Tumour immune microenvironment in primary and metastatic papillary renal cell carcinoma

Comprehensive molecular analyses and experimental validation of CDCAs with potential implications in kidney renal papillary cell carcinoma prognosis

Previous reports have revealed that the abnormal expression of the cell division cycle-associated gene family (CDCAs) is closely associated with some human cancers. However, the precise functional roles and mechanisms of CDCAs in kidney renal papillary cell carcinoma (KIRP) remain unclear. In this study, RNA sequencing data from the Cancer Genome Atlas database and Genotype-Tissue Expression databases were utilized to perform the expression, correlation, survival, mutation, functional enrichment analysis, and immunoinfiltration analyses of CDCAs in KIRP. We found that the expression levels of CDCA genes were significantly increased in KIRP across multiple databases, as confirmed by immunohistochemistry and quantitative reverse transcription PCR (RT-qPCR). Moreover, increased expression of CDCA genes is significantly associated with poor prognosis. Univariate and multivariate Cox regression analyses demonstrated that pathologic T and N staging, NUF2, CDCA2, CDCA3, CDCA5, CBX2, CDCA7, and CDCA8 were independent prognostic factors for patients with KIRP. Utilizing these nine variables, we developed a nomogram prognostic model. Furthermore, the results of GO and KEGG functional enrichment analyses suggested that CDCA genes were associated with nuclear division, mitotic nuclear division, and chromosome segregation and were involved in the cell cycle, p53 signaling pathway, and cellular senescence. We found that the expression of NUF2, CDCA2, CDCA5, and CBX2 was closely associated with the expression of lymphocytes, immunostimulatory molecules, immunoinhibitory molecules, and chemokines. In summary, NUF2, CDCA2, CDCA3, CDCA5, CBX2, CDCA7, and CDCA8 are potential biomarkers for KIRP diagnosis and prognosis.

Potential biomarkers for predicting the overall survival outcome of kidney renal papillary cell carcinoma: an analysis of ferroptosis-related LNCRNAs

Background

Kidney renal papillary cell carcinoma (KIRP) is a dangerous cancer, which accounts for 15–20% of all kidney malignancies. Ferroptosis is a rare kind of cell death that overcomes medication resistance. Ferroptosis-related long non-coding RNAs (LNCRNAs) in KIRP, remain unknown.

Method

We wanted to express how ferroptosis-related LNCRNAs interact with immune cell infiltration in KIRP. Gene set enrichment analysis in the GO and KEGG databases were used to explore gene expression enrichment. The prognostic model was constructed using Lasso regression. In addition, we also analyzed the modifications in the tumor microenvironment (TME) and immunological association.

Result

The expression of LNCRNA was closely connected to the ferroptosis, according to co-expression analyses. CASC19, AC090197.1, AC099850.3, AL033397.2, LINC00462, and B3GALT1-AS1 were found to be significantly increased in the high-risk group, indicating that all of these markers implicates the malignancy processes for KIRP patients and may be cancer-promoting variables. LNCTAM34A and AC024022.1 were shown to be significantly elevated in the low-risk group; these might represent as the KIRP tumor suppressor genes. According to the TCGA, CCR, and inflammation-promoting genes were considered to be significantly different between the low-risk and high-risk groups. The expression of CD160, TNFSF4, CD80, BTLA, and TNFRSF9 was different in the two risk groups.

Conclusion

LNCRNAs associated with ferroptosis were linked to the occurrence and progression of KIRP. Ferroptosis-related LNCRNAs and immune cell infiltration in the TME may be potential biomarkers in KIRP that should be further investigated.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01037-0.

Construction and validation of prognostic nomograms for elderly patients with metastatic non-small cell lung cancer

BACKGROUND

Metastatic non-small cell lung cancer (NSCLC) is mostly seen in older patients and is associated with poor prognosis. There is no reliable method to predict the prognosis of elderly patients (≥60 years old) with metastatic NSCLC. The aim of our study was to develop and validate nomograms which accurately predict survival in this group of patients.

