Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics.

AIM: In this research, we aimed to develop a model for the accurate prediction of gastric cancer based on H&E findings combined with machine learning pathomics. METHODS: Transcriptome data, pathological images, and clinical data from 443 cases were retrieved from TCGA (The Cancer Genome Atlas Program) for survival analysis. The images were segmented using the Otsu algorithm, and features were extracted using the PyRadiomics package. Subsequently, the cases were randomly divided into a training cohort of 165 cases and a validation cohort of 69 cases. Features selected via minimum Redundancy - Maximum Relevance (mRMR)- recursive feature elimination (RFE) screening were used to train a model using the Gradient Boosting Machine (GBM) algorithm. The model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves. Additionally, the correlation between the Pathomics score (PS) and immune genes was examined. RESULTS: In the multivariate analysis, heightened infiltration of activated CD4 memory T cells was strongly associated with improved overall survival (HR = 0.505, 95 % CI = 0.342-0.745, P < 0.001). The pathomic model, exhibiting robust predictive capability, demonstrated impressive AUC values of 0.844 and 0.750 in both study cohorts. The Decision Curve Analysis (DCA) unequivocally underscored the model's exceptional clinical utility. In a subsequent multivariate analysis, heightened infiltration of the PS also emerged as a significant protective factor for overall survival (HR = 0.506, 95 % CI = 0.329-0.777, P = 0.002). CONCLUSION: The pathomic model based on H&E slides for predicting the infiltration degree of activated CD4 memory T cells, along with integrated bioinformatics analysis elucidating potential molecular mechanisms, offers novel prognostic indicators for the precise stratification and individualized prognosis of gastric cancer patients.

Identification of Potential Diagnostic and Prognostic Biomarkers for Gastric Cancer Based on Bioinformatic Analysis.

Gastric cancer (GC) ranks third for cancer-related fatalities worldwide. It is still unclear what causes GC to progress. Using integrated bioinformatics analysis, COL5A2 has been proved to be related to GC development, which may identify the likely pathogenic mechanism. Data from GC patients were gathered using The Cancer Gene Atlas (TCGA) and the gene expression omnibus (GEO). The level of COL5A2 expression was compared between paired GC and normal tissues. The differentially expressed genes (DEGs) in GC patients with high and low COL5A2 expression were identified using functional enrichment analysis to identify the signature pathways linked to the DEGs. The clinical pathologic traits connected to overall survival (OS) of GC patients were examined utilizing Cox regression and the Kaplan-Meier method. To assess the prognostic significance of COL5A2, receiver operating characteristic (ROC) curves was drawn. How the immune system infiltrate both normal gastric and GC tumor tissues was investigated. Using the human protein atlas (HPA) database, regression, and the Kaplan-Meier method, immunohistochemical analysis of DEG COL5A2 expression in GC tissues was carried out. The correlation between COL5A2 expression and the GC grouping was found to be highly significant. Functional annotations revealed that COL5A2 participates in extracellular matrix structure, collagen metabolism, and other biological processes (BPs). High COL5A2 expression was associated with poor prognostic and clinical features, such as clinical T, N, and M stages. ROC curves exhibited that COL5A2 might predict the occurrence of gastric cancer. The infiltration degree of 21 immune cell subsets, including activated dendritic cells (aDCs), CD8+ T cells, and cytotoxic cells, was found to be dramatically relevant to COL5A2. Immunohistochemical analysis indicated that the expression of COL5A2 in tumor tissues is higher than that in normal tissues. The COL5A2 gene may offer fresh perspectives on the pathogenic mechanism underlying GC, as well as potential biomarkers for estimating GC patient prognosis. As a result, COL5A2 may be a useful biomarker for predicting patient survival.

Preliminary report on the short-term efficacy and safety of SAPO-S1 therapy for locally advanced gastric cancer with a deep learning perspective.

The neoadjuvant therapeutic effects of various chemotherapeutic drugs on gastric cancer have reached the corresponding plateau. Whether the combination of sindilizumab and albumin-bound paclitaxel+oxaliplatin+S-1 chemotherapy (SAPO-S1 therapy) regimen can bring better efficacy and observe the incidence of adverse reactions in the neoadjuvant treatment of Gastric Cancer (GC) may be the direction of our research. This study aimed to evaluate the efficacy of S1 chemotherapy regimen combined with multiple chemotherapy drugs sindilizumab (PD-1 inhibitor), albumin-bound paclitaxel and oxaliplatin) for neoadjuvant therapy in locally advanced Gastric Cancer (LA-GC). The patients were given 4 cycles of sindilizumab combined with albumin paclitaxel+oxaliplatin+S-1 chemotherapy (SAPO-S1) before surgery. The R0 resection rate, surgical complications, pathologic complete response, complete pathologic response (pCR) the main pathological response rates (residual tumor cells≤10%, major pathological response) were observed. MPR and postoperative pathological tumor regression grade (TRG), using the response evaluation criteria in solid tumors (RECIST1.1) to evaluate the efficacy of new adjuvant therapy and record the short-term adverse events (adverse event, AE) of patients after medication to evaluate safety. The overall response rate (ORR) was achieved to 53.3% and disease control rate (DCR) was achieved in 28 patients (93.3%), and the descending phase was achieved in 17 patients (56.7%). The tumor resolution grades of TRG 0, TRG 1, TRG 2 and TRG 3 were 16.7%, 13.3%, 43.3% and 16.7%, respectively. The pCR rate was 16.7%, the MPR rate was 30.0%, and the R0 resection rate was 90.0%. In addition, SAPO-S1 therapy has fewer side effects. Overall, SAPO-S1 therapy has a good therapeutic effect and safety in LA-GC.

NUSAP1 gene silencing inhibits cell proliferation, migration and invasion through inhibiting DNMT1 gene expression in human colorectal cancer.

Colorectal cancer (CRC) is one of the most common cause of cancer-related death in both female and male patients, with a high capacity for tumor migration and invasion. Recently, aberrant nucleolar and spindle-associated protein 1 (NUSAP1) expression has been reported in several cancers. However, the biological function and molecular mechanism of NUSAP1 in CRC have not been reported. Here, we demonstrated that NUSAP1 gene expression was notably upregulated in CRC tissues and cell lines (Caco2, LS174T, SW480, and LoVo). Subsequently, SW480 and LoVo cells were transfected with NUSAP1 siRNA, respectively, and the biological function of NUSAP1 was investigated. Results indicated that NUSAP1 silencing by siRNA inhibited CRC cell proliferation, and induces cell apoptosis. Moreover, NUSAP1 knockdown suppressed cell migration, cell invasion, and epithelial-to-mesenchymal transition (EMT). Furthermore, NUSAP1 silencing notably inhibited the mRNA and protein expression level of DNA methyltransferase 1 (DNMT1). DNMT1 overexpression partly rescued the effect of NUSAP1 silencing on colorectal cancer biological function. Taken together, NUSAP1 gene silencing induced cell apoptosis, and inhibited cell proliferation, cell migration, cell invasion, and EMT in colorectal cancer through inhibiting DNMT1 gene expression. These findings indicat that NUSAP1 is a promising molecular target for CRC treatment.

Up-regulation of microRNA-302a inhibited the proliferation and invasion of colorectal cancer cells by regulation of the MAPK and PI3K/Akt signaling pathways.

Aberrant expression of microRNA-302a (miR-302a) has been frequently reported in some cancers excluding colorectal cancer (CRC). However, the role of miR-302a in CRC has not been reported. In this paper, we examined the effect of miR-302a overexpression on proliferation and invasion in CRC cells. The mRNA level of miR-302a in CRC cell lines was determined by real-time PCR. The miR-302a mimic was transiently transfected into CRC cells using Lipofectamine™ 2000 reagent. Subsequently, cell proliferation and invasion were assessed by MTT and Transwell assays. Western blot and ELISA assay were used to detect the expressions and secretions of matrix metalloproteinases (MMPs). Moreover, the expressions of epithelial marker, mesenchymal markers and transcription factors were also determined by Western blot. In addition, the effects of miR-302a overexpression on the MAPK and PI3K/Akt signaling pathways were investigated by Western blot. Our results showed that the mRNA level of miR-302a was remarkably decreased in CRC cell lines compared with normal colon epithelium cells. Up-regulation of miR-302a inhibited the proliferation and invasion of CRC cells. The expressions and secretions of MMP-9 and -2 were evidently reduced by increasing miR-302a. Besides, we found a decrease of ß-catenin, fibronection, vimentin, Snail, Slug, ZEB1 and ZEB2 expressions and an increase of E-cadherin expression. We also found that miR-302a overexpression might decrease the phosphorylation of Erk1/2 and Akt. Altogether, our results indicated that miR-302a overexpression was shown to inhibit proliferation and invasion of CRC cells by reducing the expressions of related proteins through suppressing the MAPK and PI3K/Akt signaling pathways.

Effect of Proliferator-Activated Receptor-γ Pro12Ala Polymorphism on Colorectal Cancer Risk: A Meta-Analysis.

BACKGROUND: The association between peroxisome proliferators-activated receptor γ (PPARγ) Pro12Ala polymorphism and colorectal cancer (CRC) risk is still controversial. A meta-analysis was performed. MATERIAL AND METHODS: We conducted a literature search using PubMed, EMBASE, and Cochran databases. The pooled odds ratio (OR) with 95% confidence intervals (CIs) were calculated. Fixed-effects and random-effects models were used. Dominant model, recessive model, and additive model were used in this meta-analysis. RESULTS: Fifteen studies including 13575 cases and 17085 controls were included in our meta-analysis. Result of this meta-analysis found that PPARγ Pro12Ala polymorphism was significantly associated with a reduced risk of CRC (OR=0.90; 95% CI 0.83-0.98; P=0.01). No significant association was found between PPARγ Pro12Ala polymorphism and CRC risk in Asians (OR=0.80; 95% CI 0.60-1.09; P=0.15). However, PPARγ Pro12Ala polymorphism was significantly associated with a reduced risk of CRC in Caucasians (OR=0.91; 95% CI 0.83-0.99; P=0.03). When stratified analysis was performed by CRC site, no positive association was found between PPARγ Pro12Ala polymorphism and rectal cancer (OR=0.95; 95% CI 0.74-1.22; P=0.71). However, a reduced risk of colon cancer was observed (OR=0.85; 95% CI 0.76-0.94; P=0.002). CONCLUSIONS: In summary, this study suggests that PPARγ Pro12Ala polymorphism was a protective factor of CRC.

Association between matrix metalloproteinase 1 -1607 1G>2G polymorphism and cancer risk: a meta-analysis including 19706 subjects.

The association between MMP1 -1607 1G>2G polymorphism and cancer risk has been reported, but results remained controversial and ambiguous. To assess the association between MMP1 -1607 1G>2G polymorphism and cancer risk, a meta-analysis was performed. Based on comprehensive searches of the PubMed, Elsevier Science Direct, Excerpta Medica Database (Embase), and Chinese Biomedical Literature Database (CBM), we identified outcome data from all articles estimating the association between MMP1 -1607 1G>2G polymorphism and cancer risk. The pooled odds ratio (OR) with 95% confidence intervals (CIs) were calculated. Thirty-eight studies involving 10178 cases and 9528 controls were included. Overall, significant association between MMP1 -1607 1G>2G polymorphism and cancer susceptibility was observed for additive model (OR = 1.21, 95% CI 1.09-1.35), for codominant model (OR = 1.34, 95% CI 1.10-1.63), for dominant model (OR = 1.17, 95% CI 1.01-1.34), for recessive model (OR = 1.31, 95% CI 1.14-1.52). In the subgroup analysis by ethnicity, the significant association was found among Asians but not among Caucasians. In the subgroup analysis by site of cancer, significant associations were found among lung cancer, colorectal cancer, head and neck cancer and bladder cancer. This meta-analysis demonstrated that the MMP1 -1607 1G>2G polymorphism was significantly associated with cancer risk.

Meta-analysis in the association between obesity and risk of thyroid cancer.

Although many epidemiologic studies have investigated obesity and thyroid cancer risk, definite conclusions cannot be drawn. To clarify the effects of obesity on the risk of thyroid cancer, a meta-analysis was performed. Related studies were identified from PubMed, Springer Link, Ovid, Chinese Wanfang Data Knowledge Service Platform, Chinese National Knowledge Infrastructure (CNKI), and Chinese Biology Medicine (CBM) till 16 Aug 2014. Pooled RRs and 95% CIs were used to assess the strength of the associations. A total of 16 studies including 12616154 subjects were involved in this meta-analysis. A significantly elevated thyroid cancer risk was found in overall analysis (RR = 1.29, 95% CI 1.20-1.37, P < 0.00001). In the gender subgroup analyses, a statistically significant association was found in male patients (RR = 1.35, 95% CI 1.16-1.58, P = 0.0001) and in female patients (RR = 1.29, 95% CI 1.19-1.40, P < 0.00001). When we limited the meta-analysis to studies that controlled for age (RR = 1.34, 95% CI 1.24-1.44, P < 0.00001), smoke (RR = 1.36, 95% CI 1.22-1.52, P < 0.00001), alcohol use (RR = 1.40, 95% CI 1.15-1.71, P = 0.0009), and history of benign thyroid disease (RR = 1.51, 95% CI 1.24-1.83, P < 0.0001), a significant association between obesity and thyroid cancer risk remained. This meta-analysis provides the evidence that obesity may contribute to the thyroid cancer development.