Comprehensive Characterization of RNA Processing Factors in Gastric Cancer Identifies a Prognostic Signature for Predicting Clinical Outcomes and Therapeutic Responses

Characterization of RNA Processing Genes in Colon Cancer for Predicting Clinical Outcomes

Objective

Colon cancer is associated with multiple levels of molecular heterogeneity. RNA processing converts primary transcriptional RNA to mature RNA, which drives tumourigenesis and its maintenance. The characterisation of RNA processing genes in colon cancer urgently needs to be elucidated.

Methods

In this study, we obtained 1033 relevant samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to explore the heterogeneity of RNA processing phenotypes in colon cancer. Firstly, Unsupervised hierarchical cluster analysis detected 4 subtypes with specific clinical outcomes and biological features via analysis of 485 RNA processing genes. Next, we adopted the least absolute shrinkage and selection operator (LASSO) as well as Cox regression model with penalty to characterise RNA processing-related prognostic features.

Results

An RNA processing-related prognostic risk model based on 10 genes including FXR1, MFAP1, RBM17, SAGE1, SNRPA1, SRRM4, ADAD1, DDX52, ERI1, and EXOSC7 was identified finally. A composite prognostic nomogram was constructed by combining this feature with the remaining clinical variables including TNM, age, sex, and stage. Genetic variation, pathway activation, and immune heterogeneity with risk signatures were also analysed via bioinformatics methods. The outcomes indicated that the high-risk subgroup was associated with higher genomic instability, increased proliferative and cycle characteristics, decreased tumour killer CD8+ T cells and poorer clinical prognosis than the low-risk group.

Conclusion

This prognostic classifier based on RNA-edited genes facilitates stratification of colon cancer into specific subgroups according to TNM and clinical outcomes, genetic variation, pathway activation, and immune heterogeneity. It can be used for diagnosis, classification and targeted treatment strategies comparable to current standards in precision medicine. It provides a rationale for elucidation of the role of RNA editing genes and their clinical significance in colon cancer as prognostic markers.

Dysregulation of tRNA methylation in cancer: Mechanisms and targeting therapeutic strategies

tRNA is the RNA type that undergoes the most modifications among known RNA, and in recent years, tRNA methylation has emerged as a crucial process in regulating gene translation. Dysregulation of tRNA abundance occurs in cancer cells, along with increased expression and activity of tRNA methyltransferases to raise the level of tRNA modification and stability. This leads to hijacking of translation and synthesis of multiple proteins associated with tumor proliferation, metastasis, invasion, autophagy, chemotherapy resistance, and metabolic reprogramming. In this review, we provide an overview of current research on tRNA methylation in cancer to clarify its involvement in human malignancies and establish a theoretical framework for future therapeutic interventions targeting tRNA methylation processes.

Identification of necroptosis-related diagnostic biomarkers in coronary heart disease

Background

The implication of necroptosis in cardiovascular disease was already recognized. However, the molecular mechanism of necroptosis has not been extensively studied in coronary heart disease (CHD).

Methods

The differentially expressed genes (DEGs) between CHD and control samples were acquired in the GSE20681 dataset downloaded from the GEO database. Key necroptosis-related DEGs were captured and ascertained by bioinformatics analysis techniques, including weighted gene co-expression network analysis (WGCNA) and two machine learning algorithms, while single-gene gene set enrichment analysis (GSEA) revealed their molecular mechanisms. The diagnostic biomarkers were selected via receiver operating characteristic (ROC) analysis. Moreover, an analysis of immune elements infiltration degree was carried out. Authentication of pivotal gene expression at the mRNA level was investigated in vitro utilizing quantitative real-time PCR (qRT-PCR).

Results

A total of 94 DE-NRGs were recognized here, among which, FAM166B, NEFL, POLDIP3, PRSS37, and ZNF594 were authenticated as necroptosis-related biomarkers, and the linear regression model based on them presented an acceptable ability to different sample types. Following regulatory analysis, the ascertained biomarkers were markedly abundant in functions pertinent to blood circulation, calcium ion homeostasis, and the MAPK/cAMP/Ras signaling pathway. Single-sample GSEA exhibited that APC co-stimulation and CCR were more abundant, and aDCs and B cells were relatively scarce in CHD patients. Consistent findings from bioinformatics and qRT-PCR analyses confirmed the upregulation of NEFL and the downregulation of FAM166B, POLDIP3, and PRSS37 in CHD.

