Transcriptional landscape and predictive potential of long noncoding RNAs in peritoneal recurrence of gastric cancer

Abstract

Background: Long noncoding RNAs (lncRNAs) play a critical role in gastric cancer (GC) progression and metastasis. However, research comprehensively exploring tissue-derived lncRNAs for predicting peritoneal recurrence in patients with GC remains limited. This study aims to investigate the transcriptional landscape of lncRNAs in GC with peritoneal metastasis (PM) and to develop an integrated lncRNA-based score to predict peritoneal recurrence in patients with GC after radical gastrectomy.

Methods: We analyzed the transcriptome profile of lncRNAs in paired peritoneal, primary gastric tumor, and normal tissue specimens from 12 patients with GC in the Sun Yat-sen University Cancer Center (SYSUCC) discovery cohort. Key lncRNAs were identified via interactive analysis with the TCGA database and SYSUCC validation cohort. A score model was constructed using the LASSO regression model and nomogram COX regression and evaluated using receiver operating characteristic curves. The role of lncRNAs in the PM of GC was then examined through wound healing, Transwell, 3D multicellular tumor spheroid invasion, and peritoneal cavity xenograft tumorigenicity assays in mice.

Result: Five essential lncRNAs were screened and incorporated into the PM risk score to predict peritoneal recurrence-free survival (pRFS). We developed a comprehensive, integrated nomogram score, including the PM risk score, pT, pN, and tumor size, which could effectively predict the 5-year pRFS with an Area under the curve of 0.79 (95% CI: 0.71-0.88). Subsequently, we demonstrated that one of these lncRNAs, CASC15, promoted the invasion and migration of GC cells in vitro and facilitated the PM of GC cells in vivo, initially verifying that lncRNAs contribute to the PM of GC. Mechanistic analysis demonstrated that CASC15 promoted EMT and metastasis by activating the JNK and p38 pathways.

Conclusion: A lncRNA-based integrated score was constructed in this study to predict peritoneal recurrence in patients clinically.

Keywords: Gastric cancer; Long noncoding RNA; Peritoneal recurrence; Transcriptome profiles.

Fig. 1

Identification of the lncRNA profiling for gastric cancer with peritoneal metastasis. (A) Flow diagram illustrating the study design. (B) Heatmap depicting differentially expressed lncRNAs among paired normal gastric tissue, primary gastric tumor, and PM identified from the SYSUCC discovery cohort. (C) Volcano plot displaying the log2 (fold change) of significantly differentially expressed lncRNAs between primary gastric tumor and normal gastric tissue. (D) Volcano plot displaying the log2 (fold change) of significantly differentially expressed lncRNAs between PM and normal gastric tissue. (E) Boxplots showing the expression levels of 10 lncRNAs among paired normal gastric tissue, primary gastric tumor, and peritoneal metastasis. Statistical analysis was conducted using the Mann-Whitney U test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Abbreviations: SYSUCC, Sun Yat-sen University Cancer Center; TCGA, The Cancer Genome Atlas; lncRNA, long non-coding RNA; ANT, adjacent normal tissues; GC, gastric cancer; SRCC, signet-ring-cell carcinoma; PM, peritoneal metastasis

Fig. 2

Genome-wide discovery of peritoneal metastasis-associated lncRNA. (A) Kaplan-Meier curves illustrating pRFS of patients with high and low expression levels of nine lncRNAs expression levels in the SYSUCC validation cohort using quantitative RT-PCR assays. (B) Partial likelihood deviance of seven lncRNAs were identified using the LASSO regression model, with 200-fold cross-validation applied for tuning parameter selection. (C) LASSO coefficients of lncRNAs. Each curve represents an individual lncRNA. (D) Venn diagram displaying the mRNAs co-expressed with the five lncRNAs in the TCGA cohort (Pearson correlation coefficient > 0.1). (E-F) KEGG pathway (E) and GO terms (F) enrichment analysis for mRNAs co-expressed with the five lncRNAs. Abbreviations: pRFS, peritoneal recurrence-free survival; SYSUCC, Sun Yat-sen University Cancer Center; HR, hazard ratio; CI, confidence interval

Fig. 3

Development of a PM risk score to predict peritoneal recurrence in gastric cancer. (A) Heatmap depicting the distribution of the PM risk score alongside corresponding clinicopathologic characteristics in the SYSUCC validation cohort. (B-D) Kaplan-Meier curves illustrating the probability of pRFS (B), RFS (C), and OS (D) among patients with high and low PM risk score levels. (E) Boxplot displaying the rates of peritoneal and overall recurrence, as well as death in patients with high and low PM risk score levels. (F) Heatmap illustrating the distribution of the PM risk score with corresponding clinicopathologic characteristics in the TCGA cohort. (G) Kaplan-Meier curves depicting the probability of OS among patients with high and low PM risk score levels. Abbreviations: SYSUCC, Sun Yat-sen University Cancer Center; TCGA, The Cancer Genome Atlas; PM, peritoneal metastasis; EGJ, esophagogastric junction; pRFS, peritoneal recurrence-free survival; RFS, recurrence-free survival; OS, overall survival; HR, hazard ratio; CI, confidence interval

