Pan-cancer evaluation of gene expression and somatic alteration data for cancer prognosis prediction

Prognosis of lasso-like penalized Cox models with tumor profiling improves prediction over clinical data alone and benefits from bi-dimensional pre-screening

BACKGROUND

Prediction of patient survival from tumor molecular '-omics' data is a key step toward personalized medicine. Cox models performed on RNA profiling datasets are popular for clinical outcome predictions. But these models are applied in the context of "high dimension", as the number p of covariates (gene expressions) greatly exceeds the number n of patients and e of events. Thus, pre-screening together with penalization methods are widely used for dimensional reduction.

METHODS

In the present paper, (i) we benchmark the performance of the lasso penalization and three variants (i.e., ridge, elastic net, adaptive elastic net) on 16 cancers from TCGA after pre-screening, (ii) we propose a bi-dimensional pre-screening procedure based on both gene variability and p-values from single variable Cox models to predict survival, and (iii) we compare our results with iterative sure independence screening (ISIS).

RESULTS

First, we show that integration of mRNA-seq data with clinical data improves predictions over clinical data alone. Second, our bi-dimensional pre-screening procedure can only improve, in moderation, the C-index and/or the integrated Brier score, while excluding irrelevant genes for prediction. We demonstrate that the different penalization methods reached comparable prediction performances, with slight differences among datasets. Finally, we provide advice in the case of multi-omics data integration.

CONCLUSIONS

Tumor profiles convey more prognostic information than clinical variables such as stage for many cancer subtypes. Lasso and Ridge penalizations perform similarly than Elastic Net penalizations for Cox models in high-dimension. Pre-screening of the top 200 genes in term of single variable Cox model p-values is a practical way to reduce dimension, which may be particularly useful when integrating multi-omics.

Construction and assessment of a joint prediction model and nomogram for colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most common tumors in the digestive system, and all its risk factors are not yet known. It is important to identify valuable clinical indicators to predict the risk of CRC.

METHODS

A total of 227 participants, comprising 162 healthy adults and 65 patients diagnosed with CRC at Tianjin Hospital from January 2017 to March 2022, were included in this study. Electrochemiluminescence was adopted to test the expression levels of carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA199). Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for CRC, and a joint prediction model was then constructed. A nomogram was prepared, and the model was later assessed using the receiver operating characteristic curve and calibration curve.

RESULTS

The univariate analysis showed that there were statistically significant differences between the two groups in terms of smoking (χ²=8.67), fecal occult blood (χ²=119.41), Helicobacter pylori (H. pylori) infection (χ²=30.87), a history of appendectomy (χ²=5.47), serum total bile acid levels (t=19.80), serum CEA levels (t=37.82), serum CA199 levels (t=6.82), and serum ferritin levels (t=54.31) (all P<0.05). The multiple logistic regression analysis showed that smoking, fecal occult blood, H. pylori infection, a history of appendectomy, serum CEA levels, and serum CA199 levels were independent risk factors for CRC (all P<0.05). Based on the above findings, a joint prediction model was constructed, and the area under the receiver operator characteristic (ROC) curve of the model was 0.842. A nomogram and calibration curve was drawn, and the internal validation results indicated that the model had good diagnostic value.

CONCLUSIONS

Smoking, fecal occult blood, H. pylori infection, a history of appendectomy, serum CEA levels, and serum CA199 levels are independent risk factors for CRC, and the prediction model based on these factors had good predictive ability.

Clinical significance of cyclin-dependent kinase inhibitor 2C expression in cancers: from small cell lung carcinoma to pan-cancers

Background

Cyclin-dependent kinase inhibitor 2C (CDKN2C) was identified to participate in the occurrence and development of multiple cancers; however, its roles in small cell lung carcinoma (SCLC) remain unclear.

Methods

Differential expression analysis of CDKN2C between SCLC and non-SCLC were performed based on 937 samples from multiple centers. The prognosis effects of CDKN2C in patients with SCLC were detected using both Kaplan–Meier curves and log-rank tests. Using receiver-operating characteristic curves, whether CDKN2C expression made it feasible to distinguish SCLC was determined. The potential mechanisms of CDKN2C in SCLC were investigated by gene ontology terms and signaling pathways (Kyoto Encyclopedia of Genes and Genomes). Based on 10,080 samples, a pan-cancer analysis was also performed to determine the roles of CDKN2C in multiple cancers.

Results

For the first time, upregulated CDKN2C expression was detected in SCLC samples at both the mRNA and protein levels (p of Wilcoxon rank-sum test < 0.05; standardized mean difference = 2.86 [95% CI 2.20–3.52]). Transcription factor FOXA1 expression may positively regulate CDKN2C expression levels in SCLC. High CDKN2C expression levels were related to the poor prognosis of patients with SCLC (hazard ratio > 1, p < 0.05) and showed pronounced effects for distinguishing SCLC from non-SCLC (sensitivity, specificity, and area under the curve ≥ 0.95). CDKN2C expression may play a role in the development of SCLC by affecting the cell cycle. Furthermore, the first pan-cancer analysis revealed the differential expression of CDKN2C in 16 cancers (breast invasive carcinoma, etc.) and its independent prognostic significance in nine cancers (e.g., adrenocortical carcinoma). CDKN2C expression was related to the immune microenvironment, suggesting its potential usefulness as a prognostic marker in immunotherapy.

Conclusions

This study identified upregulated CDKN2C expression and its clinical significance in SCLC and other multiple cancers, suggesting its potential usefulness as a biomarker in treating and differentiating cancers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02036-5.