Conclusion

Our current investigation identified 5 necroptosis-related genes that could be diagnostic markers for CHD and brought a novel comprehension of the latent molecular mechanisms of necroptosis in CHD.

Establishment of a 7-gene expression panel to improve the prognosis classification of gastric cancer patients

Gastric cancer (GC) ranks fifth in incidence and fourth in mortality worldwide. The high death rate in patients with GC requires new biomarkers for improving survival estimation. In this study, we performed a transcriptome-based analysis of five publicly available cohorts to identify genes consistently associated with prognosis in GC. Based on the ROC curve, patients were categorized into high and low-expression groups for each gene using the best cutoff point. Genes associated with survival (AUC > 0.5; univariate and multivariate Cox regressions, p < 0.05) were used to model gene expression-based scores by weighted sum using the pooled Cox β regression coefficients. Cox regression (p < 0.05), AUC > 0.5, sensitivity > 0.5, and specificity > 0.5 were considered to identify the best scores. Gene set enrichment analysis (KEGG, REACTOME, and Gene Ontology databases), as well as microenvironment composition and stromal cell signatures prediction (CIBERSORT, EPIC, xCell, MCP-counter, and quanTIseq web tools) were performed. We found 11 genes related to GC survival in the five independent cohorts. Then, we modeled scores by calculating all possible combinations between these genes. Among the 2,047 scores, we identified a panel based on the expression of seven genes. It was named GES7 and is composed of CCDC91, DYNC1I1, FAM83D, LBH, SLITRK5, WTIP, and NAP1L3 genes. GES7 features were validated in two independent external cohorts. Next, GES7 was found to recategorize patients from AJCC TNM stages into a best-fitted prognostic group. The GES7 was associated with activation of the TGF-β pathway and repression of anticancer immune cells. Finally, we compared the GES7 with 30 previous proposed scores, finding that GES7 is one of the most robust scores. As a result, the GES7 is a reliable gene-expression-based signature to improve the prognosis estimation in GC.

Epigenetic Modification-Associated Molecular Classification of Gastric Cancer

Epigenetic modification is involved in tumorigenesis and cancer progression. We developed an epigenetic modification-associated molecular classification of gastric cancer (GC) to identify signature genes that accurately predict prognosis and the efficacy of immunotherapy. Least absolute shrinkage and selection operator and multivariate Cox regression analysis were conducted to develop an epigenetic modification-associated molecular classification. We investigated the significance of PIP4P2, an independent prognostic factor of the classification system, in predicting the prognosis and immunotherapy efficacy of patients with GC. The epigenetic modification-associated molecular classification was highly associated with the clinicopathological characteristics of patients and the existing classification of GC. PIP4P2 was highly expressed in GC tissue and tumor-associated macrophages. High PIP4P2 expression in GC tissue-induced tumor progression by activating PI3K/AKT signal transduction had a negative impact on immunotherapy efficacy. High expression of PIP4P2 in macrophages was correlated with poor prognosis in patients with GC. PIP4P2 is an independent unfavorable prognostic factor of epigenetic modification-associated molecular classification, is involved in tumorigenic progression, and is essential for assessing the prognosis and immunotherapy efficacy of GC.

Novel prognostic gene signature for pancreatic ductal adenocarcinoma based on hypoxia

BACKGROUND

Currently, there is lack of marker to accurately assess the prognosis of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC). This study aims to establish a hypoxia-related risk scoring model that can effectively predict the prognosis and chemotherapy outcomes of PDAC patients.

METHODS

Using unsupervised consensus clustering algorithms, we comprehensively analyzed The Cancer Genome Atlas (TCGA) data to identify two distinct hypoxia clusters and used the weighted gene co-expression network analysis (WGCNA) to examine gene sets significantly associated with these hypoxia clusters. Then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used to construct a signature and its efficacy was evaluated using the International Cancer Genome Consortium (ICGC) PDAC cohort. Further, the correlation between the risk scores obtained from the signature and carious clinical, pathological, immunophenotype, and immunoinfiltration factors as well as the differences in immunotherapy potential and response to common chemotherapy drugs between high-risk and low-risk groups were evaluated.