Fig. 4

Integrated score improved the performance model for peritoneal recurrence prediction (A) An integrated score was constructed using nomogram COX regression based on PM risk score, pathological T and N stage, and tumor size in the SYSUCC validation cohort. (B) Time-dependent receiver operating characteristic (ROC) curves for 2-, 3-, and 5-year pRFS based on the integrated score. (C) Kaplan-Meier curves illustrating the probability of pRFS (left panel), RFS (middle panel), and OS (right panel) among patients with high and low integrated scores. (D) Boxplot comparing the AUC values between the integrated score and other variables. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Abbreviations: PM, peritoneal metastasis; pRFS, peritoneal recurrence-free survival; RFS, recurrence-free survival; OS, overall survival; HR, hazard ratio; CI, confidence interval; AUC, area under curve

Fig. 5

CASC15 promotes metastasis of GC cells in vitro. (A-B) Wound-healing assay illustrating the effect of CASC15 knockdown on migration ability of indicated GC cells. Left panels: representative images. Right panel: histograms of percentage migration area. Scale bar, 200 μm (C-D) Transwell assay demonstrating the effect of CASC15 knockdown on the migration (C) and invasion (D) ability of indicated GC cells. Left: representative images. Scale bar, 100 μm. Right: histograms of migratory or invasive cell numbers. (E) 3D multicellular tumor spheroids invasion assay showing the influence of CASC15 knockdown on the invasion ability of indicated GC cells. Left: representative images. Scale bar, 20 μm. Right: Quantification of the invading area normalized to the spheroid body in the control group. (F) Wound-healing assay demonstrating the effect of CASC15 overexpression on the migration ability of indicated GC cells. Left: representative images. Right: histogram of the percentage of migration area. Scale bar, 200 μm. (G) Transwell assay illustrating the effect of CASC15 overexpression on the migration (upper panel) and invasion (lower panel) ability of indicated GC cells. Left: representative images. Scale bar, 100 μm. Right: Quantification of migratory and invasive cell numbers. (H) 3D multicellular tumor spheroids invasion assay showing the effect of CASC15 overexpression on the invasion ability of indicated GC cells. Left: representative images. Scale bar, 20 μm. Right: the histogram shows the ratio of the invading area to the spheroid body normalized by the control group. The red line indicates the spheroid body and invading area. For (A-H), data represent mean ± SEM; the dot plot reflects data points from independent experiments. Statistical analysis was performed using a two-tailed unpaired Student’s t-test. Source data are provided in the Source Data file. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Fig. 6

CASC15 promotes peritoneal metastasis of GC cells in vivo. (A-C) IVIS assay depicting the effect of CASC15 knockdown on tumorigenicity in indicated GC cells (n = 5 mice). (A) Representative images of bioluminescence of each experimental group at 3 or 4 weeks. (B) Histograms showing the luminescent intensity of photons. (C) Histograms showing tumor numbers per mouse. The number of tumors was determined by counting the xenografts collected from the abdominal cavity after dissection. (D-E) IVIS assay demonstrating the effect of CASC15 overexpression on the tumorigenicity in indicated GC cells (n = 5 mice). (D) Representative images of bioluminescence of each experimental group at the 4 weeks. (E) Histogram of luminescent intensity of photons (left) and histogram of tumor numbers per mouse (right). The number of tumors was determined by counting the xenografts collected from the abdominal cavity after dissection. (F) The expression of CASC15 was detected by in situ hybridization in primary tumor and metastatic site derived from patients with peritoneal metastases of gastric cancer. Left: representative images. Scale bar, 100 μm. Right: Chart of H-score. (G) Boxplot of relative CASC15 content in plasma of patients with or without peritoneal metastases. For (A-E), data represent mean ± SEM; the dot plot reflects data points from each mouse. Statistical analysis was conducted using a two-tailed unpaired Student’s t-test. For (F), the dot plot reflects data points from each patient and the lines represent paired tissues. Statistical analysis was conducted using a paired t-test. For (G), data represent min to max; the dot plot reflects data points from each patient. Statistical analysis was conducted using a Wilcoxon test Source data are provided in the Source Data file.*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns, no significance

Fig. 7

CASC15 promotes the metastasis of GC via the p38 and JNK signaling pathways (A) RNA immuno-FISH revealing the subcellular location of CASC15 in GC cell. (B) KEGG analysis and significantly enriched pathways of changed mRNAs in CASC15-knockdown GC cells compared with control cells. (C-D) Western blotting showing the effect of CASC15 knockdown (C) and overexpression (D) on levels of the key proteins of the epithelial-mesenchymal transition (EMT) process. (E-F) Western blotting showing the effect of CASC15 knockdown (E) and overexpression (F) on levels of the key proteins of the MAPK signaling pathway. (G-H) Transwell assay demonstrating that SP600125 (20 µM 24 h) and SB203580 (15 µM 24 h) reverse the facilitating effect of CASC15 overexpression on GC cells migration and invasion. Left: representative images. Scale bar, 100 μm. Right: Quantification of migratory and invasive cell numbers. (I) Transwell assay demonstrating that anisomycin (100 nM 12 h) reverses the inhibitory effect of CASC15 knockdown on GC cell migration and invasion. Left: representative images. Scale bar, 100 μm. Right: Quantification of migratory and invasive cell numbers. For (G-I), data represent mean ± SEM from three independent experiments; the dot plot reflects data points from independent experiments. Statistical analysis was performed using a two-tailed unpaired Student’s t-test. Source data are provided in the Source Data file. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001