A Novel Six-Gene-Based Prognostic Model Predicts Survival and Clinical Risk Score for Gastric Cancer

Gastric cancer prognosis: unveiling autophagy-related signatures and immune infiltrates

Background

Autophagy played a crucial regulatory role in tumor initiation and progression. Therefore, we aimed to comprehensively analyze autophagy-related genes (ARGs) in gastric cancer, focusing on their expression, prognostic value, and potential functions.

Methods

The gastric cancer gene chip datasets (GSE79973 and GSE54129) were collected from the Gene Expression Omnibus (GEO) database. Subsequently, the Limma package was employed to identify differentially expressed genes (DEGs) between the normal and disease groups. The selected ARGs were further authenticated using the Human Protein Atlas (HPA) database, The Cancer Genome Atlas (TCGA) database, and GSE19826 database.

Results

A total of 15 autophagy-related DEGs, eight of which were upregulated [FKBP1A, IL24, PEA15, HSP90AB1, cathepsin B (CTSB), ITGB1, SPHK1, HIF1A], while seven were downregulated (DAPK2, EIF2AK3, FKBP1B, PTK6, NKX2-3, NFE2L2, PRKCD). Analysis revealed that CTSB was specifically associated with the prognosis of gastric cancer patients. Gene set enrichment analysis (GSEA) showcased a significant enrichment of CTSB-related genes within immune-related pathways. Moreover, correlation analysis demonstrated a clear association between the expression of CTSB and immune infiltration. The upregulation of CTSB in gastric cancer was linked to poor survival and increased immune infiltration.

Conclusions

We conjectured that CTSB likely played a critical role in regulating immunity and autophagy in gastric cancer.

Efficacy and safety of laparoscopic vs open gastrectomy after neoadjuvant therapy for locally advanced gastric cancer

BACKGROUND

Laparoscopic gastrectomy (LG) is widely accepted as a minimally invasive approach for the treatment of early gastric cancer. However, its role in locally advanced gastric cancer (LAGC) after neoadjuvant therapy (NAT) remains controversial. This study aimed to compare the efficacy and safety of LG vs open gastrectomy (OG) after NAT for the treatment of LAGC.

AIM

To compare the efficacy and safety of LG vs OG after NAT for LAGC.

METHODS

We conducted a prospective study of 76 patients with LAGC who underwent NAT followed by LG (n = 38) or OG (n = 38) between 2021 and 2023. The primary endpoint was overall survival (OS), and the secondary endpoints were disease-free survival (DFS), surgical complications, and quality of life (QOL).

RESULTS

The two groups had comparable baseline characteristics, with a median follow-up period of 24 mo. The 3-year OS rates in the LG and OG groups were 68.4% and 60.5%, respectively (P = 0.42). The 3-year DFS rates in the LG and OG groups were 57.9% and 50.0%, respectively (P = 0.51). The LG group had significantly less blood loss (P < 0.001), a shorter hospital stay (P < 0.001), and a lower incidence of surgical site infection (P = 0.04) than the OG group. There were no significant differences in other surgical complications between the groups, including anastomotic leakage, intra-abdominal abscess, or wound dehiscence. The LG group had significantly better QOL scores than the OG group regarding physical functioning, role functioning, global health status, fatigue, pain, appetite loss, and body image at 6 months postoperatively (P < 0.05).

CONCLUSION

LG after NAT is a viable and safe alternative to OG for the treatment of LAGC, with similar survival outcomes and superior short-term recovery and QOL. LG patients had less blood loss, shorter hospitalizations, and a lower incidence of surgical site infections than OG patients. Moreover, the LG group had better QOL scores in multiple domains 6 mo postoperatively. Therefore, LG should be considered a valid option for patients with LAGC who undergo NAT, particularly for those who prioritize postoperative recovery and QOL.

