Integration of gene interaction information into a reweighted Lasso-Cox model for accurate survival prediction

Establishment of a nomogram based on Lasso Cox regression for albumin combined with systemic immune-inflammation index score to predict prognosis in advanced pancreatic carcinoma

Purpose

The study aims to establish a nomogram to predict advanced pancreatic carcinoma patients' overall survival (OS), incorporating albumin combined with systemic immune-inflammation index (A-SII) score and clinical characteristics.

Methods

A retrospective study analyzed the clinical data of 205 advanced pancreatic carcinoma patients without antitumor treatment from the Yancheng No.1 People's Hospital between October 2011 and June 2023, and the study divided patients into the training set and the validation set randomly at the proportion of three to one. The A-SII score was divided into scores of 0, 1, and 2 according to the different levels of albumin and SII. Receiver operating characteristic (ROC) curves and time-dependent area under the curve were used to evaluate the predictive ability of the A-SII score. The nomogram1 and nomogram2 were established by the multivariate Cox regression and Lasso Cox regression respectively. The study evaluated the discriminability of nomogram1 and nomogram2 based on C-index and ROC curves to obtain the optimal model. Subsequently, we plotted decision curve analyses (DCA) and calibration curves to estimate the clinical benefit and accuracy of nomogram2.

Results

Lasso Cox regression showed that A-SII score, number of organ metastases, tumor size, chemotherapy, targeted therapy, Neutrophil-to-albumin ratio, and lactate dehydrogenase were independent prognostic factors for the OS of advanced pancreatic carcinoma patients. The C-index and ROC curve of the nomogram2 are better than the nomogram1. Subsequently, the DCA and calibration curve of the nomogram2 demonstrate excellent performance.

Conclusion

The nomogram based on the A-SII score and other independent prognostic factors determined by Lasso Cox regression can accurately predict the OS of patients suffering from advanced pancreatic carcinoma.

Identifying high-risk candidates for prolonging progression-free survival in primary gastric carcinoma subject to "double invasion": an analytical approach utilizing lasso-cox regression

OBJECTIVE

To identify high-risk gastric carcinoma patients with concurrent vascular and neural invasion ("double invasion") who are at heightened risk of progression-free survival (PFS) decline, enabling personalized clinical management.

METHODS

In this multi-center retrospective study, 559 patients with double invasion who underwent curative gastrectomy between May 2002 and December 2020 were analyzed. Prognostic factors for PFS were identified using Lasso-Cox regression. Model validation included internal bootstrapping, calibration plots, and comparison against the American Joint Committee on Cancer(AJCC) 8th edition TNM staging system via Harrell's C-index, decision curve analysis (DCA), and time-dependent receiver operating characteristic (ROC) curves.

RESULTS

The nomogram integrated gender, positive lymph node count, surgical gastrectomy method, PTEN/FHIT expression levels, and maximum tumor diameter. It demonstrated superior predictive accuracy to AJCC staging, with a C-index of 0.651 (95% CI: 0.612-0.691) versus 0.543 (95% CI: 0.517-0.569). Calibration plots showed strong agreement between predicted and observed outcomes. The area under the curve(AUC) for 3- and 5-year PFS predictions were 0.719 (95% CI: 0.655-0.771) and 0.767 (95% CI: 0.670-0.841), respectively. DCA confirmed clinical utility across decision thresholds, and risk stratification effectively differentiated low- and high-risk groups. In the training cohort, the model significantly outperformed AJCC staging (NRI: 0.218, p < 0.01; IDI: 0.085, p < 0.01). However, this superiority was not statistically significant in the validation cohort (NRI: 0.141, p = 0.08; IDI: 0.031, p = 0.239).

CONCLUSION

We developed a Lasso-Cox regression-based nomogram to stratify PFS risk in gastric carcinoma patients with double invasion. While the model outperformed AJCC staging in training, validation cohort results highlight the need for further refinement. This tool holds potential for guiding tailored therapeutic strategies, though broader validation is warranted to confirm clinical applicability.

