A novel immune-related gene signature predicting survival in sarcoma patients

Prioritizing Context-Dependent Cancer Gene Signatures in Networks

There are numerous ways of portraying cancer complexity based on combining multiple types of data. A common approach involves developing signatures from gene expression profiles to highlight a few key reproducible features that provide insight into cancer risk, progression, or recurrence. Normally, a selection of such features is made through relevance or significance, given a reference context. In the case of highly metastatic cancers, numerous gene signatures have been published with varying levels of validation. Then, integrating the signatures could potentially lead to a more comprehensive view of the connection between cancer and its phenotypes by covering annotations not fully explored in individual studies. This broader understanding of disease phenotypes would improve the predictive accuracy of statistical models used to identify meaningful associations. We present an example of this approach by reconciling a great number of published signatures into meta-signatures relevant to Osteosarcoma (OS) metastasis. We generate a well-annotated and interpretable interactome network from integrated OS gene expression signatures and identify key nodes that regulate essential aspects of metastasis. While the connected signatures link diverse prognostic measurements for OS, the proposed approach is applicable to any type of cancer.

Metabolic targeting of cancer associated fibroblasts overcomes T-cell exclusion and chemoresistance in soft-tissue sarcomas

T cell-based immunotherapies have exhibited promising outcomes in tumor control; however, their efficacy is limited in immune-excluded tumors. Cancer-associated fibroblasts (CAFs) play a pivotal role in shaping the tumor microenvironment and modulating immune infiltration. Despite the identification of distinct CAF subtypes using single-cell RNA-sequencing (scRNA-seq), their functional impact on hindering T-cell infiltration remains unclear, particularly in soft-tissue sarcomas (STS) characterized by low response rates to T cell-based therapies. In this study, we characterize the STS microenvironment using murine models (in female mice) with distinct immune composition by scRNA-seq, and identify a subset of CAFs we termed glycolytic cancer-associated fibroblasts (glyCAF). GlyCAF rely on GLUT1-dependent expression of CXCL16 to impede cytotoxic T-cell infiltration into the tumor parenchyma. Targeting glycolysis decreases T-cell restrictive glyCAF accumulation at the tumor margin, thereby enhancing T-cell infiltration and augmenting the efficacy of chemotherapy. These findings highlight avenues for combinatorial therapeutic interventions in sarcomas and possibly other solid tumors. Further investigations and clinical trials are needed to validate these potential strategies and translate them into clinical practice.

Prognostic and immunological implications of paraptosis-related genes in lung adenocarcinoma: Comprehensive analysis and functional verification of hub gene

BACKGROUND

Lung adenocarcinoma (LUAD) poses significant clinical challenges due to its inherent heterogeneity and variable response to treatment. Recent research has specifically focused on elucidating the role of Paraptosis-related genes (PRGs) in the progression of cancer and the prognosis of patients.

METHODS

We conducted a comprehensive analysis of the differential expression of PRGs in LUAD. Additionally, univariate Cox regression analysis was utilized to determine the prognostic significance of these genes. Furthermore, consensus clustering was employed to differentiate molecular subtypes within LUAD, while immune heterogeneity was assessed. To evaluate treatment outcomes, the expression of immune checkpoint inhibitors was examined, and the sensitivity of LUAD patients to chemotherapy drugs was assessed. Moreover, machine learning algorithms were employed to construct a Paraptosis-related risk score with prognostic and immunological indicators. Finally, to validate the findings, in vitro experiments were performed to verify the regulatory effect of key PRGs on Paraptosis.

RESULTS

Our analysis identified 24 PRGs that exhibited differential expression, with CDKN3, TP53, and PHB emerging as the most prominently upregulated genes in tumor tissues. Among these genes, seven were identified as prognostic markers, with HSPB8 being the sole protective factor. Notably, our analysis also revealed the existence of two distinct molecular subtypes within LUAD, each characterized by unique prognoses and immune responses. Specifically, Subtype B displayed a poorer prognosis but demonstrated increased sensitivity to both chemotherapy and immunotherapy. In addition, our development of a Paraptosis-Associated Risk Score yielded a significant prognostic value in predicting patient outcomes. Furthermore, we found regulatory effect of CDKN3 on Paraptosis in two cell lines.

CONCLUSIONS

Our study highlights the importance of PRGs in LUAD, particularly in prognosis and treatment response. The identified molecular subtypes and Paraptosis-Associated Risk Score offer valuable insights for personalized treatment strategies.

Prognostic characteristics of a six-gene signature based on ssGSEA in sarcoma

BACKGROUND

Sarcoma is a rare malignant tumor originating of the interstitial or connective tissue with a poor prognosis. Next-generation sequencing technology offers new opportunities for accurate diagnosis and treatment of sarcomas. There is an urgent need for new gene signature to predict prognosis and evaluate treatment outcomes.

METHODS

We used transcriptome data from the Cancer Genome Atlas (TCGA) database and single sample gene set enrichment analysis (ssGSEA) to explore the cancer hallmarks most associated with prognosis in sarcoma patients. Then, weighted gene coexpression network analysis, univariate COX regression analysis and random forest algorithm were used to construct prognostic gene characteristics. Finally, the prognostic value of gene markers was validated in the TCGA and Integrated Gene Expression (GEO) (GSE17118) datasets, respectively.

RESULTS

MYC targets V1 and V2 are the main cancer hallmarks affecting the overall survival (OS) of sarcoma patients. A six-gene signature including VEGFA, HMGB3, FASN, RCC1, NETO2 and BIRC5 were constructed. Kaplan-Meier analysis suggested that higher risk scores based on the six-gene signature associated with poorer OS (P < 0.001). The receiver Operating characteristic curve showed that the risk score based on the six-gene signature was a good predictor of sarcoma, with an area under the curve (AUC) greater than 0.73. In addition, the prognostic value of the six-gene signature was validated in GSE17118 with an AUC greater than 0.72.

CONCLUSION

This six-gene signature is an independent prognostic factor in patients with sarcoma and is expected to be a potential therapeutic target for sarcoma.

Innovative Breakthroughs for the Treatment of Advanced and Metastatic Synovial Sarcoma

Synovial sarcoma (SyS) is a rare aggressive soft tissue sarcoma carrying the chromosomal translocation t(X;18), encoding the fusion transcript SS18::SSX. The fusion oncoprotein interacts with both BAF enhancer complexes and polycomb repressor complexes, resulting in genome-wide epigenetic perturbations and a unique altered genetic signature. Over 80% of the patients are initially diagnosed with localized disease and have a 5-year survival rate of 70-80%, but metastatic relapse occurs in 50% of the cases. Advanced, unresectable, or metastatic disease has a 5-year survival rate below 10%, representing a critical issue. This review summarizes the molecular mechanisms behind SyS and illustrates current treatments in front line, second line, and beyond settings. We analyze the use of immune check point inhibitors (ICI) in SyS that do not behave as an ICI-sensitive tumor, claiming the need for predictive genetic signatures and tumor immune microenvironment biomarkers. We highlight the clinical translation of innovative technologies, such as proteolysis targeting chimera (PROTAC) protein degraders or adoptive transfer of engineered immune cells. Adoptive cell transfer of engineered T-cell receptor cells targeting selected cancer/testis antigens has shown promising results against metastatic SyS in early clinical trials and further improvements are awaited from refinements involving immune cell engineering and tumor immune microenvironment enhancement.

Transcriptomics-Based Subphenotyping of the Human Placenta Enabled by Weighted Correlation Network Analysis in Early-Onset Preeclampsia With and Without Fetal Growth Restriction

BACKGROUND

Placental disorders contribute to pregnancy complications, including preeclampsia and fetal growth restriction (FGR), but debate regarding their specific pathobiology persists. Our objective was to apply transcriptomics with weighted gene correlation network analysis to further clarify the placental dysfunction in these conditions.

METHODS

We performed RNA sequencing with weighted gene correlation network analysis using human placental samples (n=30), separated into villous tissue and decidua basalis, and clinically grouped as follows: (1) early-onset preeclampsia (EOPE)+FGR (n=7); (2) normotensive, nonanomalous preterm FGR (n=5); (2) EOPE without FGR (n=8); (4) spontaneous idiopathic preterm birth (n=5) matched for gestational age; and (5) uncomplicated term births (n=5). Our data was compared with RNA sequencing data sets from public databases (GSE114691, GSE148241, and PRJEB30656; n=130 samples).

RESULTS

We identified 14 correlated gene modules in our specimens, of which most were significantly correlated with birthweight and maternal blood pressure. Of the 3 network modules consistently predictive of EOPE±FGR across data sets, we prioritized a coexpression gene group enriched for hypoxia-response and metabolic pathways for further investigation. Cluster analysis based on transcripts from this module and the glycolysis/gluconeogenesis metabolic pathway consistently distinguished a subset of EOPE±FGR samples with an expression signature suggesting modified tissue bioenergetics. We demonstrated that the expression ratios of LDHA/LDHB and PDK1/GOT1 could be used as surrogate indices for the larger panels of genes in identifying this subgroup.

CONCLUSIONS

We provide novel evidence for a molecular subphenotype consistent with a glycolytic metabolic shift that occurs more frequently but not universally in placental specimens of EOPE±FGR.

A nomogram for predicting survival in patients with advanced (stage III/IV) pancreatic body tail cancer: a SEER-based study

OBJECTIVE

Pancreatic body tail carcinoma (PBTC) is a relatively few pancreatic cancer in clinical practice, and its specific clinicopathological features and prognosis have not been fully described. In this study, we aimed to create a nomogram to predict the overall survival (OS) of patients with advanced PBTC.