RESULTS

From a total of 8 significantly related modules and 4423 genes, 5 hypoxia-related signature genes were identified to construct a risk model. Further analysis revealed that the overall survival rate (OS) of patients in the low-risk group was significantly higher than the high-risk group. Univariate and multivariate Cox regression analysis showed that the risk scoring signature was an independent factor for prognosis prediction. Analysis of immunocyte infiltration and immunophenotype showed that the immune score and the anticancer immune response in the high-risk were significantly lower than that in the low-risk group.

CONCLUSION

The constructed hypoxia-associated prognostic signature demonstrated could be used as a potential risk classifier for PDAC.

Methyltransferase-like proteins in cancer biology and potential therapeutic targeting

RNA modification has recently become a significant process of gene regulation, and the methyltransferase-like (METTL) family of proteins plays a critical role in RNA modification, methylating various types of RNAs, including mRNA, tRNA, microRNA, rRNA, and mitochondrial RNAs. METTL proteins consist of a unique seven-beta-strand domain, which binds to the methyl donor SAM to catalyze methyl transfer. The most typical family member METTL3/METTL14 forms a methyltransferase complex involved in N6-methyladenosine (m6A) modification of RNA, regulating tumor proliferation, metastasis and invasion, immunotherapy resistance, and metabolic reprogramming of tumor cells. METTL1, METTL4, METTL5, and METTL16 have also been recently identified to have some regulatory ability in tumorigenesis, and the rest of the METTL family members rely on their methyltransferase activity for methylation of different nucleotides, proteins, and small molecules, which regulate translation and affect processes such as cell differentiation and development. Herein, we summarize the literature on METTLs in the last three years to elucidate their roles in human cancers and provide a theoretical basis for their future use as potential therapeutic targets.

Identification of functional genes regulating gastric cancer progression using integrated bioinformatics analysis

BACKGROUND

Gastric cancer (GC) is one of the most common cancers and has a poor prognosis. Treatment of GC has remained unchanged over the past few years.

AIM

To investigate the potential therapeutic targets and related regulatory biomarkers of GC.

METHODS

We obtained the public GC transcriptome sequencing dataset from the Gene Expression Omnibus database. The datasets contained 348 GC tissues and 141 healthy tissues. In total, 251 differentially expressed genes (DEGs) were identified, including 187 down-regulated genes and 64 up-regulated genes. The DEGs' enriched functions and pathways include Progesterone-mediated oocyte maturation, cell cycle, and oocyte meiosis, Hepatitis B, and the Hippo signaling pathway. Survival analysis showed that BUB1, MAD2L1, CCNA2, CCNB1, and BIRC5 may be associated with regulation of the cell cycle phase mitotic spindle checkpoint pathway. We selected 26 regulated genes with the aid of the protein-protein interaction network analyzed by Molecular Complex Detection.

RESULTS

We focused on three critical genes, which were highly expressed in GC, but negatively related to patient survival. Furthermore, we found that knockdown of BIRC5, TRIP13 or UBE2C significantly inhibited cell proliferation and induced cell apoptosis. In addition, knockdown of BIRC5, TRIP13 or UBE2C increased cellular sensitivity to cisplatin.

CONCLUSION

Our study identified significantly upregulated genes in GC with a poor prognosis using integrated bioinformatics methods.

Comprehensive analysis reveals TSEN54 as a robust prognosis biomarker and promising immune-related therapeutic target for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma represents the most common primary malignancy of all liver cancer types and its prognosis is usually unsatisfactory. TSEN54 encodes a protein constituting a subunit of the tRNA splicing endonuclease heterotetramer. Previous researches concentrated on the contribution of TSEN54 in pontocerebellar hypoplasia, but no studies have yet reported its role in HCC.

METHODS

TIMER, HCCDB, GEPIA, HPA, UALCAN, MEXPRESS, SMART, TargetScan, RNAinter, miRNet, starBase, Kaplan-Meier Plotter, cBioPortal, LinkedOmics, GSEA, TISCH, TISIDB, GeneMANIA, PDB, GSCALite were applied in this research.

RESULTS

We identified the upregulation of TSEN54 expression in HCC and related it to multiple clinicopathological features. Hypomethylation of TSEN54 was closely associated with its high expression. HCC sufferers who held high TSEN54 expression typically had shorter survival expectations. Enrichment analysis showed the involvement of TSEN54 in the cell cycle and metabolic processes. Afterward, we observed that TSEN54 expression level had a positive relationship to the infiltration level of multiple immune cells and the expression of several chemokines. We additionally identified that TSEN54 was related to the expression level of several immune checkpoints and TSEN54 was linked to several m6A-related regulators.