Multi-modality risk prediction of cardiovascular diseases for breast cancer cohort in the All of Us Research Program

OBJECTIVE

This study leverages the rich diversity of the All of Us Research Program (All of Us)'s dataset to devise a predictive model for cardiovascular disease (CVD) in breast cancer (BC) survivors. Central to this endeavor is the creation of a robust data integration pipeline that synthesizes electronic health records (EHRs), patient surveys, and genomic data, while upholding fairness across demographic variables.

MATERIALS AND METHODS

We have developed a universal data wrangling pipeline to process and merge heterogeneous data sources of the All of Us dataset, address missingness and variance in data, and align disparate data modalities into a coherent framework for analysis. Utilizing a composite feature set including EHR, lifestyle, and social determinants of health (SDoH) data, we then employed Adaptive Lasso and Random Forest regression models to predict 6 CVD outcomes. The models were evaluated using the c-index and time-dependent Area Under the Receiver Operating Characteristic Curve over a 10-year period.

RESULTS

The Adaptive Lasso model showed consistent performance across most CVD outcomes, while the Random Forest model excelled particularly in predicting outcomes like transient ischemic attack when incorporating the full multi-model feature set. Feature importance analysis revealed age and previous coronary events as dominant predictors across CVD outcomes, with SDoH clustering labels highlighting the nuanced impact of social factors.

DISCUSSION

The development of both Cox-based predictive model and Random Forest Regression model represents the extensive application of the All of Us, in integrating EHR and patient surveys to enhance precision medicine. And the inclusion of SDoH clustering labels revealed the significant impact of sociobehavioral factors on patient outcomes, emphasizing the importance of comprehensive health determinants in predictive models. Despite these advancements, limitations include the exclusion of genetic data, broad categorization of CVD conditions, and the need for fairness analyses to ensure equitable model performance across diverse populations. Future work should refine clinical and social variable measurements, incorporate advanced imputation techniques, and explore additional predictive algorithms to enhance model precision and fairness.

CONCLUSION

This study demonstrates the liability of the All of Us's diverse dataset in developing a multi-modality predictive model for CVD in BC survivors risk stratification in oncological survivorship. The data integration pipeline and subsequent predictive models establish a methodological foundation for future research into personalized healthcare.

A fatty acid metabolism-related genes model for predicting the prognosis and immunotherapy effect of lung adenocarcinoma

ObjectiveLung adenocarcinoma (LUAD) is a common and highly heterogeneous malignancy cancer with increasing morbidity and mortality. Dysregulation of fatty acid metabolism (FAM) has been identified as a key regulator of LUAD progression. Our purpose was to establish a risk model of FAM-related genes to provide a reference for the prognosis prediction of LUAD.MethodsFirstly, we screened FAM-related differentially expressed genes (DEGs) based on the Cancer Genome Atlas (TCGA) database, and identified the prognostic signatures by Cox-regression analysis. The least absolute shrinkage and selection operator algorithm (LASSO) was used to obtain the formula for risk model. And the analysis of Gene Expression Omnibus (GEO) dataset used to verify. Nomogram was produced for individualized prediction in clinical treatment. Immune cell function and drug sensitivity analysis used to screen potential therapeutic drugs.ResultsPatients in low-risk had better overall survival (OS). High-risk patients exhibit higher TMB and lower TIDE scores, and they are more likely to benefit from immunotherapy. The analysis of GEO verified that risk model has a high prediction accuracy.ConclusionThe risk model based on 17 FAM-related DEGs is of great value in predicting the prognosis of LUAD, and these prognostic signatures may be potential therapeutic targets for LUAD.

Tissue-resident memory T cell signatures from single-cell analysis associated with better melanoma prognosis

Tissue-resident memory T cells (TRM) are a specialized T cell population residing in peripheral tissues. The presence and potential impact of TRM in the tumor immune microenvironment (TIME) remain to be elucidated. Here, we systematically investigated the relationship between TRM and melanoma TIME based on multiple clinical single-cell RNA-seq datasets and developed signatures indicative of TRM infiltration. TRM infiltration is associated with longer overall survival and abundance of T cells, NK cells, M1 macrophages, and memory B cells in the TIME. A 22-gene TRM-derived risk score was further developed to effectively classify patients into low- and high-risk categories, distinguishing overall survival and immune activation, particularly in T cell-mediated responses. Altogether, our analysis suggests that TRM abundance is associated with melanoma TIME activation and patient survival, and the TRM-based machine learning model can potentially predict prognosis in melanoma patients.

