Portraying the dark side of endogenous IFN-λ for promoting cancer progression and immunoevasion in pan-cancer.

BACKGROUND: IFN-λ has been shown to have a dual function in cancer, with its tumor-suppressive roles being well-established. However, the potential existence of a negative ''tumor-promoting'' effect of endogenous IFN-λ is still not fully understood. METHODS: We conducted a comprehensive review and analysis of the perturbation of IFN-λ genes across various cancer types. Correlation coefficients were utilized to examine the relationship between endogenous IFN-λ expression and clinical factors, immune cell infiltration, tumor microenvironment, and response to immunotherapy. Genes working together with IFN-λ were obtained by constructing the correlation-based network related to IFN-λ and the gene interaction network in the KEGG pathway and IFN-λ-related genes obtained from the networks were integrated as candidate markers for the prognosis model. We then applied univariate and multivariate COX regression models to select cancer-specific independent prognostic markers associated with IFN-λ and to investigate risk factors for these genes by survival analysis. Additionally, computational methods were used to analyze the transcriptome, copy number variations, genetic mutations, and methylation of IFN-λ-related patient groups. RESULT: Endogenous expression of IFN-λ has been linked to poor prognosis in cancer patients, with the genes IFN-λ2 and IFN-λ3 serving as independent prognostic markers. IFN-λ acts in conjunction with related genes such as STAT1, STAT2, and STAT3 to affect the JAK-STAT signaling pathway, which promotes tumor progression. Abnormalities in IFN-λ genes are associated with changes in immune checkpoints and immune cell infiltration, which in turn affects cancer- and immune-related pathways. While there is increased immune cell infiltration in patients with IFN-λ expression, this does not improve survival prognosis, as T-cell dysfunction and an inflammatory environment are also present. The amplification of IFNL2 and IFNL3 copy number variants drives specific endogenous expression of IFN-λ in patients, and those with this specific expression have been found to have more mutations in the TP53 gene and lower levels of DNA methylation. CONCLUSION: Our study integrated multi-omics data to provide a comprehensive insight into the dark side of endogenous IFN-λ, providing a fundamental resource for further discovery and therapeutic exploration in cancer.

Multi-omics analysis reveals a molecular landscape of the early recurrence and early metastasis in pan-cancer.

Cancer remains a formidable challenge in medicine due to its propensity for recurrence and metastasis, which can result in unfavorable treatment outcomes. This challenge is particularly acute for early-stage patients, who may experience recurrence and metastasis without timely detection. Here, we first analyzed the differences in clinical characteristics among the primary tumor, recurrent tumor, and metastatic tumor in different stages of cancer, which may be caused by the molecular level. Moreover, the importance of predicting early cancer recurrence and metastasis is emphasized by survival analyses. Next, we used a multi-omics approach to identify key molecular changes associated with early cancer recurrence and metastasis and discovered that early metastasis in cancer demonstrated a high degree of genomic and cellular heterogeneity. We performed statistical comparisons for each level of omics data including gene expression, mutation, copy number variation, immune cell infiltration, and cell status. Then, various analytical techniques, such as proportional hazard model and Fisher's exact test, were used to identify specific genes or immune characteristics associated with early cancer recurrence and metastasis. For example, we observed that the overexpression of BPIFB1 and high initial B-cell infiltration levels are linked to early cancer recurrence, while the overexpression or amplification of ANKRD22 and LIPM, mutation of IGHA1 and MUC16, high fibroblast infiltration level, M1 polarization of macrophages, cellular status of DNA repair are all linked to early cancer metastasis. These findings have led us to construct classifiers, and the average area under the curve (AUC) of these classifiers was greater than 0.75 in The Cancer Genome Atlas (TCGA) cancer patients, confirming that the features we identified could be biomarkers for predicting recurrence and metastasis of early cancer. Finally, we identified specific early sensitive targets for targeted therapy and immune checkpoint inhibitor therapy. Once the biomarkers we identified changed, treatment-sensitive targets can be treated accordingly. Our study has comprehensively characterized the multi-omics characteristics and identified a panel of biomarkers of early cancer recurrence and metastasis. Overall, it provides a valuable resource for cancer recurrence and metastasis research and improves our understanding of the underlying mechanisms driving early cancer recurrence and metastasis.

