Construction of an immune infiltration landscape based on immune-related genes in cervical cancer

Comprehensive analysis of PHF5A as a potential prognostic biomarker and therapeutic target across cancers and in hepatocellular carcinoma

OBJECTIVE

Cancer is a predominant cause of death globally. PHD-finger domain protein 5 A (PHF5A) has been reported to participate in various cancers; however, there has been no pan-cancer analysis of PHF5A. This study aims to present a novel prognostic biomarker and therapeutic target for cancer treatment.

METHODS

This study explored PHF5A expression and its impact on prognosis, tumor mutation burden (TMB), microsatellite instability (MSI), functional status and tumor immunity across cancers using various public databases, and validated PHF5A expression and its correlation with survival, immune evasion, angiogenesis, and treatment response in hepatocellular carcinoma (HCC) using bioinformatics tools, qRT-PCR and immunohistochemistry (IHC).

RESULTS

PHF5A was differentially expressed between tumor and corresponding normal tissues and was correlated with prognosis in diverse cancers. Its expression was also associated with TMB, MSI, functional status, tumor microenvironment, immune infiltration, immune checkpoint genes and tumor immune dysfunction and exclusion (TIDE) score in diverse malignancies. In HCC, PHF5A was confirmed to be upregulated by qRT-PCR and IHC, and elevated PHF5A expression may promote immune evasion and angiogenesis in HCC. Additionally, multiple canonical pathways were revealed to be involved in the biological activity of PHF5A in HCC. Moreover, immunotherapy and transcatheter arterial chemoembolization (TACE) worked better in the low PHF5A expression group, while sorafenib, chemotherapy and AKT inhibitor were more effective in the high expression group.

CONCLUSIONS

This study provides a comprehensive understanding of the biological function of PHF5A in the carcinogenesis and progression of various cancers. PHF5A could serve as a tumor biomarker related to prognosis across cancers, especially HCC, and shed new light on the development of novel therapeutic targets.

Sequencing-based transcriptome analysis reveals diversification of immune response- and angiogenesis-related expression patterns of early-stage cervical carcinoma as compared with high-grade CIN

Background

Molecular diversity of virus-associated cervical cancer remains a relatively underexplored issue, and interrelations of immunologic and angiogenic features during the establishment of a particular landscape of the cervical cancer microenvironment are not well-characterized, especially for its earliest clinical stages, although this may provide insight into the mechanisms behind the differences in tumor aggressiveness, treatment responsiveness and prognosis. In this research, we were aimed at identifying transcriptomic landscapes of early-stage cervical carcinoma that differ substantially in their immune-related characteristics, patterns of signaling pathways and composition of the microenvironment in comparison with immediate precursor (intraepithelial) lesions.

Methods

We performed the Illumina platform-based RNA sequencing using a panel of fresh tissue samples that included human papillomavirus-positive cervical intraepithelial neoplastic lesions (CIN), invasive squamous carcinoma of the cervix of FIGO IA1-IIB stages, and morphologically normal epithelium. The derived transcriptomic profiles were bioinformatically analyzed and compared by patterns of signaling pathway activation, distribution of tumor-infiltrating cell populations, and genomic regions involved.

Result

According to hierarchical cluster analysis of the whole-transcriptome profiles, tissue samples were distributed between three groups, or gene expression patterns (the one comprising most pre-cancer cases and the other two encompassing mostly early-stage invasive cancer cases). Differentially expressed genes were retrieved in each intergroup pairwise comparison followed by Gene Ontology analysis. Gene set enrichment analysis of the two groups of tumor samples in comparison with the CIN group identified substantial differences in immunological and angiogenic properties between tumorous groups suggesting the development of different molecular phenotypes. Cell composition analysis confirmed the diverse changes in the abundancies of immune and non-immune populations and, accordingly, different impacts of the immune and stromal compartments on the tumor microenvironment in these two groups of tumors compared to CIN. Positional gene expression analysis demonstrated that the identified transcriptomic differences were linked to different chromosomal regions and co-localized with particular gene families implicated in immune regulation, inflammation, cell differentiation, and tumor invasion.

Conclusions

Overall, detection of different transcriptomic patterns of invasive cervical carcinoma at its earliest stages supports the diverse impacts of immune response- and angiogenesis-related mechanisms on the onset of tumor invasion and progression. This may provide new options for broadening the applicability and increasing the efficiency of target anti-angiogenic and immune-based therapy of virus-associated cervical carcinoma.

Function of CD8+, conventional CD4+, and regulatory CD4+ T cell identification in lung cancer

Lung cancer is the malignant tumor with the highest mortality rate in the world. There is obvious heterogeneity within the tumor. Single cell sequencing technology enables scholars to obtain information about the cell type, status, subpopulation distribution and communication behavior between cells in the tumor microenvironment from the cellular level. However, due to the problem of sequencing depth, some genes with low expression cannot be detected, which results in that most of the specific genes of immune cells cannot be recognized, and lead to defects in the functional identification of immune cells. In this paper, we used single cell sequencing data of 12346 T cells in 14 treatment-naïve non-small-cell lung cancer patients to identify immune cell-specific genes and infer the function of three types of T cells. The method, named GRAPH-LC, implemented this function by gene interaction network and graph learning methods. Graph learning methods are used to extract genes feature and dense neural network is used to identify immune cell-specific genes. The experiments on 10-cross validation shows that the AUROC and AUPR reached at least 0.802, 0.815 on identifying cell-specific genes of three types of T cells. And we did functional enrichment analysis on the top 15 expressed genes. By functional enrichment analysis, we got 95 GO terms and 39 KEGG pathways that related to three types of T cells. The use of this technology will help to deeply understand the mechanism of the occurrence and development of lung cancer, find new diagnostic markers and therapeutic targets, and provide a theoretical reference for the precise treatment of lung cancer patients in the future.