Dynamic Survival Risk Prognostic Model and Genomic Landscape for Atypical Teratoid/Rhabdoid Tumors: A Population-Based, Real-World Study

BACKGROUND

An atypical teratoid/rhabdoid tumor (AT/RT) is an uncommon and aggressive pediatric central nervous system neoplasm. However, a universal clinical consensus or reliable prognostic evaluation system for this malignancy is lacking. Our study aimed to develop a risk model based on comprehensive clinical data to assist in clinical decision-making.

METHODS

We conducted a retrospective study by examining data from the Surveillance, Epidemiology, and End Results (SEER) repository, spanning 2000 to 2019. The external validation cohort was sourced from the Children's Hospital Affiliated to Chongqing Medical University, China. To discern independent factors affecting overall survival (OS) and cancer-specific survival (CSS), we applied Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression analyses. Based on these factors, we structured nomogram survival predictions and initiated a dynamic online risk-evaluation system. To contrast survival outcomes among diverse treatments, we used propensity score matching (PSM) methodology. Molecular data with the most common mutations in AT/RT were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.

RESULTS

The annual incidence of AT/RT showed an increasing trend (APC, 2.86%; 95% CI:0.75-5.01). Our prognostic study included 316 SEER database participants and 27 external validation patients. The entire group had a median OS of 18 months (range 11.5 to 24 months) and median CSS of 21 months (range 11.7 to 29.2). Evaluations involving C-statistics, DCA, and ROC analysis underscored the distinctive capabilities of our prediction model. An analysis via PSM highlighted that individuals undergoing triple therapy (integrating surgery, radiotherapy, and chemotherapy) had discernibly enhanced OS and CSS. The most common mutations of AT/RT identified in the COSMIC database were SMARCB1, BRAF, SMARCA4, NF2, and NRAS.

CONCLUSIONS

In this study, we devised a predictive model that effectively gauges the prognosis of AT/RT and briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges.

Tutorial on survival modeling with applications to omics data

MOTIVATION

Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics datasets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes.

RESULTS

We present a general workflow for survival analysis that is applicable to high-dimensional omics data as inputs when identifying survival-associated features and validating survival models. In particular, we focus on the commonly used Cox-type penalized regressions and hierarchical Bayesian models for feature selection in survival analysis, which are especially useful for high-dimensional data, but the framework is applicable more generally.

AVAILABILITY AND IMPLEMENTATION

A step-by-step R tutorial using The Cancer Genome Atlas survival and omics data for the execution and evaluation of survival models has been made available at https://ocbe-uio.github.io/survomics.

Development and validation of questionnaire-based machine learning models for predicting all-cause mortality in a representative population of China

Background

Considering that the previously developed mortality prediction models have limited applications to the Chinese population, a questionnaire-based prediction model is of great importance for its accuracy and convenience in clinical practice.

Methods

Two national cohort, namely, the China Health and Nutrition Survey (8,355 individual older than 18) and the China Health and Retirement Longitudinal Study (12,711 individuals older than 45) were used for model development and validation. One hundred and fifty-nine variables were compiled to generate predictions. The Cox regression model and six machine learning (ML) models were used to predict all-cause mortality. Finally, a simple questionnaire-based ML prediction model was developed using the best algorithm and validated.

Results

In the internal validation set, all the ML models performed better than the traditional Cox model in predicting 6-year mortality and the random survival forest (RSF) model performed best. The questionnaire-based ML model, which only included 20 variables, achieved a C-index of 0.86 (95%CI: 0.80-0.92). On external validation, the simple questionnaire-based model achieved a C-index of 0.82 (95%CI: 0.77-0.87), 0.77 (95%CI: 0.75-0.79), and 0.79 (95%CI: 0.77-0.81), respectively, in predicting 2-, 9-, and 11-year mortality.