Identification of novel lactate metabolism-related lncRNAs with prognostic value for bladder cancer

Background: Bladder cancer (BCA) has high recurrence and metastasis rates, and current treatment options show limited efficacy and significant adverse effects. It is crucial to find diagnostic markers and therapeutic targets with clinical value. This study aimed to identify lactate metabolism-related lncRNAs (LM_lncRNAs) to establish a model for evaluating bladder cancer prognosis. Method: A risk model consisting of lactate metabolism-related lncRNAs was developed to forecast bladder cancer patient prognosis using The Cancer Genome Atlas (TCGA) database. Kaplan‒Meier survival analysis, receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA) were used to evaluate the reliability of risk grouping for predictive analysis of bladder cancer patients. The results were also validated in the validation set. Chemotherapeutic agents sensitive to lactate metabolism were assessed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Results: As an independent prognostic factor for patients, lactate metabolism-related lncRNAs can be used as a nomogram chart that predicts overall survival time (OS). There were significant differences in survival rates between the high-risk and low-risk groups based on the Kaplan‒Meier survival curve. decision curve analysis and receiver operating characteristic curve analysis confirmed its good predictive capacity. As a result, 22 chemotherapeutic agents were predicted to positively affect the high-risk group. Conclusion: An lactate metabolism-related lncRNA prediction model was proposed to predict the prognosis for patients with bladder cancer and chemotherapeutic drug sensitivity in high-risk groups, which provided a new idea for the prognostic evaluation of the clinical treatment of bladder cancer.

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.

Development and validation of novel immune-inflammation-based clinical predictive nomograms in HER2-negative advanced gastric cancer

Purpose

To explore the predictive value of multiple immune-inflammatory biomarkers including serum VEGFA and systemic immune-inflammation index (SII) in HER2-negative advanced gastric cancer (AGC) and establish nomograms for predicting the first-line chemotherapeutic efficacy, progression-free survival (PFS) and overall survival (OS) of patients with this fatal disease.

Methods

From November 2017 to April 2022, 102 and 34 patients with a diagnosis of HER2-negative AGC at the First Affiliated Hospital of Bengbu Medical College were enrolled as development and validation cohorts, respectively. Univariate and multivariate analyses were performed to evaluate the clinical value of the candidate indicators. The variables were screened using LASSO regression analysis. Predictive models were developed using significant predictors and are displayed as nomograms.

Results

Baseline VEGFA expression was significantly higher in HER2-negative AGC patients than in nonneoplastic patients and was associated with malignant serous effusion and therapeutic efficacy (all p<0.001). Multivariate analysis indicated that VEGFA was an independent predictor for first-line therapeutic efficacy and PFS (both p<0.01) and SII was an independent predictor for first-line PFS and OS (both p<0.05) in HER2-negative AGC patients. The therapeutic efficacy model had an R2 of 0.37, a Brier score of 0.15, and a Harrell's C-index of 0.82 in the development cohort and 0.90 in the validation cohort. The decision curve analysis indicated that the model added more net benefits than VEGFA assessment alone. The PFS/OS models had Harrell's C-indexes of 0.71/0.69 in the development cohort and 0.71/0.62 in the validation cohort.

Conclusion

The established nomograms integrating serum VEGFA/SII and commonly available baseline characteristics provided satisfactory performance in predicting the therapeutic efficacy and prognosis of HER2-negative AGC patients.

Integrating multiple machine learning algorithms for prognostic prediction of gastric cancer based on immune-related lncRNAs

Background: Long non-coding RNAs (lncRNAs) play an important role in the immune regulation of gastric cancer (GC). However, the clinical application value of immune-related lncRNAs has not been fully developed. It is of great significance to overcome the challenges of prognostic prediction and classification of gastric cancer patients based on the current study.

Methods: In this study, the R package ImmLnc was used to obtain immune-related lncRNAs of The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) project, and univariate Cox regression analysis was performed to find prognostic immune-related lncRNAs. A total of 117 combinations based on 10 algorithms were integrated to determine the immune-related lncRNA prognostic model (ILPM). According to the ILPM, the least absolute shrinkage and selection operator (LASSO) regression was employed to find the major lncRNAs and develop the risk model. ssGSEA, CIBERSORT algorithm, the R package maftools, pRRophetic, and clusterProfiler were employed for measuring the proportion of immune cells among risk groups, genomic mutation difference, drug sensitivity analysis, and pathway enrichment score.

Results: A total of 321 immune-related lncRNAs were found, and there were 26 prognostic immune-related lncRNAs. According to the ILPM, 18 of 26 lncRNAs were selected and the risk score (RS) developed by the 18-lncRNA signature had good strength in the TCGA training set and Gene Expression Omnibus (GEO) validation datasets. Patients were divided into high- and low-risk groups according to the median RS, and the low-risk group had a better prognosis, tumor immune microenvironment, and tumor signature enrichment score and a higher metabolism, frequency of genomic mutations, proportion of immune cell infiltration, and antitumor drug resistance. Furthermore, 86 differentially expressed genes (DEGs) between high- and low-risk groups were mainly enriched in immune-related pathways.