METHODS

NSCLC patients diagnosed between 2010 and 2015 were all identified from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed by significant clinicopathological variables (p < 0.05) selected in multivariate Cox analysis regression.

RESULTS

A total of 9584 patients met the inclusion criteria and were randomly allocated in the training (n = 6712) and validation (n = 2872) cohorts. In training cohort, independent prognostic factors included age, gender, race, grade, tumor site, pathology, T stage, N stage, radiotherapy, surgery, chemotherapy, and metastatic site (p < 0.05) for lung cancer-specific survival (LCSS) and overall survival (OS) were identified by the Cox regression. Nomograms for predicting 1-, 2-, and 3-years LCSS and OS were established and showed excellent predictive performance with a higher C-index than that of the 7th TNM staging system (LCSS: training cohort: 0.712 vs. 0.534; p < 0.001; validation cohort: 0.707 vs. 0.528; p < 0.001; OS: training cohort: 0.713 vs. 0.531; p < 0.001; validation cohort: 0.710 vs. 0.528; p < 0.001). The calibration plots showed good consistency from the predicted to actual survival probabilities both in training cohort and validation cohort. Moreover, the decision curve analysis (DCA) achieved better net clinical benefit compared with TNM staging models.

CONCLUSIONS

We established and validated novel nomograms for predicting LCSS and OS in elderly patients with metastatic NSCLC with desirable discrimination and calibration ability. These nomograms could provide personalized risk assessment for these patients and assist in clinical decision.

Characterizing the tumor microenvironment in rare renal cancer histological types

The tumor microenvironment (TME), including immune cells, cancer-associated fibroblasts, endothelial cells, adjacent normal cells, and others, plays a crucial role in influencing tumor behavior and progression. Here, we characterized the TME in 83 primary renal tumors and matched metastatic or recurrence tissue samples (n = 15) from papillary renal cell carcinoma (pRCC) types 1 (n = 20) and 2 (n = 49), collecting duct carcinomas (CDC; n = 14), and high-grade urothelial carcinomas (HGUC; n = 5). We investigated 10 different markers of immune infiltration, vasculature, cell proliferation, and epithelial-to-mesenchymal transition by using machine learning image analysis in conjunction with immunohistochemistry. Marker expression was compared by Mann-Whitney and Kruskal-Wallis tests and correlations across markers using Spearman's rank correlation coefficient. Multivariable Poisson regression analysis was used to compare marker expression between histological types, while accounting for variation in tissue size. Several immune markers showed different rates of expression across histological types of renal carcinoma. Using pRCC1 as reference, the incidence rate ratio (IRR) of CD3+ T cells (IRR [95% confidence interval, CI] = 2.48 [1.53-4.01]) and CD20+ B cells (IRR [95% CI] = 4.38 [1.22-5.58]) was statistically significantly higher in CDC. In contrast, CD68+ macrophages predominated in pRCC1 (IRR [95% CI] = 2.35 [1.42-3.9]). Spatial analysis revealed CD3+ T-cell and CD20+ B-cell expressions in CDC to be higher at the proximal (p < 0.0001) and distal (p < 0.0001) tumor periphery than within the central tumor core. In contrast, expression of CD68+ macrophages in pRCC2 was higher in the tumor center compared to the proximal (p = 0.0451) tumor periphery and pRCC1 showed a distance-dependent reduction, from the central tumor, in CD68+ macrophages with the lowest expression of CD68 marker at the distal tumor periphery (p = 0.004). This study provides novel insights into the TME of rare kidney cancer types, which are often understudied. Our findings of differences in marker expression and localization by histological subtype could have implications for tumor progression and response to immunotherapies or other targeted therapies.