CONCLUSIONS

TSEN54 is a prognostic marker of HCC. TSEN54 could become a prospective candidate for HCC diagnosis and therapy.

Exploration of prognostic genes and risk signature in breast cancer patients based on RNA binding proteins associated with ferroptosis

Background: Breast cancer (BRCA) is a life-threatening malignancy in women with an unsatisfactory prognosis. The purpose of this study was to explore the prognostic biomarkers and a risk signature based on ferroptosis-related RNA-binding proteins (FR-RBPs). Methods: FR-RBPs were identified using Spearman correlation analysis. Differentially expressed genes (DEGs) were identified by the "limma" R package. The univariate Cox and multivariate Cox analyses were executed to determine the prognostic genes. The risk signature was constructed and verified with the training set, testing set, and validation set. Mutation analysis, immune checkpoint expression analysis in high- and low-risk groups, and correlation between risk signature and chemotherapeutic agents were conducted using the "maftools" package, "ggplot2" package, and the CellMiner database respectively. The Human Protein Atlas (HPA) database was employed to confirm protein expression trends of prognostic genes in BRCA and normal tissues. The expression of prognostic genes in cell lines was verified by Real-time quantitative polymerase chain reaction (RT-qPCR). Kaplan-meier (KM) plotter database analysis was applied to predict the correlation between the expression levels of signature genes and survival statuses. Results: Five prognostic genes (GSPT2, RNASE1, TIPARP, TSEN54, and SAMD4A) to construct an FR-RBPs-related risk signature were identified and the risk signature was validated by the International Cancer Genome Consortium (ICGC) cohort. Univariate and multivariate Cox regression analysis demonstrated the risk score was a robust independent prognostic factor in overall survival prediction. The Tumor Mutational Burden (TMB) analysis implied that the high- and low-risk groups responded differently to immunotherapy. Drug sensitivity analysis suggested that the risk signature may serve as a chemosensitivity predictor. The results of GSEA suggested that five prognostic genes might be related to DNA replication and the immune-related pathways. RT-qPCR results demonstrated that the expression trends of prognostic genes in cell lines were consistent with the results from public databases. KM plotter database analysis suggested that high expression levels of GSPT2, RNASE1, and SAMD4A contributed to poor prognoses. Conclusion: In conclusion, this study identified the FR-RBPs-related prognostic genes and developed an FR-RBPs-related risk signature for the prognosis of BRCA, which will be of great significance in developing new therapeutic targets and prognostic molecular biomarkers for BRCA.

USP51/ZEB1/ACTA2 axis promotes mesenchymal phenotype in gastric cancer and is associated with low cohesion characteristics

poorly cohesive (PC) gastric cancer (GC) (PC-GC) is a distinct histological subtype of GC and is defined as a tumor consisting of isolated or small clusters of tumor cells with poorly differentiated and metastatic characteristics. According to multiple studies, PC-GC is intrinsically heterogeneous, with mesenchymal variants being the most aggressive. However, to date, the molecular mechanisms associated with PC-GC are still not fully understood. This study investigated the role of the USP51/ZEB1/ACTA2 axis in promoting GC metastasis. Single-cell sequencing revealed that E-box binding homeobox 1 (ZEB1) expression was significantly increased in a subpopulation of low-adherent cells and was an independent prognostic factor in GC patients. Furthermore, the bulk transcriptome analysis revealed a significant positive correlation between Ubiquitin Specific Peptidase 51 (USP51), ZEB1, and Actin Alpha 2 (ACTA2), and our data further confirmed that all three were highly co-localized in PC-GC tissues. According to the findings of in vitro and in vivo experiments, USP51 was able to maintain ZEB1 expression to promote ACTA2 transcription, thereby activating the mesenchymal phenotype of GC cells and promoting tumor metastasis. Moreover, USP51 could recruit and activate stromal cells, including M2-like macrophages and fibroblasts, through cancer cells. Clinical data suggested that overexpression of USP51 predicts that patients have difficulty benefiting from immunotherapy and is associated with immune-exclusion tumor characteristics. Collectively, the findings of this study shed light on a key mechanism by which elevated USP51 expression induces Epithelial-mesenchymal transition (EMT) in GC cells, hence facilitating GC cell proliferation, survival, and dissemination. In this view, USP51/ZEB1/ACTA2 may serve as a candidate therapeutic target against GC metastasis.