Conclusion: The ILPM developed based on 26 prognostic immune-related lncRNAs can help in predicting the prognosis of patients suffering from gastric cancer. Precision medicine can be effectively carried out by dividing patients into high- and low-risk groups according to the RS.

Localization and density of tertiary lymphoid structures associate with molecular subtype and clinical outcome in colorectal cancer liver metastases

BACKGROUND

Tertiary lymphoid structures (TLSs) have been proposed to assess the prognosis of patients with cancer. Here, we investigated the prognostic value and relevant mechanisms of TLSs in colorectal cancer liver metastases (CRCLM).

METHODS

603 patients with CRCLM treated by surgical resection from three cancer centers were included. The TLSs were categorized according to their anatomic subregions and quantified, and a TLS scoring system was established for intratumor region (T score) and peritumor region (P score). Differences in relapse-free survival (RFS) and overall survival (OS) between groups were determined. Multiplex immunohistochemical staining (mIHC) was used to determine the cellular composition of TLSs in 40 CRCLM patients.

RESULTS

T score positively correlated with superior prognosis, while P score negatively associated with poor survival (all p<0.05). Meanwhile, T score was positively associated with specific mutation subtype of KRAS. Furthermore, TLSs enrichment gene expression was significantly associated with survival and transcriptomic subtypes of CRCLM. Subsequently, mIHC showed that the densities of Treg cells, M2 macrophages and Tfh cells were significantly higher in intratumor TLSs than in peritumor TLSs (p=0.029, p=0.047 and p=0.041, respectively), and the frequencies of Treg cells and M2 macrophages were positively correlated with P score, while the frequencies of Tfh cells were positively associated with T scores in intratumor TLSs (all p<0.05). Next, based on the distribution and abundance of TLSs, an Immune Score combining T score and P score was established which categorized CRCLM patients into four immune classes with different prognosis (all p<0.05). Among them, patients with higher immune class have more favorable prognoses. The C-index of Immune Class for RFS and OS was higher than Clinical Risk Score statistically. These results were also confirmed by the other two validation cohorts.

CONCLUSIONS

The distribution and abundance of TLSs is significantly associated with RFS and OS of CRCLM patients, and a novel immune class was proposed for predicting the prognosis of CRCLM patients.

Comprehensive analysis of autophagy-related clusters and individual risk model for immunotherapy response prediction in gastric cancer

Introduction

Autophagy can be triggered by oxidative stress and is a double-edged sword involved in the progression of multiple malignancies. However, the precise roles of autophagy on immune response in gastric cancer (GC) remain clarified.

Methods

We endeavor to explore the novel autophagy-related clusters and develop a multi-gene signature for predicting the prognosis and the response to immunotherapy in GC. A total of 1505 patients from eight GC cohorts were categorized into two subtypes using consensus clustering. We compare the differences between clusters by the multi-omics approach. Cox and LASSO regression models were used to construct the prognostic signature.

Results

Two distinct clusters were identified. Compared with cluster 2, the patients in cluster 1 have favorable survival outcomes and lower scores for epithelial-mesenchymal transition (EMT). The two subtypes are further characterized by high heterogeneity concerning immune cell infiltration, somatic mutation pattern, and pathway activity by gene set enrichment analysis (GSEA). We obtained 21 autophagy-related differential expression genes (DEGs), in which PTK6 amplification and BCL2/CDKN2A deletion were highly prevalent. The four-gene (PEA15, HSPB8, BNIP3, and GABARAPL1) risk signature was further constructed with good predictive performance and validated in 3 independent datasets including our local Tianjin cohort. The risk score was proved to be independent prognostic factor. A prognostic nomogram showed robust validity of GC survival. The risk score was significantly associated with immune cell infiltration status, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint molecules. Furthermore, the model was efficient for predicting the response to tumor-targeted agent and immunotherapy and verified by the IMvigor210 cohort. This model is also capable of discriminating between low and high-risk patients receiving chemotherapy.

Conclusion

Altogether, our exploratory research on the landscape of autophagy-related patterns may shed light on individualized therapies and prognosis in GC.

