Dataset Information

A chemoresistance lncRNA signature for recurrence risk stratification of colon cancer patients with chemotherapy.

ABSTRACT: Chemotherapy is considered the nonsurgical treatment of choice for colon cancer patients. However, no precise molecular markers are available to determine which patients can actually benefit from it. In this study, we identified 55 chemotherapy-specific long non-coding RNAs (lncRNAs) of colon cancer patients through a systematic assessment of lncRNA expression profiles from a public database. These were taken from multiple cohorts of colon cancer patients who had received chemotherapy, or not. Based on these data, a chemoresistance lncRNA signature, named CRLSig, was constructed and successfully applied to divide chemotherapy patients into two groups with different recurrence-free survival (RFS) rates. Gene set enrichment analysis revealed that patients with low CRLSig had more infiltrating CD8+ T cells and macrophages, while those with high CRLSig had more infiltrating natural killer T cells. KEGG pathway analysis revealed that the low CRLSig group had more activated metabolic pathways compared with those in the high CRLSig group, indicating better response to chemotherapy. Single-cell sequencing analysis revealed that stromal cells and epithelial cells had higher CRLSig. Thus, we have constructed an auxiliary prognostic tool, CRLSig, able to discriminate patients at high risk of RFS, despite having received standard adjuvant chemotherapy treatment.

SUBMITTER: Wang H

PROVIDER: S-EPMC8733234 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:PurposeGastric cancer peritoneal carcinomatosis is fatal. Delay in detection of peritoneal metastases contributes to high mortality, highlighting the need to develop biomarkers that can help identify patients at high risk for peritoneal recurrence or metastasis.Experimental designWe performed a systematic discovery and validation for the identification of peritoneal recurrence prediction and peritoneal metastasis detection biomarkers by analyzing expression profiling datasets from 249 patients with gastric cancer, followed by analysis of 426 patients from three cohorts for clinical validation.ResultsGenome-wide expression profiling identified a 12-gene panel for robust prediction of peritoneal recurrence in patients with gastric cancer (AUC = 0.95), which was successfully validated in a second dataset (AUC = 0.86). Examination of 216 specimens from a training cohort allowed us to establish a six gene-based risk-prediction model [AUC = 0.72; 95% confidence interval (CI): 0.66-0.78], which was subsequently validated in an independent cohort of 111 patients with gastric cancer (AUC = 0.76; 95% CI: 0.67-0.83). In both cohorts, combining tumor morphology and depth of invasion further improved the predictive accuracy of the prediction model (AUC = 0.84). Thereafter, we evaluated the performance of the identical six-gene panel for its ability to detect peritoneal metastasis by analyzing 210 gastric cancer specimens (prior 111 patients plus additional 99 cases), which discriminated patients with and without peritoneal metastasis (AUC = 0.72). Finally, our biomarker panel was also remarkably effective for identifying peritoneal micrometastasis (AUC = 0.72), and its diagnostic accuracy was significantly enhanced when depth of invasion was included in the model (AUC = 0.85).ConclusionsOur novel transcriptomic signature for risk stratification and identification of high-risk patients with peritoneal carcinomatosis might serve as an important clinical decision making in patients with gastric cancer.

Project description:BackgroundFollowing neoadjuvant chemotherapy for operable gastroesophageal cancer, lymph node metastasis is the only validated prognostic variable; however, within lymph node groups there is still heterogeneity with risk of relapse. We hypothesized that gene profiles from neoadjuvant chemotherapy treated resection specimens from gastroesophageal cancer patients can be used to define prognostic risk groups to identify patients at risk for relapse.Patients and methodsThe Medical Research Council Adjuvant Gastric Infusional Chemotherapy (MAGIC) trial (n = 202 with high quality RNA) samples treated with perioperative chemotherapy were profiled for a custom gastric cancer gene panel using the NanoString platform. Genes associated with overall survival (OS) were identified using penalized and standard Cox regression, followed by generation of risk scores and development of a NanoString biomarker assay to stratify patients into risk groups associated with OS. An independent dataset served as a validation cohort.ResultsRegression and clustering analysis of MAGIC patients defined a seven-Gene Signature and two risk groups with different OS [hazard ratio (HR) 5.1; P < 0.0001]. The median OS of high- and low-risk groups were 10.2 [95% confidence interval (CI) of 6.5 and 13.2 months] and 80.9 months (CI: 43.0 months and not assessable), respectively. Risk groups were independently prognostic of lymph node metastasis by multivariate analysis (HR 3.6 in node positive group, P = 0.02; HR 3.6 in high-risk group, P = 0.0002), and not prognostic in surgery only patients (n = 118; log rank P = 0.2). A validation cohort independently confirmed these findings.ConclusionsThese results suggest that gene-based risk groups can independently predict prognosis in gastroesophageal cancer patients treated with neoadjuvant chemotherapy. This signature and associated assay may help risk stratify these patients for post-surgery chemotherapy in future perioperative chemotherapy-based clinical trials.

Dataset Information

A chemoresistance lncRNA signature for recurrence risk stratification of colon cancer patients with chemotherapy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure