Identification of genes associated with gastric cancer survival and construction of a nomogram to improve risk stratification for patients with gastric cancer

Screening high-risk individuals for primary gastric adenocarcinoma: evaluating progression-free survival probability score in the presence and absence of Rictor expression after gastrectomy

Objective

Developing nomogram-based risk stratification model to determine 3-year and 5-year progression-free survival (PFS) and to identify high-risk patients with gastric adenocarcinoma based on different Rictor statuses.

Methods

1366 individuals who underwent radical gastric surgery to treat gastric adenocarcinoma at Shanxi Cancer Hospital from May 2002 to December 2020 were analyzed. Cox regression analysis was employed to create the nomograms. The nomograms' performance was assessed using C-index, time receiver operating characteristic (t-ROC) curves, calibration curves, and decision curve analysis (DCA) curves in training and validation cohorts. Subsequently, patients were categorized into high-risk and low-risk groups based on the nomogram's risk scores.

Results

The Rictor (-) nomogram for predicting PFS included variables such as age, number of positive lymph nodes, vascular invasion, maximum diameter of the tumor, omentum metastasis, and expression of MSH2. In the internal validation, the C-index of the Rictor (-) nomogram was 0.760 (95%CI: 0.720-0.799), which was superior to the C-index of the American Joint Committee on Cancer (AJCC) 8th edition TNM staging (0.683, 95%CI: 0.646-0.721). Similarly, the Rictor (+) nomogram for predicting PFS included variables such as gender, age, pT stage, number of positive lymph nodes, neural invasion, maximum diameter of the tumor, omentum metastasis, Clavien-Dindo classification for complications, and CGA expression. The C-index of the Rictor (+) nomogram was 0.795 (95%CI: 0.764-0.825), which outperformed the C-index of the AJCC 8th edition TNM staging (0.693, 95%CI: 0.662-0.723). The calibration curves, t-ROC curves, and decision curve analysis for both nomogram models demonstrated their excellent prediction ability.

Conclusion

This study presents the first risk stratification for Rictor status in gastric adenocarcinoma. Our model identifies low-risk patients who may not require additional postoperative treatment, while high-risk patients should consider targeted therapies that specifically target Rictor-positive indicators.

The role of prickle proteins in vertebrate development and pathology

Prickle is an evolutionarily conserved family of proteins exclusively associated with planar cell polarity (PCP) signalling. This signalling pathway provides directional and positional cues to eukaryotic cells along the plane of an epithelial sheet, orthogonal to both apicobasal and left-right axes. Through studies in the fruit fly Drosophila, we have learned that PCP signalling is manifested by the spatial segregation of two protein complexes, namely Prickle/Vangl and Frizzled/Dishevelled. While Vangl, Frizzled, and Dishevelled proteins have been extensively studied, Prickle has been largely neglected. This is likely because its role in vertebrate development and pathologies is still being explored and is not yet fully understood. The current review aims to address this gap by summarizing our current knowledge on vertebrate Prickle proteins and to cover their broad versatility. Accumulating evidence suggests that Prickle is involved in many developmental events, contributes to homeostasis, and can cause diseases when its expression and signalling properties are deregulated. This review highlights the importance of Prickle in vertebrate development, discusses the implications of Prickle-dependent signalling in pathology, and points out the blind spots or potential links regarding Prickle, which could be studied further.

Genes associated with diagnosis and prognosis of Burkitt lymphoma

Burkitt lymphoma (BL) is one of the most aggressive forms of non-Hodgkin's lymphomas that affect children and young adults. The expression of genes and other molecular markers during carcinogenesis can be the basis for diagnosis, prognosis and the design of new and effective drugs for the management of cancers. The aim of this study was to identify genes that can serve as prognostic and therapeutic targets for BL. We analysed RNA-seq data of BL transcriptome sequencing projects in Africa using standard RNA-seq analyses pipeline. We performed pathway enrichment analyses, protein-protein interaction networks, gene co-expression and survival analyses. Gene and pathway enrichment analyses showed that the differentially expressed genes are involved in tube development, signalling receptor binding, viral protein interaction, cell migration, external stimuli response, serine hydrolase activity and PI3K-Akt signalling pathway. Protein-protein interaction network analyses revealed the genes to be highly interconnected, whereas module analyses revealed 25 genes to possess the highest interaction score. Overall survival analyses delineated six genes (ADAMTSL4, SEMA5B, ADAMTS15, THBS2, SPON1 and THBS1) that can serve as biomarkers for prognosis for BL management.