Identification and validation of prognostic autophagy-related genes associated with immune microenvironment in human gastric cancer

Autophagy-related genes (ATGs) play critical roles in tumorigenesis and progression in gastric cancer (GC). The present study aimed to identify immune-based prognostic ATGs and verify their functions in tumor immune microenvironment (TIME) in GC. Macrophage infiltration was found to negatively correlate with prognosis in GC patients. After stratifying by infiltration levels of macrophages, we screened The Cancer Genome Atlas and Human Autophagy Database to identify the differentially expressed ATGs (DE-ATGs). Of 1,433 differentially expressed genes between the two groups, seven genes qualified as DE-ATGs. Of these, CXCR4, DLC1, and MAP1LC3C, exhibited strong prognostic prediction ability in Kaplan-Meier survival–log-rank test. High expression of these genes correlated with increased occurrence of advanced grade 3 tumors and poor prognoses. Furthermore, GSEA indicated that they were significantly associated with oncogenic and immune-related pathways. The comprehensive evaluation of TIME via GEPIA, ESTIMATE, CIBERSORT, and TIMER suggested that the three DE-ATGs were closely associated with immune condition, both in terms of immune cells and immune scores. Thus, the outcome of this study may aid in better understanding of the ATGs and their interaction with the immune microenvironment, which would allow the development of novel inhibitors, personalized treatment, and immunotherapy in gastric cancer.

Pan-Cancer Analyses Reveal Oncogenic and Immunological Role of PLOD2

Some previous studies have shown that PLOD2 has some value in tumorigenesis. However, the broad significance of PLOD2 has not been discussed in depth. This study was aimed at elaborated and summarized the value of PLOD2 in various tumors. First, we integrated GTEx, The Cancer Genome Atlas and Cancer Cell Line Encyclopedia databases to analyze the expression of PLOD2, and found that it was expressed differently in normal tissues and significantly highly expressed in most tumors compared with normal tissues. Second, our analysis revealed that PLOD2 expression was negatively correlated with the prognosis of several tumors. For gastric cancer, the median overall survival time was significantly higher in the PLOD2 low expression group [HR 0.616 (95%CI 0.442–0.858), p = 0.004]. Third, for tumor immunity, PLOD2 was significantly associated with tumor infiltration, including immune infiltrating cells; immune checkpoint expression; immune microenvironment scores (immune score, stromal score and estimate scores); immunotherapy-related scores (tumor mutational burden, microsatellite instability, tumor neoantigen burden); expression of DNA repair genes Mismatch Repairs and methyltransferase; and enrichment analyses identified PLOD2-associated terms and pathways. Lastly, twenty pairs of gastric cancer and adjacent immunohistochemistry showed that PLOD2 was significantly overexpressed in gastric cancer (p < 0.001). Collectively, PLOD2 played a significant role in tumorigenesis and maybe serve as a potential biomarker for diagnosis and prognosis in cancers.

Inhibitory Mechanism of Combined Hydroxychavicol With Epigallocatechin-3-Gallate Against Glioma Cancer Cell Lines: A Transcriptomic Analysis

Emerging reports have shown therapeutic potential of hydroxychavicol (HC) and epigallocatechin-3-gallate (EGCG) against cancer cells, however high concentrations are required to achieve the anticancer activity. We reported the synergy of low combination doses of EGCG+HC in glioma cell lines 1321N1, SW1783, and LN18 by assessing the effects of EGCG+HC through functional assays. Using high throughput RNA sequencing, the molecular mechanisms of EGCG+HC against glioma cell lines were revealed. EGCG/HC alone inhibited the proliferation of glioma cell lines, with IC50 values ranging from 82 to 302 µg/ml and 75 to 119 µg/ml, respectively. Sub-effective concentrations of combined EGCG+HC enhanced the suppression of glioma cell growth, with SW1783 showing strong synergism with a combination index (CI) of 0.55 and LN18 showing a CI of 0.51. A moderate synergistic interaction of EGCG+HC was detected in 1321N1 cells, with a CI value of 0.88. Exposure of 1321N1, SW1783, and LN18 cells to EGCG+HC for 24 h induces cell death, with caspase-3 activation rates of 52%, 57%, and 9.4%, respectively. However, the dose for SW1783 is cytotoxic to normal cells, thus this dose was excluded from other tests. EGCG+HC induced cell cycle arrest at S phase and reduced 1321N1 and LN18 cell migration and invasion. Combined EGCG+HC amplified its anticancer effect by downregulating the axon guidance process and metabolic pathways, while simultaneously interfering with endoplasmic reticulum unfolded protein response pathway. Furthermore, EGCG+HC exerted its apoptotic effect through the alteration of mitochondrial genes such as MT-CO3 and MT-RNR2 in 1321N1 and LN18 cells respectively. EGCG+HC dynamically altered DYNLL1 alternative splicing expression in 1321N1 and DLD splicing expression in LN18 cell lines. Our work indicated the pleiotropic effects of EGCG+HC treatment, as well as particular target genes that might be investigated for future glioma cancer therapeutic development.