The Identification of a Tumor Infiltration CD8+ T-Cell Gene Signature That Can Potentially Improve the Prognosis and Prediction of Immunization Responses in Papillary Renal Cell Carcinoma

Background

CD8+ T cells, vital effectors pertaining to adaptive immunity, display close relationships to the immunization responses to kill tumor cells. Understanding the effect exerted by tumor infiltration CD8+ T cells in papillary renal cell carcinoma (papRCC) is critical for assessing the prognosis process and responses to immunization therapy in cases with this disease.

Materials and Approaches

The single-cell transcriptome data of papRCC were used for screening CD8+ T-cell-correlated differentially expressed genes to achieve the following investigations. On that basis, a prognosis gene signature associated with tumor infiltration CD8+ T cell was built and verified with The Cancer Genome Atlas data set. Risk scores were determined for papRCC cases and categorized as high- or low-risk groups. The prognosis significance for risk scores was assessed with multiple-variate Cox investigation and Kaplan–Meier survival curves. In addition, the possible capability exhibited by the genetic profiles of cases to assess the response to immunization therapy was further explored.

Results

Six hundred twenty-one cell death-inhibiting RNA genes were screened using single-cell RNA sequencing. A gene signature consisting of seven genes (LYAR, YBX1, PNRC1, TCF25, MYL12B, MINOS1, and LINC01420) was then identified, and this collective was considered to be an independent prognosis indicator that could strongly assess overall survival in papRCC. In addition, the data allowed papRCC cases to fall to cohorts at high and low risks, exhibiting a wide range of clinically related features as well as different CD8+ T-cell immunization infiltration and immunization therapy responses.

Conclusions

Our work provides a possible explanation for the limited response of current immunization checkpoint-inhibiting elements for combating papRCC. Furthermore, the researchers built a novel genetic signature that was able to assess the prognosis and immunotherapeutic response of cases. This may also be considered as a promising therapeutic target for the disease.

Immune Cell Infiltration and the Expression of PD-1 and PD-L1 in Primary PDGFRA-Mutant Gastrointestinal Stromal Tumors

PURPOSE

To characterize the immune cell profile and expression of PD-1, PD-L1, and IDO in PDGFRA-mutant gastrointestinal stromal tumors (GISTs).

METHODS

The clinicopathological data of PDGFRA-mutant GIST patients who received surgical resection in Zhongshan Hospital between January 2013 and August 2019 were reviewed retrospectively. The specimens of tissue chips were detected for immune cell infiltration and the expression of PD-1, PD-L1, and IDO by immunohistochemical staining.

RESULTS

CD3⁺, CD8⁺, and CD68⁺ cells were the main infiltrating immune cells in the 42 patients included in this study. In addition, CD4⁺, CD56⁺, Foxp3⁺, and CD20⁺ cells were also observed. A higher CD8⁺ T cell count was associated with smaller tumor size and PDGFRA D842V mutation (P = 0.047, P = 0.005). A higher CD3⁺ and CD68⁺ cell count was associated with a higher mitotic index (P = 0.022, P = 0.006). CD4⁺ and CD20⁺ cell count was associated with tumor morphology (P = 0.002, P = 0.045). PD-1 expression was present in 37 (88%) samples. Eighteen samples were positive for PD-L1 expression, and it was higher in small vs. large tumors (P = 0.012) and epithelioid and mixed cell type vs. spindle cell type GISTs (P = 0.046). IDO expression was positive in all 42 patients. The number of CD4⁺ cells was significantly greater in the specimens with high IDO expression (P = 0.012).

CONCLUSION

There were abundant infiltrating immune cells in PDGFRA-mutant GISTs. PD-L1 expression was negatively associated with tumor size. The immunotherapy targeting PD-1/PD-L1 checkpoint and IDO may be valuable.

Identification of 4-genes model in papillary renal cell tumor microenvironment based on comprehensive analysis

BACKGROUND

The tumor microenvironment acts a pivotal part in the occurrence and development of tumor. However, there are few studies on the microenvironment of papillary renal cell carcinoma (PRCC). Our study aims to explore prognostic genes related to tumor microenvironment in PRCC.