METTLing in Stem Cell and Cancer Biology

The methyltransferase-like (METTL) family is a diverse group of methyltransferases that can methylate nucleotides, proteins, and small molecules. Despite this diverse array of substrates, they all share a characteristic seven-beta-strand catalytic domain, and recent evidence suggests many also share an important role in stem cell biology. The most well characterized family members METTL3 and METTL14 dimerize to form an N6-methyladenosine (m6A) RNA methyltransferase with established roles in cancer progression. However, new mouse models indicate that METTL3/METTL14 are also important for embryonic stem cell (ESC) development and postnatal hematopoietic and neural stem cell self-renewal and differentiation. METTL1, METTL5, METTL6, METTL8, and METTL17 also have recently identified roles in ESC pluripotency and differentiation, while METTL11A/11B, METTL4, METTL7A, and METTL22 have been shown to play roles in neural, mesenchymal, bone, and hematopoietic stem cell development, respectively. Additionally, a variety of other METTL family members are translational regulators, a role that could place them as important players in the transition from stem cell quiescence to differentiation. Here we will summarize what is known about the role of METTL proteins in stem cell differentiation and highlight the connection between their growing importance in development and their established roles in oncogenesis.

The polymerase δ-interacting protein family and their emerging roles in diseases

The polymerase δ-interacting protein (POLDIP) family is a new family that can interact with DNA polymerase δ (delta). The members of the POLDIP family include POLDIP1, POLDIP2, and POLDIP3. Screened by the two-hybrid method, POLDIP1, POLDIP2, and POLDIP3 were initially discovered and named for their ability to bind to the p50 subunit of DNA polymerase δ. Recent studies have confirmed that POLDIPs are involved in the regulation of signal transduction pathways in neurodevelopment, neuropsychiatric diseases, cardiovascular diseases, tumors, and other diseases. However, each protein participates in different signaling pathways. In this review, we elucidate upon the family in terms of their genes and protein structures, their biological functions, in addition to the pathways that they are involved in during the development of diverse diseases. Finally, to provide new insights to the scientific community, we used the TCGA database to analyze and summarize the gene expressions of POLDIP family members in various tumors, as well as the correlations between their expressions and the overall survival times of tumor patients. Our data summary will give researchers working on cancer new concepts.

Potential mechanisms and prognostic model of eRNAs-regulated genes in stomach adenocarcinoma

Gastric Carcinoma is the fourth leading cause of cancer deaths worldwide, in which stomach adenocarcinoma (STAD) is the most common histological type. A growing amount of evidence has suggested the importance of enhancer RNAs (eRNAs) in the cancer. However, the potential mechanism of eRNAs in STAD remains unclear. The eRNAs-regulated genes (eRRGs) were identified through four different enhancer resources. The differentially expressed eRRGs were obtained by ‘DESeq2’ R package. The prognosis prediction model was constructed by Cox and Lasso regression analysis. The ‘ChAMP’ R package and ‘maftools’ R package were used to investigate the multi-omics characters. In this study, combining the concept of contact domain, a total of 9014 eRRGs including 4926 PCGs and 4088 lncRNAs were identified and these eRRGs showed higher and more stable expression. Besides, the functions of these genes were mainly associated with tumor-related biological processes. Then, a prognostic prediction model was constructed and the AUC values of the 1-, 3- and 5-year survival prediction reached 0.76, 0.84 and 0.84, respectively, indicating that this model has a high accuracy. Finally, the difference between high-risk group and low-risk group were investigated using multi-omics data including gene expression, DNA methylation and somatic mutations. Our study provides significant clues for the elucidation of eRNAs in STAD and may help improve the overall survival for STAD patients.

Development of an innovative nomogram of risk factors to predict postoperative recurrence of gastrointestinal stromal tumors

BACKGROUND

There are many staging systems for gastrointestinal stromal tumors (GISTs), and the risk indicators selected are also different; thus, it is not possible to quantify the risk of recurrence among individual patients.

AIM

To develop and internally validate a model to identify the risk factors for GIST recurrence after surgery.

METHODS

The least absolute shrinkage and selection operator (LASSO) regression model was performed to identify the optimum clinical features for the GIST recurrence risk model. Multivariable logistic regression analysis was used to develop a prediction model that incorporated the possible factors selected by the LASSO regression model. The index of concordance (C-index), calibration curve, receiver operating characteristic curve (ROC), and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the predictive model. Internal validation of the clinical predictive capability was also evaluated by bootstrapping validation.

RESULTS

The nomogram included tumor site, lesion size, mitotic rate/50 high power fields, Ki-67 index, intracranial necrosis, and age as predictors. The model presented perfect discrimination with a reliable C-index of 0.836 (95%CI: 0.712-0.960), and a high C-index value of 0.714 was also confirmed by interval validation. The area under the curve value of this prediction nomogram was 0.704, and the ROC result indicated good predictive value. Decision curve analysis showed that the predicting recurrence nomogram was clinically feasible when the recurrence rate exceeded 5% after surgery.

CONCLUSION

This recurrence nomogram combines tumor site, lesion size, mitotic rate, Ki-67 index, intracranial necrosis, and age and can easily predict patient prognosis.

A nine–consensus–prognostic –gene–based prognostic signature, recognizing the dichotomized subgroups of gastric cancer patients with different clinical outcomes and therapeutic strategies

Background: The increasing prevalence and mortality of gastric cancer (GC) has promoted the urgent need for prognostic signatures to predict the long-term risk and search for therapeutic biomarkers.

Methods and materials: A total of 921 GC patients from three GEO cohorts were enrolled in the current study. The GSE15459 and GSE62254 cohorts were used to select the top prognostic gene via the evaluation of the area under the receiver operating characteristic (ROC) curve (AUC) values. The GSE84437 cohort was used as the external validation cohort. Least absolute shrinkage and selector operation (LASSO) regression analysis was applied to reduce the feature dimension and construct the prognostic signature. Furthermore, a nomogram was constructed by integrating the independent prognostic analysis and validated by calibration plot, decision curve analysis and clinical impact curve. The molecular features and response to chemo-/immunotherapy among risk subgroups were evaluated by the “MOVICS” and “ESTAMATE” R packages and the SubMap algorithm. Lauren classification and ACRG molecular subtype were obtained to compare with the risk model.

Results: Forty-four prognosis-associated genes were identified with a preset cutoff AUC value of 0.65 in both the GSE62254 and GSE15459 cohorts. With the 10-fold cross validation analysis of LASSO, nine genes were selected to construct the nine-consensus-prognostic-gene signature. The signature showed good prognostic value in the GSE62254 (p < 0.001, HR: 3.81, 95% CI: 2.44–5.956) and GSE15459 (p < 0.001, HR: 2.65, 95% CI: 1.892–3.709) cohorts and the external validation GSE84437 cohort (p < 0.001, HR: 2.06, 95% CI: 1.554–2.735). The nomogram constructed based on two independent predictive factors, tumor stage and the signature, predicted events tightly consistent with the actual (Hosmer–Lemeshow p value: 1-year, 0.624; 3-years, 0.795; 5-years, 0.824). For the molecular features, we observed the activation of apical junction, epithelial mesenchymal transition, and immune pathways in the high-risk group, while in the low-risk group, cell cycle associated G2M, E2F and MYC target pathways were activated. Based on the results we obtained, we indicated that gastric patients in the low-risk group are more suitable for 5-fluorouracil therapy, while high-risk group patients are more suitable for anti-CTLA4 immunotherapy, these results need more support in the further studies. After compare with proposed molecular subtypes, we realized that the nine-consensus prognostic gene signature is a powerful addition to identify the gastric patients with poor prognosis.

Conclusion: In summary, we constructed a robust nine-consensus-prognostic-gene signature for the prediction of GC prognosis, which can also predict the personalized treatment of GC patients.

Establishment of a Novel Prognostic Prediction Model for Gastric Cancer Based on Necroptosis-Related Genes

Background: Necroptosis plays a crucial role in the progression of multiple types of cancer. However, the role of necroptosis in gastric cancer (GC) remains unclear. The aim of this study is to establish a necroptosis-related prediction model, which could provide information for treatment monitoring.

Methods: The TCGA-STAD cohort was employed to establish a prognostic prediction signature and the GEO dataset was employed for external validation. The correlation between the risk score and the immune landscape, tumor mutational burden (TMB), microsatellite instability (MSI), as well as therapeutic responses of different therapies were analyzed.

