An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Breast Cancer

Penalized estimation for varying coefficient additive hazards models

Varying coefficient models are commonly used to capture intricate interaction effects among covariates in regression models, allowing for the modification of one covariate's effect by another. Although these models offer increased flexibility, they also introduce greater estimation and computational complexity as a trade-off. This complexity is particularly evident in genomic studies, where the covariates are often high-dimensional, rendering conventional estimation methods inapplicable. In this paper, we study a penalized estimation method for the varying coefficient additive hazards model. We adopt the group lasso penalty along with the kernel smoothing technique to estimate the varying coefficients. In contrast to existing kernel methods, which only use a "local" neighborhood of subjects to estimate the varying coefficient function at any given point, the proposed method takes a "global" approach that incorporates all subjects and is more efficient. Through extensive simulation studies, we demonstrate that the proposed method produces interpretable results with satisfactory predictive performance. We provide an application to a major cancer genomic study.

Cell Populations in Human Breast Cancers are Molecularly and Biologically Distinct with Age

Aging is associated with increased breast cancer risk and outcomes are worse for the oldest and youngest patients, regardless of subtype. It is not known how cells in the breast tumor microenvironment are impacted by age and how they might contribute to age-related disease pathology. Here, we discover age-associated differences in cell states and interactions in human estrogen receptor-positive (ER+) and triple-negative breast cancers (TNBC) using new computational analyses of existing single-cell gene expression data. Age-specific program enrichment (ASPEN) analysis reveals age-related changes, including increased tumor cell epithelial-mesenchymal transition, cancer-associated fibroblast inflammatory responses, and T cell stress responses and apoptosis in TNBC. ER+ breast cancer is dominated by increased cancer cell estrogen receptor 1 (ESR1) and luminal cell activity, reduced immune cell metabolism, and decreased vascular and extracellular matrix (ECM) remodeling with age. Cell interactome analysis reveals candidate signaling pathways that drive many of these cell states. This work lays a foundation for discovery of age-adapted therapeutic interventions for breast cancer.

Bioinformatics identification and validation of aging‑related molecular subtype and prognostic signature in breast cancer

Patients with metastatic breast cancer have a poor clinical outcome, accounting for more than 90 percent of breast cancer-related deaths. Aging could regulate many biological processes in malignancies by regulating cell senescence. The role of aging has not been fully clarified. Consensus cluster analysis was performed to differentiate The Cancer Genome Atlas (TCGA) breast cancer cases. Least absolute shrinkage and selection operator (LASSO) cox regression analysis was performed to construct an aging-related prognostic signature. A total of 118 differentially expressed aging-related genes (ARGs) was obtained in breast cancer. Consensus clustering analysis identified 3 categories of TCGA-breast cancer with significant difference in prognosis and immune infiltration. We also constructed an aging-related prognostic signature for breast cancer, which had a good performance in predicting the 1-year, 3-year and 5-year OS and disease specific survival (DSS) of breast cancer patients. Further single gene analysis revealed that the expression of PIK3R1 was significantly different in different pT and pN stages of breast cancer. Moreover, low expression of PIK3R1 showed resistance to many drugs based on the data of Genomics of Drug Sensitivity in Cancer (GDSC) and Genomics of Therapeutics Response Portal (CTRP). PIK3R1 played a vital role in many well-known cancer-related pathways. The current study identified 3 clusters of TCGA-breast cancer cases with significant differences in prognosis and immune infiltration. We also constructed an aging-related prognostic signature for breast cancer. However, further in vivo and in vitro studies should be conducted to verify these results.

Bioinformatics identification and validation of aging-related molecular subtype and prognostic signature in sarcoma

Aging could regulate many biological processes in malignancies by regulating cell senescence. Consensus cluster analysis was conducted to differentiate TCGA sarcoma cases. LASSO cox regression analysis was performed to construct an aging-related prognostic signature. We identified two categories of TCGA-sarcoma with significant difference in prognosis, immune infiltration and chemotherapy and targeted therapy. Moreover, an aging-related prognostic signature was constructed for sarcoma, which had a good performance in predicting the 3-year and 5-year overall survival of sarcoma patients. We also identified a lncRNA MALAT1/miR-508-3p/CCNA2 regulatory axis for sarcoma. This stratification could provide more evidence for estimating prognosis and immunotherapy of sarcoma.

Construction and validation of an aging-related gene signature for prognosis prediction of patients with breast cancer

BACKGROUND

Breast cancer (BC) is an aging-related disease. Aging-related genes (ARGs) participate in the initiation and development of lung and colon cancer, but the prognosis signature of ARGs in BC has not been clearly studied.