Conclusions

In this prospective population-based study, a model based on the RSF analysis performed best among all models. Furthermore, there was no significant difference between the prediction performance of the questionnaire-based ML model, which only included 20 variables, and that of the model with all variables (including laboratory variables). The simple questionnaire-based ML prediction model, which needs to be further explored, is of great importance for its accuracy and suitability to the Chinese general population.

Joint learning sample similarity and correlation representation for cancer survival prediction

BACKGROUND

As a highly aggressive disease, cancer has been becoming the leading death cause around the world. Accurate prediction of the survival expectancy for cancer patients is significant, which can help clinicians make appropriate therapeutic schemes. With the high-throughput sequencing technology becoming more and more cost-effective, integrating multi-type genome-wide data has been a promising method in cancer survival prediction. Based on these genomic data, some data-integration methods for cancer survival prediction have been proposed. However, existing methods fail to simultaneously utilize feature information and structure information of multi-type genome-wide data.

RESULTS

We propose a Multi-type Data Joint Learning (MDJL) approach based on multi-type genome-wide data, which comprehensively exploits feature information and structure information. Specifically, MDJL exploits correlation representations between any two data types by cross-correlation calculation for learning discriminant features. Moreover, based on the learned multiple correlation representations, MDJL constructs sample similarity matrices for capturing global and local structures across different data types. With the learned discriminant representation matrix and fused similarity matrix, MDJL constructs graph convolutional network with Cox loss for survival prediction.

CONCLUSIONS

Experimental results demonstrate that our approach substantially outperforms established integrative methods and is effective for cancer survival prediction.

Immunogenic cell death-related classifications in breast cancer identify precise immunotherapy biomarkers and enable prognostic stratification

Background: Immunogenic cell death (ICD) remodels the tumor immune microenvironment, plays an inherent role in tumor cell apoptosis, and promotes durable protective antitumor immunity. Currently, appropriate biomarker-based ICD immunotherapy for breast cancer (BC) is under active exploration. Methods: To determine the potential link between ICD genes and the clinical risk of BC, TCGA-BC was used as the training set and GSE58812 was used as the validation set. Gene expression, consistent clustering, enrichment analysis, and mutation omics analyses were performed to analyze the potential biological pathways of ICD genes involved in BC. Furthermore, a risk and prognosis model of ICD was constructed to evaluate the correlation between risk grade and immune infiltration, clinical stage, and survival prognosis. Results: We identified two ICD-related subtypes by consistent clustering and found that the C2 subtype was associated with good survival prognosis, abundant immune cell infiltration, and high activity of immune biological processes. Based on this, we constructed and validated an ICD risk and prognosis model of BC, including ATG5, HSP90AA1, PIK3CA, EIF2AK3, MYD88, IL1R1, and CD8A. This model can effectively predict the survival rate of patients with BC and is negatively correlated with the immune microenvironment and clinical stage. Conclusion: This study provides new insights into the role of ICD in BC. The novel classification risk model based on ICD in BC established in this study can aid in estimating the potential prognosis of patients with BC and the clinical outcomes of immunotherapy and postulates targets that are more useful in comprehensive treatment strategies.

Correlation between Ferroptosis-Related Gene Signature and Immune Landscape: Prognosis in Breast Cancer

Breast cancer (BC) is the most commonly diagnosed cancer and second leading cause of cancer-related death in women worldwide. Ferroptosis, an iron-dependent newly discovered mode of cell death, can be induced by lenaltinib and plays an important role in the biological behaviors of BC. Therefore, the prognostic value of ferroptosis-related genes (FRGs) in BC warrants further investigation. FRG expression profiles and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO). Immune-related pathways were found in the functional analysis. Significant differences in enrichment scores for immune cells were observed. Some patients from TCGA-BRCA were included as the training cohort. A six-gene prediction signature was constructed with the least absolute shrinkage and selection operator Cox regression. This model was validated in the rest of the TCGA-BRCA and GEO cohort. The expressions of the six FRGs were verified with real-time quantitative polymerase chain reaction and immunohistochemistry in the Human Protein Atlas. Relapse or metastasis was more likely in the high-risk group. Risk score was an independent predictor of disease-free survival. Collectively, the ferroptosis-related risk model established in this study may serve as an effective tool to predict the prognosis in BC.