METHODS

We extracted clinical and related prognostic data of advanced PBTC patients from 2000 to 2018 from the Surveillance, Epidemiology, and End Results database. Independent prognostic factors were selected using univariate and multivariate Cox analyses, and a nomogram was constructed using R software. The C-index, area under the curve (AUC) of receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA) were used to assess the clinical utility of the nomogram. Finally, OS was assessed using the Kaplan-Meier method.

RESULTS

A total of 1256 patients with advanced PBTC were eventually included in this study. Age, grade, N stage, M stage, surgery, and chemotherapy were identified as independent risk factors using univariate and multivariate Cox regression analyses (p < 0.05). In the training cohort, the calibration index of the nomogram was 0.709, while the AUC values of the nomogram, age, grade, N stage, M stage, surgery, and chemotherapy were 0.777, 0.562, 0.621, 0.5, 0.576, 0.632, and 0.323, respectively. Meanwhile, in the validation cohort, the AUC values of the nomogram, age, grade, N stage, M stage, surgery, and chemotherapy were 0.772, 0.551, 0.629, 0.534, 0.577, 0.606, and 0.639, respectively. Good agreement of the model in the training and validation cohorts was demonstrated in the calibration and DCA curves. Univariate survival analysis showed a statistically significant effect of age, grade, M stage, and surgery on prognosis (p < 0.05).

CONCLUSION

Age, grade, M stage, and surgery were independently associated with OS, and the established nomogram was a visual tool to effectively predict OS in advanced PBTC patients.

Integration of immune and hypoxia gene signatures improves the prediction of radiosensitivity in breast cancer

Immunity and hypoxia are two important factors that affect the response of cancer patients to radiotherapy. At the same time, considering the limited predictive value of a single predictive model and the uncertainty of grouping patients near the cutoff value, we developed and validated a combined model based on immune- and hypoxia-related gene expression profiles to predict the radiosensitivity of breast cancer patients. This study was based on breast cancer data from The Cancer Genome Atlas (TCGA). Spike-and-slab Lasso regression analysis was performed to select three immune-related genes and develop a radiosensitivity model. Lasso Cox regression modeling selected 11 hypoxia-related genes for development of radiosensitivity model. Three independent datasets (Molecular Taxonomy of Breast Cancer International Consortium [METABRIC], E-TABM-158, GSE103746) were used to validate the predictive value of radiosensitivity signatures. In the TCGA dataset, the 10-year survival probabilities of the immune radioresistant (IRR) and hypoxia radioresistant (HRR) groups were 0.189 (0.037, 0.973) and 0.477 (0.293, 0.776), respectively. The 10-year survival probabilities of the immune radiosensitive (IRS) and hypoxia radiosensitive (HRS) groups were 0.778 (0.676, 0.895) and 0.824 (0.723, 0.939), respectively. Based on these two gene signatures, we further constructed a combined model and divided all patients into three groups (IRS/HRS, mixed, IRR/HRR). We identified the IRS/HRS patients most likely to benefit from radiotherapy; the 10-year survival probability was 0.886 (0.806, 0.976). The 10-year survival probability of the IRR/HRR group was 0. In conclusion, a combined model integrating immune- and hypoxia-related gene signatures could effectively predict the radiosensitivity of breast cancer and more accurately identify radiosensitive and radioresistant patients than a single model.

Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database

Objective

We aimed to identify feature immune-related genes that correlated with graft rejection and to develop a prognostic model based on immune-related genes in kidney transplantation.

Methods

Gene expression profiles were obtained from the GEO database. The GSE36059 dataset was used as a discovery cohort. Then, differential expression analysis and a machine learning method were performed to select feature immune-related genes. After that, univariate and multivariate Cox regression analyses were used to identify prognosis-related genes. A novel Riskscore model was built based on the results of multivariate regression. The levels of these feature genes were also confirmed in an independent single-cell dataset and other GEO datasets.

Results

15 immune-related genes were expressed differently between non-rejection and rejection kidney allografts. Those differentially expressed immune-related genes (DE-IRGs) were mainly associated with immune-related biological processes and pathways. Subsequently, a 5-immune-gene signature was constructed and showed favorable predictive results in the GSE21374 dataset. Recipients were divided into the high-risk and low-risk groups according to the median value of RiskScore. The GO and KEGG analysis indicated that the differentially expressed genes (DEGs) between high-risk and low-risk groups were mainly involved in inflammatory pathways, chemokine-related pathways, and rejection-related pathways. Immune infiltration analysis demonstrated that RiskScore was potentially related to immune infiltration. Kaplan-Meier survival analysis suggested that recipients in the high-risk group had poor graft survival. AUC values of 1- and 3-year graft survival were 0.804 and 0.793, respectively.

Conclusion

Our data suggest that this immune-related prognostic model had good sensitivity and specificity in predicting the 1- and 3-year kidney graft survival and might act as a useful tool for predicting kidney graft loss.