AIMS

This study aimed to construct an ARGs signature to predict the prognosis of patients with breast cancer.

METHOD

Firstly, the expression data of ARGs from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were collected. Then COX and least absolulute shrinkage and selection operator(LASSO) were performed to construct the ARGs prognostic signature. The correlation between the signature and immune cell infiltration, immunotherapeutic response and drug sensitivity were subsequently analysed. The TCGA nomogram was constructed by combining the signature with other clinical features, and was validated by using GEO database.

RESULTS

After LASSO and COX regression analyses, a prognostic signature based on nine ARGs, namely, HSP90AA1, NFKB2, PLAU, PTK2, RECQL4, CLU, JAK2, MAP3K5, and S100B, was built by using the TCGA dataset. Moreover, this risk signature is closely related to immune cell infiltration, immunotherapeutic response, and responses to chemotherapy and targeted therapy. Subsequently, The calibration curve demonstrates that the nomogram agrees well with practical prediction results. The receiver operating characteristic curve and decision-making curve analysis demonstrate that ARG signature has the better prognosis diagnosis ability and clinical net benefits.

CONCLUSIONS

Therefore, the proposed ARG prognosis signature is a new prognosis molecular marker of patients with BC, and it can provide good references to individual clinical therapy.

An Aging and Senescence-Related Gene Signature for Prognosis Prediction in Clear Cell Renal Cell Carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is the most common solid lesion in the kidney. This study aims to establish an aging and senescence-related mRNA model for risk assessment and prognosis prediction in ccRCC patients.

Methods: ccRCC data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. By applying univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, a new prognostic model based on aging and senescence-related genes (ASRGs) was established. Depending on the prognostic model, high- and low-risk groups were identified for further study. The reliability of the prediction was evaluated in the validation cohort. Pan-cancer analysis was conducted to explore the role of GNRH1 in tumors.

Results: A novel prognostic model was established based on eight ASRGs. This model was an independent risk factor and significantly correlated with the prognosis and clinicopathological features of ccRCC patients. The high- and low-risk groups exhibited distinct modes in the principal component analysis and different patterns in immune infiltration. Moreover, the nomogram combining risk score and other clinical factors showed excellent predictive ability, with AUC values for predicting 1-, 3-, and 5-year overall survival in the TCGA cohort equal to 0.88, 0.82, and 0.81, respectively.

Conclusion: The model and nomogram based on the eight ASRGs had a significant value for survival prediction and risk assessment for ccRCC patients, providing new insights into the roles of aging and senescence in ccRCC.

A Novel Inflammatory-Nutritional Prognostic Scoring System for Patients with Early-Stage Breast Cancer

PURPOSE

We attempted to explore the prognostic value of baseline inflammatory and nutritional biomarkers at diagnosis in patients with early-stage breast cancer and develop a novel scoring system, the inflammatory-nutritional prognostic score (INPS).

PATIENTS AND METHODS

We collected clinicopathological and baseline laboratory data of 1259 patients with early-stage breast cancer between December 2010 and November 2012 from Sun Yat-sen University Cancer Center. Eligible patients were randomly divided into training and validation cohorts (n = 883 and 376, respectively) in a 7:3 ratio. We selected the most valuable biomarkers to develop INPS by the least absolute shrinkage and selection operator (LASSO) Cox regression model. A prognostic nomogram incorporating INPS and other independent clinicopathological factors was developed based on the stepwise multivariate Cox regression method. Then, we used the concordance index (C-index), calibration plot, and time-dependent receiver operating characteristic (ROC) analysis to evaluate the prognostic performance and predictive accuracy of the predictive nomogram.

RESULTS

Four inflammatory-nutritional biomarkers, including neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), prognostic nutritional index (PNI), and albumin-alkaline phosphatase ratio (AAPR), were selected using the LASSO Cox analysis to construct INPS, which remained an independent prognostic indicator per the multivariate Cox regression analysis. Patients were stratified into low- and high-INPS groups based on the cutoff INPS determined by the maximally selected rank statistics. The prognostic model for overall survival consisting of INPS and other independent clinicopathological indicators showed excellent discrimination with C-indexes of 0.825 (95% confidence interval [CI]: 0.786-0.864) and 0.740 (95% CI: 0.657-0.822) in the training and validation cohorts, respectively. The time-dependent ROC curves showed a higher predictive accuracy of our prognostic nomogram than that of traditional tumor-node-metastasis staging.

CONCLUSION

Baseline INPS is an independent indicator of OS in patients with early-stage breast cancer. The INPS-based prognostic nomogram could be used as a practical tool for individualized prognostic predictions.