Identification and Development of Subtypes With Poor Prognosis in Pan-Gynecological Cancer Based on Gene Expression in the Glycolysis-Cholesterol Synthesis Axis

Research on molecular characteristics of ADME-related genes in kidney renal clear cell carcinoma

Genes involved in drug absorption, distribution, metabolism, and excretion (ADME) are named ADME genes. However, the comprehensive role of ADME genes in kidney renal clear cell carcinoma (KIRC) remains unclear. Using the clinical and gene expression data of KIRC patients downloaded from The Cancer Genome Atlas (TCGA), ArrayExpress, and the Gene Expression Omnibus (GEO) databases, we cluster patients into two patterns, and the population with a relatively poor prognosis demonstrated higher level of immunosuppressive cell infiltration and higher proportion of glycolytic subtypes. Then, 17 ADME genes combination identified through the least absolute shrinkage and selection operator algorithm (LASSO, 1000 times) was utilized to calculate the ADME score. The ADME score was found to be an independent predictor of prognosis in KIRC and to be tightly associated with the infiltration level of immune cells, metabolic properties, tumor-related signaling pathways, genetic variation, and responses to chemotherapeutics. Our work revealed the characteristics of ADME in KIRC. Assessing the ADME profiles of individual patients can deepen our comprehension of tumor microenvironment (TME) features in KIRC and can aid in developing more personalized and effective therapeutic strategies.

Identification and Validation a Necroptosis-Related Prognostic Signature in Cervical Cancer

Necroptosis is a promising novel target for cervical cancer therapy. Nevertheless, differentially expressed necroptosis-related genes (NRGs) in cervical cancer and their associations with prognosis are far from fully clarified. In this study, differentially expressed NRGs (DE-NRGs) were screened out and their bio-function was elucidated. Subsequently, a prognostic scoring model based on the regression coefficients of the screened out NRGs and their corresponding mRNA expressions were constructed and validated. Finally, the survival probability of cervical cancer patients based on the constructed prognostic scoring model in 3 and 5 years was predicted and assessed. We found 17 DE-NRGs in cervical cancer tissues which were closely related to cancer progression, and most of them were significantly highly expressed. Furthermore, 3 NRG were confirmed as the prognostic signature genes from 17 DE-NRGs by regression analysis. Overall survival predicted through our prognostic scoring model was lower in the high-risk group than in the low-risk group (p < 0.05) in both the TCGA cohort and the external GEO44001 validation cohort. What's more, the prediction performance of our prognostic scoring models well verified by the ROC curve, and the risk score calculated could act as an independent prognostic factor for cervical cancer patients. The calibration curve and C-index (0.776) of the nomogram analysis suggested that the predictive performance of the nomogram was satisfactory. Our study identified and validated a necroptosis-related prognostic signature in cervical cancer, which could well predict the prognosis for cervical cancer patients.

Interaction between glycolysis‒cholesterol synthesis axis and tumor microenvironment reveal that gamma-glutamyl hydrolase suppresses glycolysis in colon cancer

Background

Metabolic reprogramming is a feature of cancer. However, colon cancer subtypes based on the glycolysis‒cholesterol synthesis axis have not been identified, and little is known about connections between metabolic features and the tumor microenvironment.

Methods

Data for 430 colon cancer cases were extracted from The Cancer Genome Atlas, including transcriptome data, clinical information, and survival outcomes. Glycolysis and cholesterol synthesis-related gene sets were obtained from the Molecular Signatures Database for a gene set variation analysis. The relationship between the genomic landscape and immune landscape were investigated among four metabolic subtypes. Hub genes were determined. The clinical significance of candidate hub gene was evaluated in 264 clinical samples and potential functions were validated in vitro and in vivo.

Results

Colon cancer cases were clustered into four metabolic subtypes: quiescent, glycolytic, cholesterogenic, and mixed. The metabolic subtypes differed with respect to the immune score, stromal score, and estimate score using the ESTIMATE algorithm, cancer-immunity cycle, immunomodulator signatures, and signatures of immunotherapy responses. Patients in the cholesterogenic group had better survival outcomes than those for other subtypes, especially glycolytic. The glycolytic subtype was related to unfavorable clinical characteristics, including high mutation rates in TTN, APC, and TP53, high mutation burden, vascular invasion, right colon cancer, and low-frequency microsatellite instability. GGH, CACNG4, MME, SLC30A2, CKMT2, SYN3, and SLC22A31 were identified as differentially expressed both in glycolytic-cholesterogenic subgroups as well as between colon cancers and healthy samples, and were involved in glycolysis‒cholesterol synthesis. GGH was upregulated in colon cancer; its high expression was correlated with CD4⁺ T cell infiltration and longer overall survival and it was identified as a favorable independent prognostic factor. The overexpression of GGH in colon cancer-derived cell lines (SW48 and SW480) inhibited PKM, GLUT1, and LDHA expression and decreased the extracellular lactate content and intracellular ATP level. The opposite effects were obtained by GGH silencing. The phenotype associated with GGH was also validated in a xenograft nude mouse model.

Conclusions

Our results provide insight into the connection between metabolism and the tumor microenvironment in colon cancer and provides preliminary evidence for the role of GGH, providing a basis for subsequent studies.

