A novel T-cell proliferation-associated regulator signature pre-operatively predicted the prognostic of bladder cancer

Lipid metabolism subtypes reflects the prognosis and immune profiles of bladder cancer patients by NMF clustering and building related signature

BACKGROUND

Lipid metabolism is crucial in tumor formation and progression. However, the role of lipid metabolism genes (LMGs) in bladder cancer (BLCA) are unknown. The purpose of this study was to construct a LMGs-related subtypes that predicted the treatment and prognosis of BLCA patients.

METHODS

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used for this study. The gene set enrichment analysis (GSEA) was utilized to distinguish functional differences between high-risk (HR) and low-risk (LR) groups. Single-sample GSEA (ssGSEA) was employed to determine potential associations between prognostic outcomes and immune status.

RESULTS

First, BLCA patients were divided into two subtypes by non-negative matrix factorization (NMF) clustering, and there were substantial variations in survival status, immune cell infiltration and immune classification between the two subtypes. Next, a prognostic signature involving 8 LMGs was identified (AKR1B1, SCD, CYP27B1, UGCG, SGPL1, FASN, TNFAIP8L3, PLA2G2A). HR patients exhibited worse outcome than LR patients. Multivariate Cox regression analysis confirmed that LMGs-related signature was an independent prognostic indicator of BLCA patients' survival. Compared with clinicopathological variables, LMGs-related signature showed higher prognostic predictive ability, with an area under curve of 0.720 at 5 years of follow-up. Through immunotherapy analysis, drug sensitivity analysis, TIDE score and immune cell infiltration characteristics, LMGs-related signature was confirmed to accurately predict the prognosis and treatment response of BLCA patients.

CONCLUSION

Our newly established prognostic signature, which involved eight LMGs, can give prognostic distinction for BLCA and may eventually lead to novel targets for treatment.

Discovery of a T cell proliferation-associated regulator signature correlates with prognosis risk and immunotherapy response in bladder cancer

BACKGROUND

The efficacy of immunotherapy is heavily influenced by T cell activity. This study aimed to examine how T cell proliferation regulators can predict the prognosis and response to immunotherapy in patients with bladder cancer (BCa).

METHODS

T cell proliferation-related subtypes were determined by employing the non-negative matrix factorization (NMF) algorithm that analyzed the expression patterns of T cell proliferation regulators. Subtypes were assessed for variations in prognosis, immune infiltration, and functional behaviors. Subsequently, a risk model related to T cell proliferation was created through Cox and Lasso regression analyses in the TCGA cohort and then confirmed in two GEO cohorts and an immunotherapy cohort.

RESULTS

BCa patients were categorized into two subtypes (C1 and C2) according to the expression profiles of 31 T cell proliferation-related genes (TRGs) with distinct prognoses and immune landscapes. The C2 subtype had a shorter overall survival (OS), with higher levels of M2 macrophage infiltration, and the activation of cancer-related pathways than the C1 subtype. Following this, thirteen prognosis-related genes that were involved in T cell proliferation were utilized to create the prognostic signature. The model's predictive accuracy was confirmed by analyzing both internal and external datasets. Individuals in the high-risk category experienced a poorer prognosis, increased immunosuppressive factors in the tumor microenvironment, and diminished responses to immunotherapy. Additionally, the immunotherapeutic prediction efficacy of the model was further confirmed by an immunotherapy cohort (anti-PD-L1 in the IMvigor210 cohort).

CONCLUSIONS

Our study characterized two subtypes linked to T cell proliferation in BCa patients with distinct prognoses and tumor microenvironment (TME) patterns, providing new insights into the heterogeneity of T cell proliferation in BCa and its connection to the immune landscape. The signature has prospective clinical implications for predicting outcomes and may help physicians to select prospective responders who prioritize current immunotherapy.

Prognostic modeling of hepatocellular carcinoma based on T-cell proliferation regulators: a bioinformatics approach

Background

The prognostic value and immune significance of T-cell proliferation regulators (TCRs) in hepatocellular carcinoma (HCC) have not been previously reported. This study aimed to develop a new prognostic model based on TCRs in patients with HCC.

Method

This study used The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and International Cancer Genome Consortium-Liver Cancer-Riken, Japan (ICGC-LIRI-JP) datasets along with TCRs. Differentially expressed TCRs (DE-TCRs) were identified by intersecting TCRs and differentially expressed genes between HCC and non-cancerous samples. Prognostic genes were determined using Cox regression analysis and were used to construct a risk model for HCC. Kaplan-Meier survival analysis was performed to assess the difference in survival between high-risk and low-risk groups. Receiver operating characteristic curve was used to assess the validity of risk model, as well as for testing in the ICGC-LIRI-JP dataset. Additionally, independent prognostic factors were identified using multivariate Cox regression analysis and proportional hazards assumption, and they were used to construct a nomogram model. TCGA-LIHC dataset was subjected to tumor microenvironment analysis, drug sensitivity analysis, gene set variation analysis, and immune correlation analysis. The prognostic genes were analyzed using consensus clustering analysis, mutation analysis, copy number variation analysis, gene set enrichment analysis, and molecular prediction analysis.