Results: We constructed a prognostic model based on necroptosis-associated genes (NAGs), and its favorable predictive ability was confirmed in an external cohort. The risk score was confirmed as an independent determinant, and a nomogram was further established for prognosis. A high score implies higher tumor immune microenvironment (TIME) scores and more significant TIME cell infiltration. High-risk patients presented with lower TMB, and low-TMB patients had worse overall survival (OS). Meanwhile, Low-risk scores are characterized by MSI-high (MSI-H), lower Tumor Immune Dysfunction and Exclusion (TIDE) score, and higher immunogenicity in immunophenoscore (IPS) analysis.

Conclusion: The developed NAG score provides a novel and effective method for predicting the outcome of GC as well as potential targets for further research.

Comparing Different Anesthesia Methods on Anesthetic Effect and Postoperative Pain in Patients with Early Gastric Cancer during Endoscopic Submucosal Dissection

Endoscopic submucosal dissection (ESD) is a minimally invasive technique to completely peel the pathological mucosa from the submucosa under endoscopy, which has been often utilized to treat early gastric cancer. During the operation, anesthesia is required to reduce the discomfort due to the complexity, high risk, and longtime operation of ESD. In this study, we compared different anesthesia methods on anesthetic effect and postoperative pain in patients (≥65 years old) with early gastric cancer during ESD. For this purpose, 60 patients with early gastric cancer who were more than 65 years old were selected from January 2019 to December 2021, where 30 patients treated with simple intravenous general anesthesia were divided into the simple group and 30 patients treated with intravenous combined inhalation general anesthesia were regarded as the composite group. The hemodynamic index, wake-up time, postoperative pain intensity, operation time, and the incidence of adverse reactions were compared between the two groups. For the hemodynamic index before incision, after incision, and at the end of the operation, the mean arterial pressure (MAP) in the composite group was higher than that in the simple group (P < 0.05) and the heart rate (HR) was lower than that in the simple group (P < 0.05). After the ESD operation, the wake-up time and visual analogue scale (VAS) in the composite group were lower than those in the simple group (P < 0.05). In addition, the ESD operation time and incidence of adverse reactions in the composite group was significantly lower than that in the simple group (P < 0.05). These results showed that intravenous combined inhalation general anesthesia had a good anesthetic effect, stable hemodynamics during ESD operation, and slight postoperative pain.

Patient-Level DNA Damage Repair Pathway Profiles and Anti-Tumor Immunity for Gastric Cancer

DNA damage repair (DDR) comprises the detection and correction of alterations in the chemical structure of DNA. The dysfunction of the DDR process has been determined to have important implications for tumor carcinogenesis, malignancy progression, treatment resistance, and prognosis assessment. However, the role of the DDR process in gastric cancer (GC) remains to be fully understood. Thus, a total of 2,019 GC samples from our hospital (Harbin Medical University Cancer Hospital in china) and 12 public data sets were included in our study. In this study, single-sample gene set enrichment analysis (ssGSEA) was used to generate the DDR pathway activity profiles of 8 DDR sub-pathways and identify a DDR pathway signature by combining the DDR sub-pathway gene sets. The DDR pathway profiling’s impacts on the clinical outcomes, biological functions, genetic variants, immune heterogeneity, and treatment responses were analyzed through multidimensional genomics and clinical data. The results demonstrate that the DDR pathway profiling was clearly distinguished between tumor and normal tissues. The DDR pathway profiling reveals patient-level variations, which may contribute to explaining the high heterogeneity of human GC for the biological features and treatment outcomes. Thus, tumors with low DDR signature scores were independently correlated with shorter overall survival time and significantly associated with mesenchymal, invasion, and metastasis phenotypes. The statistical model integrating this DDR pathway signature with other clinical predictors outperforms each predictor alone for predicting overall survival in discrimination, calibration, and net clinical benefit. Moreover, low DDR signature scores were tightly associated with genome stability, characterized by low tumor mutational burden (TMB) and low fractions of genome alteration. Furthermore, this study confirms that patients with low DDR pathway signature scores might not benefit from adjuvant chemotherapy and a monoclonal antibody directed against programmed cell death-1 ligand 1 (anti-PD1) therapy. These findings highlighted that the DDR pathway profiling confers important implications for patients with GC and provides insights into the specific clinical and molecular features underlying the DDR process, which may help to facilitate clinical management.