The comparison of long-term oncological outcomes and complications after proximal gastrectomy with double tract reconstruction versus total gastrectomy for proximal gastric cancer.

BACKGROUND: Conventional methods for treating patients with proximal gastric cancer (PGC) include proximal gastrectomy (PG) and total gastrectomy (TG) and such methods have become challenging due to double tract reconstruction (DTR). However, the clinical outcomes remain unclear. This study was performed with the aim of verifying that PG-DTR was beneficial in terms of reducing the incidence of postoperative complications and improving the prognosis. METHODS: The PGC patient cohort was retrospectively grouped into the PG-DTR and TG groups. Clinicopathological features, complications, and survival data were compared between the two groups. RESULTS: A total of 388 patients were included in the analyses. Patients who were subjected to TG tended to have more severe gastroesophageal reflux (GR) (P = 0.041), anemia (P = 0.007), and hypoalbuminemia (P < 0.001). Overall survival rates, regardless of clinical stage, were significantly different between the PG-DTR and TG groups (all P < 0.05). The multivariate Cox regression analysis confirmed that surgical procedure, tumor size, infiltration depth, lymph node metastasis, differentiation, and age were independent risk factors. The patients were likely to benefit from PG-DTR (all HR > 1 and P < 0.05). However, no significant differences were observed in the risks of GR, anemia, and hypoalbuminemia (all P > 0.05). Moreover, the nomogram derived from significant parameters showed great calibration and discrimination ability and significant clinical benefit. CONCLUSIONS: The patients who underwent PG-DTR had a favorable prognosis. The risk of postoperative complications, such as severe GR, anemia, and hypoalbuminemia, was lower in PG-DTR than in TG. Thus, PG-DTR is more beneficial for patients with PGC and may be a valuable and promising surgical procedure.

Prognosis genes in gastric adenocarcinoma identified by cross talk genes in disease­related pathways.

The aim of the present study was to investigate the prognostic value of genes that participate in the development of gastric adenocarcinoma, via exploring gene cross talk in disease­related pathways. Differentially expressed genes (DEGs) in the gastric samples were identified by analyzing the expression data downloaded from the GEO database. The DEGs were subjected to the human protein­protein interaction (PPI) network to construct the PPI network of DEGs, which was then used for the identification of key genes in cancer samples via the expression deviation score and degree in the network. A total of 635 DEGs, including 432 downregulated and 203 upregulated ones were screened in the gastric adenocarcinomas samples. The PPI network of DEGs comprised 590 DEGs and 4,299 interaction pairs. A total of 200 key genes were obtained, which were significantly enriched in six downregulated and six upregulated pathways. Cross talk genes in the connected pathways were analyzed, and the Kyoto Encyclopedia of Genes and Genomes pathways hsa00980 (Metabolism of xenobiotics by cytochrome P450) and hsa00982 (Drug metabolism) were reported to share 8 cross talk genes: ADH7, ALDH3A1, GSTA1, GSTA2, UGT2B17, UGT2B10, ADH1B and CYP2C18. Among all cross talk genes, ADH7, ALDH3A1 and CLDN3 were the most specific genes. The high­ and low­risk samples identified by the prognosis model presented a remarkable difference in total survival time, indicating its robustness and sensitivity as the prognosis genes for gastric adenocarcinoma. ADH7, ALDH3A1, GSTA1, GSTA2, UGT2B17, UGT2B10, ADH1B, CYP2C18ADH7, ALDH3A1 and CLDN3 may be used as the prognosis markers and target biomarkers for chemotherapies in gastric adenocarcinoma.

Key Genes in Stomach Adenocarcinoma Identified via Network Analysis of RNA-Seq Data.

RNA-seq data of stomach adenocarcinoma (STAD) were analyzed to identify critical genes in STAD. Meanwhile, relevant small molecule drugs, transcription factors (TFs) and microRNAs (miRNAs) were also investigated. Gene expression data of STAD were downloaded from The Cancer Genome Atlas (TCGA). Differential analysis was performed with package edgeR. Relationships with correlation coefficient > 0.6 were retained in the gene co-expression network. Functional enrichment analysis was performed for the genes in the network with DAVID and KOBASS 2.0. Modules were identified using Cytoscape. Relevant small molecules drugs, transcription factors (TFs) and microRNAs (miRNAs) were revealed by using CMAP and WebGestalt databases. A total of 520 DEGs were identified between 285 STAD samples and 33 normal controls, including 244 up-regulated and 276 down-regulated genes. A gene co-expression network containing 53 DEGs and 338 edges was constructed, the genes of which were significantly enriched in focal adhesion, ECM-receptor interaction and vascular smooth muscle contraction pathways. Three modules were identified from the gene co-expression network and they were associated with skeletal system development, inflammatory response and positive regulation of cellular process, respectively. A total of 20 drugs, 9 TFs and 6 miRNAs were acquired that may regulate the DEGs. NFAT-COL1A1/ANXA1, HSF2-FOS, SREBP-IL1RN and miR-26-COL5A2 regulation axes may be important mechanisms for STAD.