Association between high immune activity and worse prognosis in uveal melanoma and low-grade glioma in TCGA transcriptomic data

Mechanisms of blood-retinal barrier disruption related to intraocular inflammation and malignancy

Blood-retinal barrier (BRB) disruption is a common accompaniment of intermediate, posterior and panuveitis causing leakage into the retina and macular oedema resulting in vision loss. It is much less common in anterior uveitis or in patients with intraocular lymphoma who may have marked signs of intraocular inflammation. New drugs used for chemotherapy (cytarabine, immune checkpoint inhibitors, BRAF inhibitors, EGFR inhibitors, bispecific anti-EGFR inhibitors, MET receptor inhibitors and Bruton tyrosine kinase inhibitors) can also cause different types of uveitis and BRB disruption. As malignant disease itself can cause uveitis, particularly from breast, lung and gastrointestinal tract cancers, it can be clinically difficult to sort out the cause of BRB disruption. Immunosuppression due to malignant disease and/or chemotherapy can lead to infection which can also cause BRB disruption and intraocular infection. In this paper we address the pathophysiology of BRB disruption related to intraocular inflammation and malignancy, methods for estimating the extent and effect of the disruption and examine why some types of intraocular inflammation and malignancy cause BRB disruption and others do not. Understanding this may help sort and manage these patients, as well as devise future therapeutic approaches.

Reduced YAP1 and FOLR1 in gliomas predict better response to chemotherapeutics

Gliomas harbouring mutations in IDH1 (isocitrate dehydrogenase 1) are characterized by greater sensitivity to chemotherapeutics. These mutants also exhibit diminished levels of transcriptional coactivator YAP1 (yes-associated protein 1). Enhanced DNA damage in IDH1 mutant cells, as evidenced by γH2AX formation (phosphorylation of histone variant H2A.X) and ATM (serine/threonine kinase; ataxia telangiectasia mutated) phosphorylation, was accompanied by reduced FOLR1 (folate receptor 1) expression. Diminished FOLR1, concomitant with heightened γH2AX levels, was also observed in patient-derived IDH1 mutant glioma tissues. Chromatin immunoprecipitation, overexpression of mutant YAP1, and treatment with YAP1-TEAD (TEA domain transcription factors) complex inhibitor verteporfin demonstrated regulation of FOLR1 expression by YAP1 and its partner transcription factor TEAD2. TCGA (The Cancer Genome Atlas) data analysis demonstrated better patient survival with reduced FOLR1 expression. Depletion of FOLR1 rendered IDH1 wild-type gliomas more susceptible to temozolomide-mediated death. Despite heightened DNA damage, IDH1 mutants exhibited reduced levels of IL6 (interleukin 6) and IL8 (interleukin 8) - pro-inflammatory cytokines known to be associated with persistent DNA damage. While both FOLR1 and YAP1 influenced DNA damage, only YAP1 was involved in regulating IL6 and IL8. ESTIMATE and CIBERSORTx analyses revealed the association between YAP1 expression and immune cell infiltration in gliomas. By identifying the influence of YAP1-FOLR1 link in DNA damage, our findings suggest that simultaneous depletion of both could amplify the potency of DNA damaging agents, while concomitantly reducing the release of inflammatory mediators and potentially affecting immune modulation. This study also highlights the novel role of FOLR1 as a probable prognostic marker in gliomas, predicting responsiveness to temozolomide and other DNA damaging agents.

Constructing metabolism-protein interaction relationship to identify glioma prognosis using deep learning

Glioma is heterogeneous disease that requires classification into subtypes with similar clinical phenotypes, prognosis or treatment responses. Metabolic-protein interaction (MPI) can provide meaningful insights into cancer heterogeneity. Moreover, the potential of lipids and lactate for identifying prognostic subtypes of glioma remains relatively unexplored. Therefore, we proposed a method to construct an MPI relationship matrix (MPIRM) based on a triple-layer network (Tri-MPN) combined with mRNA expression, and processed the MPIRM by deep learning to identify glioma prognostic subtypes. These Subtypes with significant differences in prognosis were detected in glioma (p-value < 2e-16, 95% CI). These subtypes had a strong correlation in immune infiltration, mutational signatures and pathway signatures. This study demonstrated the effectiveness of node interaction from MPI networks in understanding the heterogeneity of glioma prognosis.

Construction and Validation of a Novel Immune Checkpoint-Related Model in Clear Cell Renal Cell Carcinoma

Background

With the highest mortality and metastasis rate, kidney renal clear cell carcinoma (KIRC) is one of the most common urological malignant tumors and not sensitive to chemotherapy and radiotherapy. Immunotherapy, which proves to be effective and a big progression, such as PD-1/PD-L1 inhibitors, is not sensitive to all KIRC patients. To predict prognosis and immunotherapy response, a novel immune checkpoint gene- (ICG-) related model is essential in clinics.

Methods

From the public database-downloaded dataset, a novel ICG-related model for predicting prognosis and immunotherapy response in KIRC patients was built up and verified with R packages and Cox regression analysis. The Kaplan-Meier curve was plotted.

Results

39 ICGs were identified to have different expression in KIRC patients and enriched in immune-related biological pathways and activities. Three ICGs (CTLA4, TNFSF14, and HHLA2) were screened to generate KIRC-ICG model. The KIRC-ICG model was verified to be effective. With conducting KIRC-SYS model, KIRC-ICGscore was verified to be an independent factor regardless of age, gender, stage, grade, and TNM stage. Compared to the ICG-low subgroup, the ICG-high subgroup had more immune activities. KIRC-ICGscore was significantly positively correlated with the expression of Treg markers. KIRC-ICG model could also be reliable to predict immunotherapy response.

Conclusion

The KIRC-ICG model was reliable to predict prognosis and immunotherapy response for KIRC patients and could be an independent factor regardless of clinical characteristics.

Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Background

Advances in our understanding of the tumor microenvironment have radically changed the cancer field, highlighting the emerging need for biomarkers of an active, favorable tumor immune phenotype to aid treatment stratification and clinical prognostication. Numerous immune-related gene signatures have been defined; however, their prognostic value is often limited to one or few cancer types. Moreover, the area of non-coding RNA as biomarkers remains largely unexplored although their number and biological roles are rapidly expanding.

Methods

We developed a multi-step process to identify immune-related long non-coding RNA signatures with prognostic connotation in multiple TCGA solid cancer datasets.

Results

Using the breast cancer dataset as a discovery cohort we found 2988 differentially expressed lncRNAs between immune favorable and unfavorable tumors, as defined by the immunologic constant of rejection (ICR) gene signature. Mapping of the lncRNAs to a coding-non-coding network identified 127 proxy protein-coding genes that are enriched in immune-related diseases and functions. Next, we defined two distinct 20-lncRNA prognostic signatures that show a stronger effect on overall survival than the ICR signature in multiple solid cancers. Furthermore, we found a 3 lncRNA signature that demonstrated prognostic significance across 5 solid cancer types with a stronger association with clinical outcome than ICR. Moreover, this 3 lncRNA signature showed additional prognostic significance in uterine corpus endometrial carcinoma and cervical squamous cell carcinoma and endocervical adenocarcinoma as compared to ICR.

Conclusion

We identified an immune-related 3-lncRNA signature with prognostic connotation in multiple solid cancer types which performed equally well and in some cases better than the 20-gene ICR signature, indicating that it could be used as a minimal informative signature for clinical implementation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03654-7.