Results

Among the 18 DE-TCRs, six genes (DCLRE1B, RAN, HOMER1, ADA, CDK1, and IL1RN) could predict the prognosis of HCC. A risk model that can accurately predict HCC prognosis was established based on these genes. An efficient nomogram model was also developed using clinical traits and risk scores. Immune-related analyses revealed that 39 immune checkpoints exhibited differential expression between the high-risk and low-risk groups. The rate of immunotherapy response was low in patients belonging to the high-risk group. Patients with HCC were further divided into cluster 1 and cluster 2 based on prognostic genes. Mutation analysis revealed that HOMER1 and CDK1 harbored missense mutations. DCLRE1B exhibited an increased copy number, whereas RAN exhibited a decreased copy number. The prognostic genes were significantly enriched in tryptophan metabolism pathways.

Conclusions

This bioinformatics analysis identified six TCR genes associated with HCC prognosis that can serve as diagnostic markers and therapeutic targets for HCC.

Unveiling clinical significance and tumor immune landscape of CXCL12 in bladder cancer: Insights from multiple omics analysis

Objective

The interplay between chemokine C-X-C motif ligand 12 (CXCL12) and its specific receptors is known to trigger various signaling pathways, contributing to tumor proliferation and metastasis. Consequently, targeting this signaling axis has emerged as a potential strategy in cancer therapy. However, the precise role of CXCL12 in clinical therapy, especially in immunotherapy for bladder cancer (BCa), remains poorly elucidated.

Methods

We gathered multiple omics data from public databases to unveil the clinical relevance and tumor immune landscape associated with CXCL12 in BCa patients. Univariate and multivariate Cox regression analyses were employed to assess the independent prognostic significance of CXCL12 expression and formulate a nomogram. The expression of CXCL12 in BCa cell lines and clinical tissue samples was validated using enzyme-linked immunosorbent assays (ELISA) and immunohistochemistry (IHC).

Results

While transcriptional expression of CXCL12 exhibited a decrease in nearly all tumor tissues, CXCL12 methylation expression was notably increased in BCa tissues. Single-cell RNA analysis highlighted tissue stem cells and endothelial cells as the primary sources expressing CXCL12. Abnormal CXCL12 expression, based on transcriptional and methylation levels, correlated with various clinical characteristics in BCa patients. Functional analysis indicated enrichment of CXCL12 and its co-expression genes in immune regulation and cell adhesion. The immune landscape analysis unveiled a significant association between CXCL12 expression and M2 macrophages (CD163+ cells) in BCa tissues. Notably, CXCL12 expression emerged as a potential predictor of immunotherapy response and chemotherapy drug sensitivity in BCa patients.

Conclusions

Taken together, these findings suggest aberrant production of CXCL12 in BCa tissues, potentially influencing the treatment responses of affected individuals.

Bioinformatics identification of a T-cell-related signature for predicting prognosis and drug sensitivity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a fatal disease with poor clinical outcomes. T cells play a vital role in the crosstalk between the tumour microenvironment and HCC. Single-cell RNA sequencing data were downloaded from the GSE149614 dataset. The T-cell-related prognostic signature (TRPS) was developed with the integrative procedure including 10 machine learning algorithms. The TRPS was established using 7 T-cell-related markers in the Cancer Genome Atlas cohort with 1-, 2- and 3-year area under curve values of 0.820, 0.725 and 0.678, respectively. TRPS acted as an independent risk factor for HCC patients. HCC patients with a high TRPS-based risk score had a higher Tumour Immune Dysfunction and Exclusion score, lower PD1 and CTLA4 immunophenoscore and lower level of immunoactivated cells, including CD8+ T cells and NK cells. The response rate was significantly higher in patients with low-risk scores in immunotherapy cohorts, including IMigor210 and GSE91061. The TRPS-based nomogram had a relatively good predictive value in evaluating the mortality risk at 1, 3 and 5 years in HCC. Overall, this study develops a TRPS by integrated bioinformatics analysis. This TRPS acted as an independent risk factor for the OS rate of HCC patients. It can screen for HCC patients who might benefit from immunotherapy, chemotherapy and targeted therapy.