Integration of multi-omics data reveals a novel hybrid breast cancer subtype and its biomarkers.

Tumor heterogeneity in breast cancer hinders proper diagnosis and treatment, and the identification of molecular subtypes may help enhance the understanding of its heterogeneity. Therefore, we proposed a novel integrated multi-omics approach for breast cancer typing, which led to the identification of a hybrid subtype (Mix_Sub subtype) with a poor survival prognosis. This subtype is characterized by lower levels of the inflammatory response, lower tumor malignancy, lower immune cell infiltration, and higher T-cell dysfunction. Moreover, we found that cell-cell communication mediated by NCAM1-FGFR1 ligand-receptor interaction and cellular functional states, such as cell cycle, DNA damage, and DNA repair, were significantly altered and upregulated in patients with this subtype, and that such patients displayed greater sensitivity to targeted therapies. Subsequently, using differential genes among subtypes as biomarkers, we constructed prognostic risk models and subtype classifiers for the Mix_Sub subtype and validated their generalization ability in external datasets obtained from the GEO database, indicating their potential therapeutic and prognostic significance. These biomarkers also showed significant spatially variable expression in malignant tumor cells. Collectively, the identification of the Mix_Sub breast cancer subtype and its biomarkers, based on the driving relationship between omics, has deepened our understanding of breast cancer heterogeneity and facilitated the development of breast cancer precision therapy.

Oncogenic signaling pathway dysregulation landscape reveals the role of pathways at multiple omics levels in pan-cancer.

Dysregulation of signaling pathways plays an essential role in cancer. However, there is not a comprehensive understanding on how oncogenic signaling pathways affect the occurrence and development with a common molecular mechanism of pan-cancer. Here, we investigated the oncogenic signaling pathway dysregulation by using multi-omics data on patients from TCGA from a pan-cancer perspective to identify commonalities across different cancer types. First, the pathway dysregulation profile was constructed by integrating typical oncogenic signaling pathways and the gene expression of TCGA samples, and four molecular subtypes with significant phenotypic and clinical differences induced by different oncogenic signaling pathways were identified: TGF-ß+ subtype; cell cycle, MYC, and NF2- subtype; cell cycle and TP53+ subtype; and TGF-ß and TP53- subtype. Patients in the TGF-ß+ subtype have the best prognosis; meanwhile, the TGF-ß+ subtype is associated with hypomethylation. Moreover, there is a higher level of immune cell infiltration but a slightly worse survival prognosis in the cell cycle, MYC, and NF2- subtype patients due to the effect of T-cell dysfunction. Then, the prognosis and subtype classifiers constructed by differential genes on a multi-omics level show great performance, indicating that these genes can be considered as biomarkers with potential therapeutic and prognostic significance for cancers. In summary, our study identified four oncogenic signaling pathway-driven patterns presented as molecular subtypes and their related potential prognostic biomarkers by integrating multiple omics data. Our discovery provides a perspective for understanding the role of oncogenic signaling pathways in pan-cancer.

Identification of a Combined RNA Prognostic Signature in Adenocarcinoma of the Lung.

BACKGROUND Adenocarcinoma of the lung is a type of non-small cell lung cancer (NSCLC). Clinical outcome is associated with tumor grade, stage, and subtype. This study aimed to identify RNA expression profiles, including long noncoding RNA (lncRNA), microRNA (miRNA), and mRNA, associated with clinical outcome in adenocarcinoma of the lung using bioinformatics data. MATERIAL AND METHODS The miRNA and mRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database, and lncRNA expression profiles were downloaded from The Atlas of Noncoding RNAs in Cancer (TANRIC) database. The independent dataset, the Gene Expression Omnibus (GEO) accession dataset, GSE81089, was used. RNA expression profiles were used to identify comprehensive prognostic RNA signatures based on patient survival time. RESULTS From 7,704 lncRNAs, 787 miRNAs, and 28,937 mRNAs of 449 patients, four joint RNA molecular signatures were identified, including RP11-909N17.2, RP11-14N7.2 (lncRNAs), MIR139 (miRNA), KLHDC8B (mRNA). The random forest (RF) classifier was used to test the prediction ability of patient survival risk and showed a good predictive accuracy of 71% and also showed a significant difference in overall survival (log-rank P=0.0002; HR, 3.54; 95% CI, 1.74-7.19). The combined RNA signature also showed good performance in the identification of patient survival in the validation and independent datasets. CONCLUSIONS This study identified four RNA sequences as a prognostic molecular signature in adenocarcinoma of the lung, which may also provide an increased understanding of the molecular mechanisms underlying the pathogenesis of this malignancy.