METHODS

PRCC expression profiles and clinical data were extracted from The Cancer Gene Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Immune/stromal scores were performed utilizing the ESTIMATE algorithm. Three hundred fifty-seven samples were split into two groups on the basis of median immune/stromal score, and comparison of gene expression was conducted. Intersect genes were obtained by Venn diagrams. Hub genes were selected through protein-protein interaction (PPI) network construction, and relevant functional analysis was conducted by DAVID. We used Kaplan-Meier analysis to identify the correlations between genes and overall survival (OS) and progression-free survival (PFS). Univariate and multivariate cox regression analysis were employed to construct survival model. Cibersort was used to predict the immune cell composition of high and low risk group. Combined nomograms were built to predict PRCC prognosis. Immune properties of PRCC were validated by The Cancer Immunome Atlas (TCIA).

RESULTS

We found immune/stromal score was correlated with T pathological stages and PRCC subtypes. Nine hundred eighty-nine differentially expressed genes (DEGs) and 1169 DEGs were identified respectively on the basis of immune and stromal score. Venn diagrams indicated that 763 co-upregulated genes and 4 co-downregulated genes were identified. Kaplan-Meier analysis revealed that 120 genes were involved in tumor prognosis. Then PPI network analysis identified 22 hub genes, and four of which were significantly related to OS in patients with PRCC confirmed by cox regression analysis. Finally, we constructed a prognostic nomogram which combined with influence factors.

CONCLUSIONS

Four tumor microenvironment-related genes (CD79A, CXCL13, IL6 and CCL19) were identified as biomarkers for PRCC prognosis.

Prognostic role of proliferating CD8+ cytotoxic Tcells in human cancers

PURPOSE

Expansion of CD8⁺ cytotoxic Tlymphocytes is a prerequisite for anti-cancer immune activity and has gained interest in the era of immune checkpoint therapy.

METHODS

To understand the CD8⁺ T cell dynamics in the tumor microenvironment, we used multiplex fluorescence immunohistochemistry to quantitate CD8⁺ proliferation (Ki67 co-expression) in tissue microarrays from 1107 colorectal, 642 renal cell, 1066 breast, 375 ovarian, 451 pancreatic and 347 gastric cancer samples.

RESULTS

The density and the percentage of proliferating (Ki67⁺) CD8⁺ T cells were both highly variable between tumor types as well as between patients with the same tumor type. Elevated density and percentage of proliferating CD8⁺ cytotoxic T cells were significantly associated with favorable tumor parameters such as low tumor stage, negative nodal stage (p ≤ 0.0041 each), prolonged overall survival (p ≤ 0.0028 each) and an inflamed immune phenotype (p = 0.0025) in colorectal cancer and, in contrast, linked to high tumor stage, advanced ISUP/Fuhrman/Thoenes grading (each p ≤ 0.003), shorter overall survival (p ≤ 0.0330 each) and an immune inflamed phenotype (p = 0.0094) in renal cell cancer. In breast, ovarian, pancreatic and gastric cancer the role of (Ki67⁺)CD8⁺ Tcells was not linked to clinicopathological data.

CONCLUSION

Our data demonstrate a tumor type dependent prognostic impact of proliferating (Ki67⁺)CD8⁺ Tcells and an inverse impact in colorectal and renal cell cancer.

Tumor cell PD-L1 expression is a strong predictor of unfavorable prognosis in immune checkpoint therapy-naive clear cell renal cell cancer

BACKGROUND

PD-L1 expression predicts response to immune checkpoint inhibitors in renal cell carcinomas (RCC), but has also been suggested to be linked to poor patient outcome.

METHODS

We analyzed PD-L1 in > 1400 RCC in a tissue microarray format by immunohistochemistry. Results were compared with histological tumor type, parameters of cancer aggressiveness, and intratumoral CD8⁺ cytotoxic cells.