Review of T cell proliferation regulatory factors in treatment and prognostic prediction for solid tumors

T cell proliferation regulators (Tcprs), which are positive regulators that promote T cell function, have made great contributions to the development of therapies to improve T cell function. CAR (chimeric antigen receptor) -T cell therapy, a type of adoptive cell transfer therapy that targets tumor cells and enhances immune lethality, has led to significant progress in the treatment of hematologic tumors. However, the applications of CAR-T in solid tumor treatment remain limited. Therefore, in this review, we focus on the development of Tcprs for solid tumor therapy and prognostic prediction. We summarize potential strategies for targeting different Tcprs to enhance T cell proliferation and activation and inhibition of cancer progression, thereby improving the antitumor activity and persistence of CAR-T. In summary, we propose means of enhancing CAR-T cells by expressing different Tcprs, which may lead to the development of a new generation of cell therapies.

A novel T-cell proliferation-associated gene predicts prognosis and reveals immune infiltration in patients with oral squamous cell carcinoma

OBJECTIVE

Oral squamous cell carcinoma (OSCC) is a highly malignant tumour, and the prediction of its prognosis remains challenging. The prognostic value of T-lymphocyte proliferation regulators in OSCC remains to be explored.

DESIGN

We integrated mRNA expression profiles and relevant clinical information of OSCC patients from The Cancer Genome Atlas database. The expression and function of T-lymphocyte proliferation regulators and their relationship with overall survival (OS) were analysed. The T-lymphocyte proliferation regulator signature was screened using univariate Cox regression and least absolute shrinkage and selection operator coefficients and used to construct models for prognosis and staging prediction as well as for immune infiltration analysis. Final validation was performed using single-cell sequencing database and immunohistochemical staining.

RESULTS

Most T-lymphocyte proliferation regulators in the TCGA cohort exhibited different expression levels between OSCC and paracancerous tissues. A prognostic model constructed using the T-lymphocyte proliferation regulator signature (RAN, CDK1, and CDK2) was used to categorise patients into high- and low-risk groups. The OS was significantly lower in the high-risk group than the low-risk group (p < 0.01). The predictive ability of the T-lymphocyte proliferation regulator signature was validated by receiver operating characteristic curve analysis. Immune infiltration analysis revealed different immune statuses in both groups.

CONCLUSIONS

We established a new T-lymphocyte proliferation regulator signature that can predict the prognosis of OSCC. The results of this study will contribute to studies of T-cell proliferation and the immune microenvironment in OSCC to improve prognosis and immunotherapeutic response.

Subtype classification based on t cell proliferation-related regulator genes and risk model for predicting outcomes of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD), the major lung cancer histotype, represents 40% lung cancers. Currently, outcomes are remarkably different in LUAD patients with similar AJCC/UICC-TNM features. T cell proliferation-related regulator genes (TPRGs) relate to the proliferation, activity and function of T cells and tumor progression. The values of TPRGs in classifying LUAD patients and predicting outcomes remain unknown.

Methods

Gene expression profile and corresponding clinical data were downloaded from TCGA and the GEO databases. We systematically analyzed the expression profile characteristics of 35 TPRGs in LUAD patients and investigated the differences in overall survival (OS), biology pathway, immunity and somatic mutation between different TPRGs-related subtypes. Subsequently, we constructed a TPRGs-related risk model in TCGA cohort to quantify risk scores using LASSO cox regression analysis and then validated this risk model in two GEO cohorts. LUAD patients were divided into high- and low-risk subtypes according to the median risk score. We systematically compared the biology pathway, immunity, somatic mutation and drug susceptibility between the two risk subtypes. Finally, we validate biological functions of two TPRGs-encoded proteins (DCLRE1B and HOMER1) in LUAD cells A549.

Results

We identified different TPRGs-related subtypes (including cluster 1/cluster A and its counterpart cluster 2/cluster B). Compared to the cluster 1/cluster A subtype, cluster 2/cluster B subtype tended to have a prominent survival advantage with an immunosuppressive microenvironment and a higher somatic mutation frequency. Then, we constructed a TPRGs-related 6-gene risk model. The high-risk subtype characterized by higher somatic mutation frequency and lower immunotherapy response had a worse prognosis. This risk model was an independent prognostic factor and showed to be reliable and accurate for LUAD classification. Furthermore, subtypes with different risk scores were significantly associated with drug sensitivity. DCLRE1B and HOMER1 suppressed cell proliferation, migration and invasion in LUAD cells A549, which was in line with their prognostic values.

Conclusion

We construed a novel stratification model of LUAD based on TPRGs, which can accurately and reliably predict the prognosis and might be used as a predictive tool for LUAD patients.