Development and validation of nomograms for predicting overall survival and cancer specific survival in locally advanced breast cancer patients: A SEER population-based study

Immune infiltration and prognosis in gastric cancer: role of NAD+ metabolism-related markers

Background

This study endeavored to develop a nicotinamide adenine dinucleotide (NAD+) metabolism-related biomarkers in gastric cancer (GC), which could provide a theoretical foundation for prognosis and therapy of GC patients.

Methods

In this study, differentially expressed genes (DEGs1) between GC and paraneoplastic tissues were overlapped with NAD+ metabolism-related genes (NMRGs) to identify differentially expressed NMRGs (DE-NMRGs). Then, GC patients were divided into high and low score groups by gene set variation analysis (GSVA) algorithm for differential expression analysis to obtain DEGs2, which was overlapped with DEGs1 for identification of intersection genes. These genes were further analyzed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses to obtain prognostic genes for constructing a risk model. Enrichment and immune infiltration analyses further investigated investigate the different risk groups, and qRT-PCR validated the prognostic genes.

Results

Initially, we identified DE-NMRGs involved in NAD biosynthesis, with seven (DNAJB13, CST2, THPO, CIDEA, ONECUT1, UPK1B and SNCG) showing prognostic significance in GC. Subsequent, a prognostic model was constructed in which the risk score, derived from the expression profiles of these genes, along with gender, emerged as robust independent predictors of patient outcomes in GC. Enrichment analysis linked high-risk patients to synaptic membrane pathways and low-risk to the CMG complex pathway. Tumor immune infiltration analysis revealed correlations between risk scores and immune cell abundance, suggesting a relationship between NAD+ metabolism and immune response in GC. The prognostic significance of our identified genes was validated by qRT-PCR, which confirmed their upregulated expression in GC tissue samples.

Conclusion

In this study, seven NAD+ metabolism-related markers were established, which is of great significance for the development of prognostic molecular biomarkers and clinical prognosis prediction for gastric cancer patients.

Development and validation of a nomogram for predicting lymph node metastasis in early gastric cancer

BACKGROUND

Lymph node metastasis (LNM) significantly impacts the treatment and prognosis of early gastric cancer (EGC). Consequently, the precise prediction of LNM risk in EGC patients is essential to guide the selection of appropriate surgical approaches in clinical settings.

AIM

To develop a novel nomogram risk model for predicting LNM in EGC patients, utilizing preoperative clinicopathological data.

METHODS

Univariate and multivariate logistic regression analyses were performed to examine the correlation between clinicopathological factors and LNM in EGC patients. Additionally, univariate Kaplan-Meier and multivariate Cox regression analyses were used to assess the influence of clinical factors on EGC prognosis. A predictive model in the form of a nomogram was developed, and its discrimination ability and calibration were also assessed.

RESULTS

The incidence of LNM in the study cohort was 19.6%. Multivariate logistic regression identified tumor size, location, degree of differentiation, and pathological type as independent risk factors for LNM in EGC patients. Both tumor pathological type and LNM independently affected the prognosis of EGC. The model's performance was reflected by an area under the curve of 0.750 [95% confidence interval (CI): 0.701-0.789] for the training group and 0.763 (95%CI: 0.687-0.838) for the validation group.

CONCLUSION

A clinical prediction model was constructed (using tumor size, low differentiation, location in the middle-lower region, and signet ring cell carcinoma), with its score being a significant prognosis indicator.

Development and validation of nomograms for predicting survival in small cell lung cancer patients with brain metastases: a SEER population-based analysis

OBJECTIVE

To develop prognostic nomograms for overall survival (OS) and cancer-specific survival (CSS) probabilities in small cell lung cancer (SCLC) patients with brain metastasis (BM).

METHODS

SCLC patients with BM from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2015) were randomly allocated to training (n=1771) and validation (n=757) cohorts. Independent prognostic factors for OS and CSS were determined using univariate and multivariate Cox regression analyses in the training cohort, and prognostic nomograms for OS and CSS were constructed based on these factors. The efficacy of the nomograms was assessed using area under the receiver operating characteristic (ROC) curves (AUCs), calibration curves, decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI), with the TNM staging model as a comparator.