Establishment of predictive nomogram and web-based survival risk calculator for malignant pleural mesothelioma: A SEER database analysis

Background

Malignant pleural mesothelioma (MPM) is an uncommon condition with limited available therapies and dismal prognoses. The purpose of this work was to create a multivariate clinical prognostic nomogram and a web-based survival risk calculator to forecast patients’ prognoses.

Methods

Using a randomization process, training and validation groups were created for a retrospective cohort study that examined the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 for individuals diagnosed with MPM (7:3 ratio). Overall survival (OS) and cancer-specific survival (CSS) were the primary endpoints. Clinical traits linked to OS and CSS were identified using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, which was also utilized to develop nomogram survival models and online survival risk calculators. By charting the receiver operating characteristic (ROC), consistency index (C-index), calibration curve, and decision curve analysis (DCA), the model’s performance was assessed. The nomogram was used to classify patients into various risk categories, and the Kaplan-Meier method was used to examine each risk group’s survival rate.

Results

The prognostic model comprised a total of 1978 patients. For the total group, the median OS and CSS were 10 (9.4-10.5) and 11 (9.4-12.6) months, respectively. As independent factors for OS and CSS, age, gender, insurance, histology, T stage, M stage, surgery, and chemotherapy were chosen. The calibration graphs demonstrated good concordance. In the training and validation groups, the C-indices for OS and CSS were 0.729, 0.717, 0.711, and 0.721, respectively. Our nomogram produced a greater clinical net benefit than the AJCC 7th edition, according to DCA and ROC analysis. According to the cut-off values of 171 for OS and 189 for CSS of the total scores from our nomogram, patients were classified into two risk groups. The P-value &lt; 0.001 on the Kaplan-Meier plot revealed a significant difference in survival between the two patient groups.

Conclusions

Patient survival in MPM was correctly predicted by the risk evaluation model. This will support clinicians in the practice of individualized medicine.

Integrating Radiomics with Genomics for Non-Small Cell Lung Cancer Survival Analysis

Purpose

The objectives of our study were to assess the association of radiological imaging and gene expression with patient outcomes in non-small cell lung cancer (NSCLC) and construct a nomogram by combining selected radiomic, genomic, and clinical risk factors to improve the performance of the risk model.

Methods

A total of 116 cases of NSCLC with CT images, gene expression, and clinical factors were studied, wherein 87 patients were used as the training cohort, and 29 patients were used as an independent testing cohort. Handcrafted radiomic features and deep-learning genomic features were extracted and selected from CT images and gene expression analysis, respectively. Two risk scores were calculated through Cox regression models for each patient based on radiomic features and genomic features to predict overall survival (OS). Finally, a fusion survival model was constructed by incorporating these two risk scores and clinical factors.

Results

The fusion model that combined CT images, gene expression data, and clinical factors effectively stratified patients into low- and high-risk groups. The C-indexes for OS prediction were 0.85 and 0.736 in the training and testing cohorts, respectively, which was better than that based on unimodal data.

Conclusions

Combining radiomics and genomics can effectively improve OS prediction for NSCLC patients.

Ferroptosis-Related lncRNA Signature Correlates with the Prognosis, Tumor Microenvironment, and Therapeutic Sensitivity of Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is the most prevalent form of esophageal cancer in China and is closely associated with malignant biological characteristics and poor survival. Ferroptosis is a newly discovered iron-dependent mode of cell death that plays an important role in the biological behavior of ESCC cells. The clinical significance of ferroptosis-related long noncoding RNAs (FRLs) in ESCC remains unknown and warrants further research. The current study obtained RNA sequencing profiles and corresponding clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and FRLs were obtained through coexpression analysis. Consensus clustering was employed to divide the subjects into clusters, and immune-associated pathways were identified by functional analysis. The current study observed significant differences in the enrichment scores of immune cells among different clusters. Patients from TCGA-ESCC database were designated as the training cohort. A ten-FRL prediction signature was established using the least absolute shrinkage and selection operator Cox regression model and validated using the GEO cohort and our own independent validation database. Real-time quantitative polymerase chain reaction was used to verify the expression of the ten FRLs, and the ssGSEA analysis was employed to evaluate their function. In addition, the IMvigor database was used to assess the predictive value of the signature in terms of immunotherapeutic responses. Multivariate Cox and stratification analyses revealed that the ten-FRL signature was an independent predictor of the overall survival (OS). Patients with ESCC in the high-risk group displayed worse survival, a characteristic tumor immune microenvironment, and low immunotherapeutic benefits compared to those in the low-risk group. Collectively, the risk model established in this study could serve as a promising predictor of prognosis and immunotherapeutic response in patients with ESCC.