RESULT

At a cut-off level of 5% PD-L1 positive tumor cells, PD-L1 positivity was seen in 6.3% of 633 clear cell RCC (ccRCC), 18.2% of 165 papillary RCC, 18.8% of 64 chromophobe RCC, and 41.7% of 103 oncocytomas. In ccRCC, PD-L1 positivity was significantly linked to high ISUP (p < 0.0001), Fuhrman (p < 0.0001), Thoenes grade (p < 0.0001), distant metastasis (p = 0.0042), short recurrence-free (p < 0.0001), and overall survival (p = 0.0002). Intratumoral CD8⁺ lymphocytes were more frequent in PD-L1 positive (1055 ± 109) than in PD-L1 negative ccRCC (407 ± 28; p < 0.0001). PD-L positive immune cells were seen in 8.2% of all RCC and 13.9% of papillary RCC. In ccRCC, PD-L1 positive immune cells were linked to high numbers of tumor-infiltrating CD8⁺ cells (p < 0.0001), high ISUP (p < 0.0001), Fuhrman (p = 0.0027), and Thoenes grade (p < 0.0001), and poor tumor-specific survival (p = 0.0280).

CONCLUSIONS

These data suggest that PD-L1 expression in highly immunogenic RCCs facilitates immune evasion and contributes to cancer aggressiveness.

An immune-related lncRNA prognostic model in papillary renal cell carcinoma: A lncRNA expression analysis

OBJECTIVE

To screen several immune-related long non-coding RNAs (lncRNAs) and construct a prognostic model for papillary renal cell carcinoma (pRCC).

METHODS

Transcriptome-sequencing data of pRCC was downloaded and a prognostic model was constructed. Time-dependent receiver operating characteristic (ROC) curve was plotted and the area under curve (AUC) was calculated. We conducted quantitative reverse transcription polymerase chain reaction (RT-PCR) to verify the model. The gene set enrichment analysis (GSEA) was used to show the connection of our model with immune pathways.

RESULT

We identified four lncRNAs to constructed the model. The model was significantly associated with the survival time and survival state. The expression-levels of the four lncRNAs were measured and the prognosis of high-risk patients was significantly worse. The two immune-gene sets had an active performance in the high-risk patients.

CONCLUSION

We constructed a prognostic model in pRCC which provided more reference for treatment.

Identification of a three-miRNA signature as a novel prognostic model for papillary renal cell carcinoma

Background

Papillary renal cell carcinoma (pRCC) accounting for near 20% of renal cell carcinoma is the second most common histological subtype. MiRNAs have been demonstrated to played significant roles on predicting prognosis of patients with tumors. An appropriate and comprehensive miRNAs analysis based on a great deal of pRCC samples from The Cancer Genome Atlas (TCGA) will provide perspective in this field.

Methods

We integrated the expression of mRNAs, miRNAs and the relevant clinical data of 321 pRCC patients recorded in the TCGA database. The survival-related differential expressed miRNAs (sDEmiRs) were estimated by COX regression analysis. The high-risk group and the low-risk group were separated by the median risk score of the risk score model (RSM) based on three screened sDEmiRs. The target genes, underlying molecular mechanisms of these sDEmiRs were explored by computational biology. The expression levels of the three sDEmiRs and their correlations with clinicopathological parameters were further validated by qPCR.

Results

Based on univariate COX analysis (P < 0.001), eighteen differential expressed miRNAs (DEmiRs) were remarkably related with the overall survival (OS) of pRCC patients. Three sDEmiRs with the most significant prognostic values (miR-34a-5p, miR-410-3p and miR-6720-3p) were employed to establish the RSM which was certified as an independent prognosis factor and closely correlated with OS. In the verification of clinical samples, the overexpression of miR-410-3p and miR-6720-3p were detected to be associated with the advanced T-stages, while miR-34a-5p showed the reversed results.

Conclusion

The study developed a RSM based on the identified sDEmiRs with significant prognosis prediction values for pRCC patients. The results pave the avenue for establishing and optimizing a reliable and referable risk assessing model and provide novel insight into the researches of biomarkers and clinical treatment strategies.