Construction of a prognostic 6-gene signature for breast cancer based on multi-omics and single-cell data.

Background: Breast cancer (BC) is one of the females' most common malignant tumors there are large individual differences in its prognosis. We intended to uncover novel useful genetic biomarkers and a risk signature for BC to aid determining clinical strategies. Methods: A combined significance (p combined) was calculated for each gene by Fisher's method based on the RNA-seq, CNV, and DNA methylation data from TCGA-BRCA. Genes with a p combined< 0.01 were subjected to univariate cox and Lasso regression, whereby an RS signature was established. The predicted performance of the RS signature would be assessed in GSE7390 and GSE20685, and emphatically analyzed in triple-negative breast cancer (TNBC) patients, while the expression of immune checkpoints and drug sensitivity were also examined. GSE176078, a single-cell dataset, was used to validate the differences in cellular composition in tumors between TNBC patients with different RS. Results: The RS signature consisted of C15orf52, C1orf228, CEL, FUZ, PAK6, and SIRPG showed good performance. It could distinguish the prognosis of patients well, even stratified by disease stages or subtypes and also showed a stronger predictive ability than traditional clinical indicators. The down-regulated expressions of many immune checkpoints, while the decreased sensitivity of many antitumor drugs was observed in TNBC patients with higher RS. The overall cells and lymphocytes composition differed between patients with different RS, which could facilitate a more personalized treatment. Conclusion: The six genes RS signature established based on multi-omics data exhibited well performance in predicting the prognosis of BC patients, regardless of disease stages or subtypes. Contributing to a more personalized treatment, our signature might benefit the outcome of BC patients.

Prognosis of recurrence after complete resection in early-stage lung adenocarcinoma based on molecular alterations: a systematic review and meta-analysis.

Molecular biomarkers have the potential to predict the recurrence risk of early-stage lung adenocarcinoma (LUAD) after complete resection, but the study results are controversial. We aimed to clarify the association of molecular alterations with disease-free survival (DFS) and recurrence-free survival (RFS) in early-stage LUAD with R0 resection. Comprehensive searches were conducted in PubMed/MEDLINE, Web of Science, and Cochrane Library for this systematic review and meta-analysis with date restrictions from 2012 to 2022. In the 18 included studies, data from a total of 7417 participants in 11 studies and 4167 participants in 9 studies were collected for the EGFR and KRAS meta-analyses, respectively. Two studies were assessed as having a moderate risk of bias, and the others were all assessed as having a high individual risk of bias. The molecular alterations in KRAS rather than EGFR, were associated with a high risk of recurrence for early-stage LUAD patients suffering from R0 resection, especially for those in pStage I, the pooled hazard ratios (HRs) of KRAS were 2.71 (95% CI, 1.81-4.06; I2 = 22%; P < 0.00001) and 1.95 (95% CI, 1.25-3.20; I2 = 57%; P = 0.003) with small interstudy heterogeneity in univariate and multivariate analyses, respectively. This finding suggests that molecular alterations in KRAS that could be detected by polymerase chain reaction techniques would provide new insight into stratifying risk and personalizing patient postoperative follow-up.

A novel prognostic signature contributes to precision treatment in colon adenocarcinoma with KRAS mutation.

BACKGROUND: Approximately 40% of colon cancer harbor Kirsten rat sarcoma viral oncogene ( KRAS ) mutations, but the prognostic value of KRAS mutations in colon cancer is still controversial. METHODS: We enrolled 412 colon adenocarcinoma (COAD) patients with KRAS mutations, 644 COAD patients with KRAS wild-type and 357 COAD patients lacking information on KRAS status from five independent cohorts. A random forest model was developed to estimate the KRAS status. The prognostic signature was established using least absolute shrinkage and selection operator-Cox regression and evaluated by Kaplan-Meier survival analysis, multivariate-Cox analysis, receiver operating characteristic curve and nomogram. The expression data of KRAS -mutant COAD cell lines from the Cancer Cell Line Encyclopedia database and the corresponding drug sensitivity data from the Genomics of Drug Sensitivity in Cancer database were used for potential target and agent exploration. RESULTS: We established a 36-gene prognostic signature classifying the KRAS -mutant COAD as high and low risk. High risk patients had inferior prognoses compared to those with low risk, while the signature failed to distinguish the prognosis of COAD with KRAS wild-type. The risk score was the independent prognostic factor for KRAS -mutant COAD and we further fabricated the nomograms with good predictive efficiency. Moreover, we suggested FMNL1 as a potential drug target and three drugs as potential therapeutic agents for KRAS -mutant COAD with high risk. CONCLUSION: We established a precise 36-gene prognostic signature with great performance in prognosis prediction of KRAS -mutant COAD providing a new strategy for personalized prognosis management and precision treatment for KRAS -mutant COAD.

Development and validation of a prognostic 9-gene signature for colorectal cancer.

Introduction: Colorectal cancer (CRC) is one of the most prevalent cancers globally with a high mortality rate. Predicting prognosis using disease progression and cancer pathologic stage is insufficient, and a prognostic factor that can accurately evaluate patient prognosis needs to be developed. In this study, we aimed to infer a prognostic gene signature to identify a functional signature associated with the prognosis of CRC patients. Methods: First, we used univariate Cox regression, least absolute shrinkage and selection operator (lasso) regression, and multivariate Cox regression analyses to screen genes significantly associated with CRC patient prognosis, from colorectal cancer RNA sequencing data in The Cancer Genome Atlas (TCGA) database. We then calculated the risk score (RS) for each patient based on the expression of the nine candidate genes and developed a prognostic signature. Results: Based on the optimal cut-off on the receiver operating characteristic (ROC) curve, patients were separated into high- and low-risk groups, and the difference in overall survival between the two groups was examined. Patients in the low-risk group had a better overall survival rate than those in the high-risk group. The results were validated using the GSE72970, GSE39582, and GSE17536 Gene Expression Omnibus (GEO) datasets, and the same conclusions were reached. ROC curve test of the RS signature also indicated that it had excellent accuracy. The RS signature was then compared with traditional clinical factors as a prognostic indicator, and we discovered that the RS signature had superior predictive ability. Conclusion: The RS signature developed in this study has excellent predictive power for the prognosis of patients with CRC and broad applicability as a prognostic indicator for patients.