A Wnt-related gene expression signature to improve the prediction of prognosis and tumor microenvironment in gastric cancer

Background: Most gastric cancer (GC) patients were diagnosed in the advanced stages without obvious symptoms, which resulted in the increased risk of death. Although the combination therapies have showed survival benefit of patients, there is still urgent need to explore the underlying mechanisms of GC development and potential novel targets for clinical applications. Numerous studies have reported the upregulation of Wnt signaling pathway in human GC, which play important role during GC development and progression. However, the current understanding of Wnt signaling pathway is still limited due to its complexity and contradictory effect on different stages of GC tumor microenvironment. Method: We used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset to screen Wnt signaling pathway-associated genes by ssGSEA and correlation analysis. Three molecular subtypes were constructed based on a consistent clustering analysis. The key Wnt-related genes were screened through univariate cox analysis, lasso, and stepwise regression. In addition, the Gene Set Enrichment Analysis (GSEA) were performed to explore potential molecular pathways regulated by the Wnt-related gene signatures. ESTIMATE was utilized for evaluating the immune cell populations in GC tumor microenvironment. Results: Three molecular subtypes associated to Wnt were identified, and 7 key Wnt-related genes were screened to establish a predictive RiskScore model. These three molecular subtypes showed significant prognostic differences and distinct functional signaling pathways. We also found the downregulated immune checkpoint expression in the clust1 with good prognosis. The RiskScore model was successfully validated in GSE26942 dataset. Nomogram based on RiskScore and Gender had better prognostic predictive ability. Conclusion: In summary, our study showed that the Wnt-related genes could be used to predict prognosis of GC patients. The risk model we established showed high accuracy and survival prediction capability.

Assessing Metabolic Risk Factors for LVSI in Endometrial Cancer: A Cross-Sectional Study

Objective

This study analyzed metabolic factors associated with lymphovascular space invasion (LVSI) and compared the difference between type 1 and type 2 endometrial cancer (EC).

Methods

Four hundred patients primarily diagnosed with EC who underwent hysterectomy with pathological results at Fujian Medical University Cancer Hospital from January 2019 to January 2021 were included. Demographic variable data were collected as well as pathological results. Laboratory evaluations included fasting blood glucose (FBG), serum cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A (Apo A) and apolipoprotein B (Apo B). Characterization of binary logistic regression models was used to test the odds ratios (ORs) between LVSI and its metabolic parameters with different subtypes of EC.

Results

The results indicated that CA125, ROMA, Ki67 score, FBG and TC were higher in EC patients with LVSI (all p<0.05). Negative ER and PR expression was positively associated with LVSI (P<0.05). CA125, ROMA, FBG, TC and ER were found to be independent risk factors for LVSI. CA125, ROMA and FBG were significantly elevated in type 1 EC patients with LVSI compared with those without LVSI (all p<0.05). TC and Ki67 scores were much higher in type 2 EC patients with vs without LVSI (all p<0.05). Negative PR expression was positively related to both type 1 and type 2 EC patients with LVSI. Consequently, CA125, ROMA, FBG and Apo B were found to be independent risk factors for LVSI in type 1 EC, and TC was found to be an independent risk factor for LVSI in type 2 EC.

Conclusion

FBG and TC were both independent risk factors for LVSI in EC. FBG and Apo B were independent risk factors for LVSI in type 1 EC. TC was an independent risk factor for LVSI in type 2 EC.

The functional roles of the circRNA/Wnt axis in cancer

CircRNAs, covalently closed noncoding RNAs, are widely expressed in a wide range of species ranging from viruses to plants to mammals. CircRNAs were enriched in the Wnt pathway. Aberrant Wnt pathway activation is involved in the development of various types of cancers. Accumulating evidence indicates that the circRNA/Wnt axis modulates the expression of cancer-associated genes and then regulates cancer progression. Wnt pathway-related circRNA expression is obviously associated with many clinical characteristics. CircRNAs could regulate cell biological functions by interacting with the Wnt pathway. Moreover, Wnt pathway-related circRNAs are promising potential biomarkers for cancer diagnosis, prognosis evaluation, and treatment. In our review, we summarized the recent research progress on the role and clinical application of Wnt pathway-related circRNAs in tumorigenesis and progression.

Molecular Characteristics of T Cell-Mediated Tumor Killing in Hepatocellular Carcinoma

BACKGROUND

Although checkpoint blockade is a promising approach for the treatment of hepatocellular carcinoma (HCC), subsets of patients expected to show a response have not been established. As T cell-mediated tumor killing (TTK) is the fundamental principle of immune checkpoint inhibitor therapy, we established subtypes based on genes related to the sensitivity to TKK and evaluated their prognostic value for HCC immunotherapies.

METHODS

Genes regulating the sensitivity of tumor cells to T cell-mediated killing (referred to as GSTTKs) showing differential expression in HCC and correlations with prognosis were identified by high-throughput screening assays. Unsupervised clustering was applied to classify patients with HCC into subtypes based on the GSTTKs. The tumor microenvironment, metabolic properties, and genetic variation were compared among the subgroups. A scoring algorithm based on the prognostic GSTTKs, referred to as the TCscore, was developed, and its clinical and predictive value for the response to immunotherapy were evaluated.

RESULTS

In total, 18 out of 641 GSTTKs simultaneously showed differential expression in HCC and were correlated with prognosis. Based on the 18 GSTTKs, patients were clustered into two subgroups, which reflected distinct TTK patterns in HCC. Tumor-infiltrating immune cells, immune-related gene expression, glycolipid metabolism, somatic mutations, and signaling pathways differed between the two subgroups. The TCscore effectively distinguished between populations with different responses to chemotherapeutics or immunotherapy and overall survival.

CONCLUSIONS

TTK patterns played a nonnegligible role in formation of TME diversity and metabolic complexity. Evaluating the TTK patterns of individual tumor will contribute to enhancing our cognition of TME characterization, reflects differences in the functionality of T cells in HCC and guiding more effective therapy strategies.