Establishment of a Prognostic Signature of Stromal/Immune-Related Genes for Gastric Adenocarcinoma Based on ESTIMATE Algorithm

Different subtypes of gastric cancer differentially respond to immune checkpoint inhibitors (ICI). This study aimed to investigate whether the Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm is related to the classification and prognosis of gastric cancer and to establish an ESTIMATE-based gene signature to predict the prognosis for patients. The immune/stromal scores of 388 gastric cancer patients from TCGA were used in this analysis. The upregulated differentially expressed genes (DEGs) in patients with high stromal/immune scores were identified. The immune-related hub DEGs were selected based on protein-protein interaction (PPI) analysis. The prognostic values of the hub DEGs were evaluated in the TCGA dataset and validated in the GSE15460 dataset using the Kaplan-Meier curves. A prognostic signature was built using the hub DEGs by Cox proportional hazards model, and the accuracy was assessed using receiver operating characteristic (ROC) analysis. Different subtypes of gastric cancer had significantly different immune/stromal scores. High stromal scores but not immune scores were significantly associated with short overall survivals of TCGA patients. Nine hub DEGs were identified in PPI analysisThe expression of these hub DEG negatively correlated with the overall survival in the TCGA cohort, which was validated in the GSE15460 cohort. A 9-gene prognostic signature was constructed. The risk factor of patients was calculated by this signature. High-risk patients had significantly shorter overall survival than low-risk patients. ROC analysis showed that the prognostic model accurately identified high-risk individuals within different time frames. We established an effective 9-gene-based risk signature to predict the prognosis of gastric cancer patients, providing guidance for prognostic stratification.

Proliferation Cycle Transcriptomic Signatures are Strongly associated With Gastric Cancer Patient Survival

Gastric cancer is one of the most heterogeneous tumors with multi-level molecular disturbances. Sustaining proliferative signaling and evading growth suppressors are two important hallmarks that enable the cancer cells to become tumorigenic and ultimately malignant, which enable tumor growth. Discovering and understanding the difference in tumor proliferation cycle phenotypes can be used to better classify tumors, and provide classification schemes for disease diagnosis and treatment options, which are more in line with the requirements of today's precision medicine. We collected 691 eligible samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, combined with transcriptome data, to explore different heterogeneous proliferation cycle phenotypes, and further study the potential genomic changes that may lead to these different phenotypes in this study. Interestingly, two subtypes with different clinical and biological characteristics were identified through cluster analysis of gastric cancer transcriptome data. The repeatability of the classification was confirmed in an independent Gene Expression Omnibus validation cohort, and consistent phenotypes were observed. These two phenotypes showed different clinical outcomes, and tumor mutation burden. This classification helped us to better classify gastric cancer patients and provide targeted treatment based on specific transcriptome data.

Development and validation of a risk prediction score for patients with nasopharyngeal carcinoma

Background

We aimed to develop and validate a predictive model for the overall survival (OS) of patients with nasopharyngeal carcinoma (NPC).

Methods

Overall, 519 patients were retrospectively reviewed in this study. In addition, a random forest model was used to identify significant prognostic factors for OS among NPC patients. Then, calibration plot and concordance index (C-index) were utilized to evaluate the predictive accuracy of the nomogram model.

Results

We used a random forest model to select the three most important features, dNLR, HGB and EBV DNA, which were significantly associated with the OS of NPC patients. Furthermore, the C-index of our model for OS were 0.733 (95% CI 0.673 ~ 0.793) and 0.772 (95% CI 0.691 ~ 0.853) in the two cohorts, which was significantly higher than that of the TNM stage, treatment, and EBV DNA. Based on the model risk score, patients were divided into two groups, associated with low-risk and high-risk. Kaplan–Meier curves demonstrated that the two subgroups were significantly associated with OS in the primary cohort, as well as in the validation cohort. The nomogram for OS was established using the risk score, TNM stage and EBV DNA in the two cohorts. The nomogram achieved a higher C-index of 0.783 (95% CI 0.730 ~ 0.836) than that of the risk score model 0.733 (95% CI 0.673 ~ 0.793) in the primary cohort (P = 0.005).

Conclusions

The established risk score model and nomogram resulted in more accurate prognostic prediction for individual patient with NPC.

Two Similar Signatures for Predicting the Prognosis and Immunotherapy Efficacy of Stomach Adenocarcinoma Patients

Background

Globally, stomach adenocarcinoma (STAD)’s high morbidity and mortality should arouse our urgent attention. How long can STAD patients survive after surgery and whether novel immunotherapy is effective are questions that our clinicians cannot escape.

Methods

Various R packages, GSEA software, Metascape, STRING, Cytoscape, Venn diagram, TIMER2.0 website, TCGA, and GEO databases were used in our study.

Results

In the TCGA and GEO, macrophage abundance of STAD tissues was significantly higher than that of adjacent tissues and was an independent prognostic factor, significantly related to the overall survival (OS) of STAD patients. Between the high- and low- macrophage abundance, we conducted differential expression, univariate and multivariate Cox analysis, and obtained 12 candidate genes, and finally constructed a 3-gene signature. Both low macrophage abundance group and group D had higher TMB and PD-L1 expression. Furthermore, top 5 common gene-mutated STAD tissues had lower macrophage abundance. Macrophage abundance and 3 key genes expression were also lower in the Epstein-Barr Virus (EBV) and HM-indel STAD subtypes and significantly correlated with the tumor microenvironment score. The functional enrichment and ssGSEA revealed 2 signatures were similar and closely related to BOQUEST_STEM_CELL_UP, including genes up-regulated in proliferative stromal stem cells. Hsa-miR-335-5p simultaneously regulated 3 key genes and significantly related to the expression of PD-L1, CD8A and PDCD1.

Conclusion

macrophage abundance and 3-gene signature could simultaneously predict the OS and immunotherapy efficacy, and both 2 signatures had remarkable similarities. Hsa-miR-335-5p and BOQUEST_STEM_CELL_UP might be novel immunotherapy targets.