped association of BMI and DFS was revealed, and no significant difference of DFS between OW and NW in postmenopausal subgroup was revealed.