Extracellular Matrix-Associated Pathways Promote the Progression of Gastric Cancer by Impacting the Dendritic Cell Axis

BACKGROUND

Gastric cancer (GC) is the third most frequent malignant tumour in the Chinese population, let alone the whole world. Recently, most prognostic models have only focused on the levels of several genes, miRNAs, lncRNAs, gene mutations, or DNA methylation; however, the activation status of biological pathways is more stable and can reflect the comprehensive inner conditions of tumours.

METHODS

We collected samples from the Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) cohort and GSE62254 cohort, with a total of 594 patients. We employed GSEA to first compare the diverse activated signalling pathways between dead GC patients and living patients. The least absolute shrinkage and selection operator (LASSO) regression analysis was subsequently performed by the "glmnet" package to generate a prognostic signature.

RESULTS

We extracted a total of 218 genes from the KEGG Focal Adhesion and KEGG ECM Receptor Interaction pathways, which showed significant activation in dead GC patients in two enrolled cohorts, for subsequent LASSO analysis. In the TCGA-STAD cohort, patients in the high-risk group faced a significantly poorer prognosis than those in the low-risk group (P < 0.001, HR: 4.62, 95% CI: 3.447-6.183), with an AUC of 0.694. In the GSE62254 cohort, the HR value was 4.94 (95% CI: 3.413-7.165), and the AUC value was as high as 0.834. A high-risk score and poor prognosis correlated with infiltrated dendritic cells, and the receptor of IFN-α was also positively linked with the risk score, as well as poor prognosis. GC patients with high-risk scores were more likely to respond to CTLA4 treatment but not PD1 treatment.

CONCLUSION

Taken together, we established and verified an extracellular matrix prognostic model of gastric cancer patients. The model can be used to evaluate the risk of death of GC patients, as well as the response to anti-CTLA4 immunotherapy.

Identification and Validation of Autophagy-Related Gene Nomograms to Predict the Prognostic Value of Patients with Cervical Cancer

Autophagy is a process of engulfing one's own cytoplasmic proteins or organelles and coating them into vesicles, fusing with lysosomes to form autophagic lysosomes, and degrading the contents it encapsulates. Increasing studies have shown that autophagy disorders are closely related to the occurrence of tumors. However, the prognostic role of autophagy genes in cervical cancer is still unclear. In this study, we constructed risk signatures of autophagy-related genes (ARGs) to predict the prognosis of cervical cancer. The expression profiles and clinical information of autophagy gene sets were downloaded from TCGA and GSE52903 queues as training and validation sets. The normal cervical tissue expression profile data from the UCSC XENA website (obtained from GTEx) were used as a supplement to the TCGA normal cervical tissue. Univariate COX regression analysis of 17 different autophagy genes was performed with the consensus approach. Tumor samples from TCGA were divided into six subtypes, and the clinical traits of the six subtypes had different distributions. Further absolute shrinkage and selection operator (LASSO) and multivariable COX regression yielded an autophagy genetic risk model consisting of eight genes. In the training set, the survival rate of the high-risk group was lower than that of the low-risk group (p < 0.0001). In the validation set, the AUC area of the receiver operating characteristic (ROC) curve was 0.772 for the training set and 0.889 for the verification set. We found that high and low risk scores were closely related to TNM stage (p < 0.05). The nomogram shows that the risk score combined with other indicators, such as G, T, M, and N, better predicts 1-, 3-, and 5-year survival rates. Decline curve analysis (DCA) shows that the risk model combined with other indicators produces better clinical efficacy. Immune cells with an enrichment score of 28 showed statistically significant differences related to high and low risk. GSEA enrichment analysis showed the main enrichment being in KRAS activation, genes defining epithelial and mesenchymal transition (EMT), raised in response to the low oxygen level (hypoxia) gene and NF-kB in response to TNF. These pathways are closely related to the occurrence of tumors. Our constructed autophagy risk signature may be a prognostic tool for cervical cancer.