RESULTS

Multivariate Cox analysis identified age, sex, race, tumor size, N staging, and presence of liver/bone/lung metastases, chemotherapy, and radiotherapy as independent prognostic factors for both OS and CSS. Prognostic nomograms were developed based on these factors. In both the training and validation cohorts, the AUC values of the nomograms for OS and CSS were significantly above 0.7, surpassing those for TNM staging. Calibration curves demonstrated a high degree of concordance between predicted and actual survival. The constructed nomograms showed superior clinical utility compared to the TNM staging system, as evidenced by NRI, IDI, and DCA.

CONCLUSIONS

This retrospective study successfully developed and validated prognostic nomograms for SCLC patients with BM, providing valuable tools for oncologists to enhance prognosis evaluation and guide clinical decision-making.

A nomogram based on platelet-to-lymphocyte ratio for predicting pathological complete response of breast cancer after neoadjuvant chemotherapy

OBJECTIVE

To investigate the role of platelet-to-lymphocyte ratio (PLR) in complete pathological response (pCR) of breast cancer (BC) patients after neoadjuvant chemotherapy (NAC), as well as to establish and validate a nomogram for predicting pCR.

METHODS

BC patients diagnosed and treated in the First Affiliated Hospital of Xi'an Jiaotong University from January 2019 to June 2022 were included. The correlation between pCR and clinicopathological characteristics was analyzed by Chi-square test. Logistic regression analysis was performed to evaluate the factors that might affect pCR. Based on the results of regression analysis, a nomogram for predicting pCR was established and validated.

RESULTS

A total of 112 BC patients were included in this study. 50.89% of the patients acquired pCR after NAC. Chi-square test showed that PLR was significantly correlated with pCR (X2 = 18.878, P < 0.001). And the PLR before NAC in pCR group was lower than that in Non-pCR group (t = 3.290, P = 0.001). Logistic regression analysis suggested that white blood cell (WBC) [odds ratio (OR): 0.19, 95% confidence interval (CI): 0.04-0.85, P = 0.030)], platelet (PLT) (OR: 0.19, 95%CI: 0.04-0.85, P = 0.030), PLR (OR: 0.18, 95%CI: 0.04-0.90, P = 0.036) and tumor grade (OR: 9.24, 95%CI: 1.89-45.07, P = 0.006) were independent predictors of pCR after NAC. A nomogram prediction model based on WBC, PLR, PLR and tumor grade showed a good predictive ability.

CONCLUSION

PLR, PLT, WBC and tumor grade were independent predictors of pCR in BC patients after NAC. The nomogram based on the above positive factors showed a good predictive ability.

Prognostic Significance of Cuproptosis-Related Gene Signatures in Breast Cancer Based on Transcriptomic Data Analysis

Breast cancer (BRCA) remains a serious threat to women's health, with the rapidly increasing morbidity and mortality being possibly due to a lack of a sophisticated classification system. To date, no reliable biomarker is available to predict prognosis. Cuproptosis has been recently identified as a new form of programmed cell death, characterized by the accumulation of copper in cells. However, little is known about the role of cuproptosis in breast cancer. In this study, a cuproptosis-related genes (CRGs) risk model was constructed, based on transcriptomic data with corresponding clinical information relating to breast cancer obtained from both the TCGA and GEO databases, to assess the prognosis of breast cancer by comprehensive bioinformatics analyses. The CRGs risk model was constructed and validated based on the expression of four genes (NLRP3, LIPT1, PDHA1 and DLST). BRCA patients were then divided into two subtypes according to the CRGs risk model. Furthermore, our analyses revealed that the application of this risk model was significantly associated with clinical outcome, immune infiltrates and tumor mutation burden (TMB) in breast cancer patients. Additionally, a new clinical nomogram model based on risk score was established and showed great performance in overall survival (OS) prediction, confirming the potential clinical significance of the CRGs risk model. Collectively, our findings revealed that the CRGs risk model can be a useful tool to stratify subtypes and that the cuproptosis-related signature plays an important role in predicting prognosis in BRCA patients.