Integrative Analysis Constructs an Extracellular Matrix-Associated Gene Signature for the Prediction of Survival and Tumor Immunity in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) accounts for the majority of lung cancers, and the survival of patients with advanced LUAD is poor. The extracellular matrix (ECM) is a fundamental component of the tumor microenvironment (TME) that determines the oncogenesis and antitumor immunity of solid tumors. However, the prognostic value of extracellular matrix-related genes (ERGs) in LUAD remains unexplored. Therefore, this study is aimed to explore the prognostic value of ERGs in LUAD and establish a classification system to predict the survival of patients with LUAD.

Methods: LUAD samples from The Cancer Genome Atlas (TCGA) and GSE37745 were used as discovery and validation cohorts, respectively. Prognostic ERGs were identified by univariate Cox analysis and used to construct a prognostic signature by Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. The extracellular matrix-related score (ECMRS) of each patient was calculated according to the prognostic signature and used to classify patients into high- and low-risk groups. The prognostic performance of the signature was evaluated using Kaplan–Meier curves, Cox regression analyses, and ROC curves. The relationship between ECMRS and tumor immunity was determined using stepwise analyses. A nomogram based on the signature was established for the convenience of use in the clinical practice. The prognostic genes were validated in multiple databases and clinical specimens by qRT-PCR.

Results: A prognostic signature based on eight ERGs (FERMT1, CTSV, CPS1, ENTPD2, SERPINB5, ITGA8, ADAMTS8, and LYPD3) was constructed. Patients with higher ECMRS had poorer survival, lower immune scores, and higher tumor purity in both the discovery and validation cohorts. The predictive power of the signature was independent of the clinicopathological parameters, and the nomogram could also predict survival precisely.

Conclusions: We constructed an ECM-related gene signature which can be used to predict survival and tumor immunity in patients with LUAD. This signature can serve as a novel prognostic indicator and therapeutic target in LUAD.

Anticolon Cancer Targets and Molecular Mechanisms of Tao-He-Cheng-Qi Formula

Background

Tao-He-Cheng-Qi Formula (THCQF) is a traditional Chinese medicine that has been proven to have antitumor effects. The aim of this study was to elucidate the molecular targets and mechanisms of THCQF against colon cancer and construct a prognostic model based on network pharmacology, bioinformatics analysis, and in vitro experiments.

Methods

Potential THCQF compounds and targets were retrieved from the Traditional Chinese Medicine Systems Pharmacology and Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine databases. Differentially expressed genes for colon cancer were screened in The Cancer Genome Atlas and Gene Expression Omnibus databases. The anticolon cancer mechanisms of THCQF were explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking simulations and molecular dynamics analysis were used to evaluate the binding between target proteins and active compounds. Finally, the identified compounds were used to treat colon cancer cells from the HCT116 cell line, and expression of mRNA and protein after relevant posttreatment were tested using real-time polymerase chain reaction and western blotting.

Results

A total of 27 anticolon cancer targets of THCQF were selected, among which four genes (CCNB1, CCNA2, IL1A, and MMP3) were shown to effectively predict patient outcomes in a prognostic colon cancer model. GO and KEGG enrichment analyses indicated that the activity against colon cancer of THCQF was associated with the interleukin (IL)-4 and IL-3 signaling pathways. Two compounds in THCQF, aloe emodin (AE) and quercetin (QR), were shown to efficiently bind to cyclin B1, the protein encoded by CCNB1. Finally, incubation of HCT116 cells with AE and QR significantly decreased CCNB1 mRNA expression and cyclin B1 levels.

