Background: Tongue squamous cell carcinoma (TSCC) is characterized by aggressive invasion and poor prognosis. Currently, immune checkpoint inhibitors may prolong overall survival compared with conventional treatments. However, PD1/PDL1 remain inapplicable in predicting the prognosis of TSCC; thus, it is urgent to explore the genetic characteristics of TSCC.Materials and methods: We utilized single‐sample gene set enrichment analysis (ssGSEA) to classify TSCC patients from the TCGA database into clusters with different immune cell infiltrations. ESTIMATE (immune‐related scores) and CIBERSORT (immune cell distribution) analyses were used to evaluate the immune landscape among clusters. GO, KEGG, and GSEA analyses were performed to analyze the different underlying molecular mechanisms in the clusters. Based on the immune characteristics, we applied the LASSO Cox regression to select hub genes and construct a prognostic risk model. Finally, we established an interactive network among these hub genes by using Cytoscape, and a pan‐cancer analysis to further verify and decipher the innate function of these genes.Results: Using ssGSEA, we constructed three functional clusters with different overall survival and immune‐cell infiltration. ESTIMATE and CIBERSORT analyses revealed the different distributions of immune cells (T cells, B cells, and macrophages) with diverse immune‐related scores (ESTIMATE, immune, stromal, and tumor purity scores). Moreover, pathways including those of the interferon‐gamma response, hypoxia, and glycolysis of the different subtypes were investigated to elucidate their involvement in mediating the heterogeneous immune characteristics. Subsequently, after LASSO Cox regression, a signature of 15 immune‐related genes was established that is more prognostically effective than the TNM stage. Furthermore, three hub genes—PGK1, GPI, and RPE—were selected using Cytoscape evaluation and verified by immunohistochemistry. PGK1, the foremost regulator, was a comprehensively profiled pan‐cancer, and a PGK1‐based interactive network was established.Conclusion: Our results suggest that immune‐related genes and clusters in TSCC have the potential to guide individualized treatments.

Chronic infections and cancer cause T cell dysfunction known as exhaustion. This cell state is caused by persistent antigen exposure, suboptimal co-stimulation and a plethora of hostile factors that dampen protective immunity and limit the efficacy of immunotherapies^1,2,3,4. The mechanisms that underlie T cell exhaustion remain poorly understood. Here we analyse the proteome of CD8⁺ exhausted T (T_ex) cells across multiple states of exhaustion in the context of both chronic viral infections and cancer. We show that there is a non-stochastic pathway-specific discordance between mRNA and protein dynamics between T effector (T_eff) and T_ex cells. We identify a distinct proteotoxic stress response (PSR) in T_ex cells, which we term T_ex-PSR. Contrary to canonical stress responses that induce a reduction in protein synthesis^5,6, T_ex-PSR involves an increase in global translation activity and an upregulation of specialized chaperone proteins. T_ex-PSR is further characterized by the accumulation of protein aggregates and stress granules and an increase in autophagy-dominant protein catabolism. We establish that disruption of proteostasis alone can convert T_eff cells to T_ex cells, and we link T_ex-PSR mechanistically to persistent AKT signalling. Finally, disruption of T_ex-PSR-associated chaperones in CD8⁺ T cells improves cancer immunotherapy in preclinical models. Moreover, a high T_ex-PSR in T cells from patients with cancer confers poor responses to clinical immunotherapy. Collectively, our findings indicate that T_ex-PSR is a hallmark and a mechanistic driver of T cell exhaustion, which raises the possibility of targeting proteostasis pathways as an approach for cancer immunotherapy.

Loss of the Y chromosome (LOY) in peripheral blood mononuclear cells (PBMCs) is the most common somatic alteration in men and is associated with higher mortality from epithelial cancers^1,2,3. In tumours, epithelial LOY is also associated with poor survival^4,5,6,7. This raises several fundamental questions, such as why LOY in PBMCs drives cancer mortality and whether there is a relationship between LOY in PBMCs, PBMC-derived immune cells and cancer cells (and, if so, what its consequences are). We sought to answer these questions through a comprehensive pan-cancer analysis of bulk and single-cell RNA sequencing data from 29 human tumour types, along with autochthonous and syngeneic mouse models. In human and mouse tumours, malignant epithelial cells had the highest LOY prevalence, yet LOY was also present in tumour stromal and immune cells, with LOY in malignant epithelial cells predicting LOY in benign cells. LOY also correlated between paired tumour and PBMC samples from patients. Among benign cells, LOY induced the strongest shift in CD4⁺ and CD8⁺ T cells, with both showing transcriptomic signatures of immunosuppression. Furthermore, the magnitude of LOY in epithelial cells, CD4⁺ T cells and CD8⁺ T cells independently predicts survival, with tumours exhibiting concurrent epithelial and T cell LOY having the worst outcomes. Here we establish a model that links LOY in immune cells to LOY in malignant cells, which may explain in part why LOY in PBMCs is associated with increased cancer mortality.

Accurate staging of bladder cancer assists in identifying optimal treatment (e.g., transurethral resection vs. radical cystectomy vs. bladder preservation). However, currently, about one-third of patients are over-staged and one-third are under-staged. There is a pressing need for a more accurate staging modality to evaluate patients with bladder cancer to assist clinical decision-making. We hypothesize that MRI/RNA-seq-based radiogenomics and artificial intelligence can more accurately stage bladder cancer. A total of 40 magnetic resonance imaging (MRI) and matched formalin-fixed paraffin-embedded (FFPE) tissues were available for analysis. Twenty-eight (28) MRI and their matched FFPE tissues were available for training analysis, and 12 matched MRI and FFPE tissues were used for validation. FFPE samples were subjected to bulk RNA-seq, followed by bioinformatics analysis. In the radiomics, several hundred image-based features from bladder tumors in MRI were extracted and analyzed. Overall, the model obtained mean sensitivity, specificity, and accuracy of 94%, 88%, and 92%, respectively, in differentiating intra- vs. extra-bladder cancer. The proposed model demonstrated improvement in the three matrices by 17%, 33%, and 25% and 17%, 16%, and 17% as compared to the genetic- and radiomic-based models alone, respectively. The radiogenomics of bladder cancer provides insight into discriminative features capable of more accurately staging bladder cancer. Additional studies are underway.

Progenitor CD8 ⁺ T cells differentiate into effector and exhausted progenies during chronic antigen stimulation; however, mechanisms that restrain exhaustion and sustain effector differentiation remain incompletely defined. Here we identified the transcription factor ZFP148 as a repressor of CD8 ⁺ T cell effector differentiation. ZFP148-deficient CD8 ⁺ T cells displayed increased frequency of cytolytic effector cells and reduced frequency of exhausted cells compared with Zfp148 ^fl/fl controls during chronic viral infection. Mechanistically, ZFP148 limited the chromatin accessibility of effector-driving transcription factor motifs and directly repressed expression of the transcription factor KLF2. Furthermore, conditional ZFP148 ablation in CD8 ⁺ T cells synergized with programmed cell death-1 blockade to improve tumor control in syngeneic mouse models. Consistently, cancer patients with lower ZNF148 expression in tumor-infiltrating CD8 ⁺ T cells showed improved responsiveness to immunotherapies. Collectively, our study identifies ZFP148 as a transcriptional repressor of CD8 ⁺ T cell effector differentiation and highlights its therapeutic potential for enhancing antitumor immunity.