Prognostic Model for Clear-cell Renal Cell Carcinoma Based on Natural Killer Cell-related Genes

BACKGROUND

Natural killer (NK) cells are a key factor affecting progression and immune surveillance of clear-cell renal cell carcinoma (ccRCC). This study sought to construct a natural killer cell-related prognostic signature (NKRPS) to predict the outcome of ccRCC patients and to furnish guidance for finding appropriate treatment strategies.

METHODS

From the TCGA and ArrayExpress databases, transcriptomic profiles and relevant clinical information of ccRCC patients were downloaded for the TCGA cohort (n = 515) and the E-MTAB-1980 cohort (n = 101). With the univariate Cox analysis and LASSO-Cox regression algorithm, a NKRPS was built to evaluate patients' prognosis. Receiver operating characteristic (ROC) curves and calibration curves were drawn to estimate the predictive power of the prognostic model. Then, tumor microenvironment (TME), tumor mutational burden (TMB), sensitization to immune checkpoint inhibitors (ICIs) therapy and targeted drug treatment were analyzed in ccRCC patients.

RESULTS

Nine genes (BID, CCL7, CSF2, IL23A, KNSTRN, RHBDD3, PIK3R3, RNF19B and VAV3) were identified to construct a NKRPS. High-risk group displayed undesirable survival compared to low-risk group (P < .05). Moreover, the area under the curve (AUC) of ROC at 1-, 3- and 5-year were 0.766, 0.755, and 0.757, respectively. High-risk group was characterized by superior immune infiltration, higher TMB, and higher expression of 5 ICI-related genes. Additionally, this model enabled to predict the sensitivity of patients to chemotherapy drugs.

CONCLUSION

NKRPS had a strong predictive power on prognosis of ccRCC patients, which may facilitate individualized treatment and medical decision making.

A novel cervix carcinoma biomarker: Pathological-epigenomics, integrated analysis of MethylMix algorithm and pathology for predicting response to cancer immunotherapy

Herein, A non-invasive pathomics approach was developed to reveal the methylation status in patients with cervical squamous cell carcinoma and predict clinical outcomes and treatment response. Using the MethylMix algorithm, 14 methylation-driven genes were selected for further analysis. We confirmed that methylation-driven genes were differentially expressed in immune, stromal, and tumor cells. In addition, we constructed a methylation-driven model and explored the alterations in immunocyte infiltration between the different models. The methylation-driven subtypes identified in our investigation could effectively predict the clinical outcomes of cervical cancer. To further evaluate the level of methylation-driven patterns, we constructed a risk model with four genes. Significant correlations were observed between the score and immune response markers, including PD1 and CTLA4. Multiple immune infiltration algorithms evaluated the level of immunocyte infiltration between the high- and low-risk groups, while the components of anti-tumor immunocytes in the low-risk group were significantly increased. Subsequently, a total of 205 acquired whole-slide imaging (WSI) images were processed to capture image signatures, and the pathological algorithm was employed to construct an image prediction model based on the risk score classification. The model achieved an area under the curve (AUC) of 0.737 and 0.582 for the training and test datasets, respectively. Moreover, we conducted vitro assays for validation of hub risk gene. The proposed prediction model is a non-invasive method that combines pathomics features and genomic profiles and shows satisfactory performance in predicting patient survival and treatment response. More interdisciplinary fields combining medicine and electronics should be explored in the future.

Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures

Background

Immunotherapy has changed the therapeutic landscape of cervical cancer (CC), but has durable anti-tumor activity only in a subset of patients. This study aims to comprehensively analyze the tumor immune microenvironment (TIME) of CC and to mine biomarkers related to immunotherapy and prognosis.

Methods

The Cancer Genome Atlas (TCGA) data was utilized to identify heterogeneous immune subtypes based on survival-related immune cell signatures (ICSs). ICSs prognostic model was constructed by Cox regression analyses, and immunohistochemistry was conducted to verify the gene with the largest weight coefficient in the model. Meanwhile, the tumor immune infiltration landscape was comprehensively characterized by ESTIMATE, CIBERSORT and MCPcounter algorithms. In addition, we also analyzed the differences in immunotherapy-related biomarkers between high and low-risk groups. IMvigor210 and two gynecologic tumor cohorts were used to validate the reliability and scalability of the Risk score.

Results

A total of 291 TCGA-CC samples were divided into two ICSs clusters with significant differences in immune infiltration landscape and prognosis. ICSs prognostic model was constructed based on eight immune-related genes (IRGs), which showed higher overall survival (OS) rate in the low-risk group (P< 0.001). In the total population, time-dependent receiver operating characteristic (ROC) curves displayed area under the curve (AUC) of 0.870, 0.785 and 0.774 at 1-, 3- and 5-years. Immunohistochemical results showed that the expression of the oncogene (FKBP10) was negatively correlated with the degree of differentiation and positively correlated with tumor stage, while the expression of tumor suppressor genes (S1PR4) was the opposite. In addition, the low-risk group had more favorable immune activation phenotype and higher enrichment of immunotherapy-related biomarkers. The Imvigor210 and two gynecologic tumor cohorts validated a better survival advantage and immune efficacy in the low-risk group.

Conclusion

This study comprehensively assessed the TIME of CC and constructed an ICSs prognostic model, which provides an effective tool for predicting patient’s prognosis and accurate immunotherapy.