Conclusions

Taken together, the results indicate that AE and QR are the pivotal active compounds of THCQF, and CCNB1 is the main molecular target through which THCQF exerts its anticolon cancer effects. The study findings provide insight for studies investigating the anticancer effects of other traditional Chinese medicines.

Development and Validation of a Robust Ferroptosis-Related Gene Panel for Breast Cancer Disease-Specific Survival

Background: New biomarker combinations have been increasingly developed to improve the precision of current diagnostic and therapeutic modalities. Recently, researchers have found that tumor cells are more vulnerable to ferroptosis. Furthermore, ferroptosis-related genes (FRG) are promising therapeutic targets in breast cancer patients. Therefore, this study aimed to identify FRG that could predict disease-specific survival (DSS) in breast cancer patients. Methods: Gene expression matrix and clinical data were downloaded from public databases. We included 960, 1,900, and 234 patients from the TCGA, METABRIC, and GSE3494 cohorts, respectively. Data for FRG were downloaded from the FerrDb website. Differential expression of FRG was analyzed by comparing the tumors with adjacent normal tissues. Univariate Cox analysis of DSS was performed to identify prognostic FRG. The TCGA-BRCA cohort was used to generate a nine-gene panel with the LASSO cox regression. The METABRIC and GSE3494 cohorts were used to validate the panel. The panel's median cut-off value was used to divide the patients into high- or low-risk subgroups. Analyses of immune microenvironment, functional pathways, and clinical correlation were conducted via GO and KEGG analyses to determine the differences between the two subgroups. Results: The DSS of the low-risk subgroup was longer than that of the high-risk subgroup. The panel's predictive ability was confirmed by ROC curves (TCGA cohort AUC values were 0.806, 0.695, and 0.669 for 2, 3, and 5 years respectively, and the METABRIC cohort AUC values were 0.706, 0.734, and 0.7, respectively for the same periods). The panel was an independent DSS prognostic indicator in the Cox regression analyses. (TCGA cohort: HR = 3.51, 95% CI = 1.792-6.875, p < 0.001; METABRIC cohort: HR = 1.76, 95% CI = 1.283-2.413, p < 0.001). Immune-related pathways were enriched in the high-risk subgroup. The two subgroups that were stratified by the nine-gene panel were also associated with histology type, tumor grade, TNM stage, and Her2-positive and TNBC subtypes. The patients in the high-risk subgroup, whose CTLA4 and PD-1 statuses were both positive or negative, demonstrated a substantial clinical benefit from combination therapy with anti-CTLA4 and anti-PD-1. Conclusion: The new gene panel consisting of nine FRG may be used to assess the prognosis and immune status of patients with breast cancer. A precise therapeutic approach can also be possible with risk stratification.

PCLasso: a protein complex-based, group lasso-Cox model for accurate prognosis and risk protein complex discovery

For high-dimensional expression data, most prognostic models perform feature selection based on individual genes, which usually lead to unstable prognosis, and the identified risk genes are inherently insufficient in revealing complex molecular mechanisms. Since most genes carry out cellular functions by forming protein complexes-basic representatives of functional modules, identifying risk protein complexes may greatly improve our understanding of disease biology. Coupled with the fact that protein complexes have been shown to have innate resistance to batch effects and are effective predictors of disease phenotypes, constructing prognostic models and selecting features with protein complexes as the basic unit should improve the robustness and biological interpretability of the model. Here, we propose a protein complex-based, group lasso-Cox model (PCLasso) to predict patient prognosis and identify risk protein complexes. Experiments on three cancer types have proved that PCLasso has better prognostic performance than prognostic models based on individual genes. The resulting risk protein complexes not only contain individual risk genes but also incorporate close partners that synergize with them, which may promote the revealing of molecular mechanisms related to cancer progression from a comprehensive perspective. Furthermore, a pan-cancer prognostic analysis was performed to identify risk protein complexes of 19 cancer types, which may provide novel potential targets for cancer research.