Identification of Aberrant Chromosomal Regions in Human Breast Cancer Using Gene Expression Data and Related Gene Information.

BACKGROUND: Chromosomal instability is a hallmark of cancer. Chromosomal imbalances, like amplifications and deletions, influence the transcriptional activity of genes. These imbalances affect not only the expression of genes in the aberrant chromosomal regions, but also that of related genes, and may be relevant to the cancer status. MATERIAL AND METHODS: Here, we used the 7 publicly available microarray studies in breast cancer tissues and propose a general and unsupervised method by using the gene expression data and related gene information to systematically identify aberrant chromosomal regions. This method aimed to identify the chromosomal regions where the genes and their related genes both show consistent changes in the expression levels. Such patterns have been reported to be associated with the chromosomal aberrations and may be used in cancer diagnosis. RESULTS: We compared 488 tumor and 222 normal samples from 7 microarray-based human breast cancer studies and detected the amplifications of 8q11.21, 14q32.11, 4q21.23, 18q11.2, Xq28, and the deletions of 3p24.1, 10q23.2 (BSCG1), 20p11.21, 9q21.13, and 1q41, which may be involved in the novel mechanisms of tumorigenesis. In addition, several known pathogenic genes, transcription factors (TFs), and microRNAs (miRNAs) associated with breast cancer were found. CONCLUSIONS: This approach can be applied to other microarray studies, which provide a new and useful method for exploring chromosome structural variations in different types of diseases.

[Ecological behavior comparison between Anopheles pseudowillmori and A. willmori in villages with malaria outbreaks in Motuo County, Tibet Autonomous Region].

OBJECTIVE: To understand the ecological behaviors of Anopheles pseudowillmori and A. willmori in medium or high altitude areas of Motuo County, Tibet Autonomous Region, and their transmission potential for malaria. METHODS: The methods of human net traps, cow baited trap, house baited trap, pig baited trap and CDC light traps were adopted for investigating the mosquito density, biting activity at night and in or out door preference of biting. All mosquitoes morphologically identified as A. maculatus group were labeled in accordance with the capture time, place and method, and PCR were used to identify the species. The resting habits were investigated with the morning capture and daytime collecting methods, and the larvae collected in different wa- ter bodies were reared to adults to study the breeding place. RESULTS: A total of 1,053 A. maculatus group were collected, and of which, 331 (31.43%) were identified as A. pseudowillmori, and 722 (68.57%) as A. willmori. The number of A. willmori were higher than A. pseudowillmori in both outer and inner doors (P < 0.01, P < 0.05) , while no statistically significant difference was found of the constituent ratio of the anopheles between outer and inner doors. The multiparous ratios of A. pseudowillmori and A. willmori were 65.90% and 69.86%, respectively, (P > 0.05). The A. pseudowillmori preferred to biting outer doors (P < 0.05), while A. willmori preferred both outer and inner doors (P > 0.05). Both mosquitoes had one biting activity peak in the night, and the biting activity peak of A. willmori was from 21 to 22 o'clock, while the peak of A. pseudowillmori was from 24 to next morning 1 o' clock. CONCLUSION: In the Anopheles, the constituent ratio and density of A. willmori are higher than those of A. pseudowillmori in semi-high altitudes area of Motuo County, Tibet, and there are obvious differences of ecological behaviors between A. willmori and A. pseudowillmori, and A. willmori has the more capacity of transmitting malaria than A. pseudowillmori.

MicroRNA regulation constrains the organization of target genes on mammalian chromosomes.

The regulation of microRNAs (miRNAs) is a complicated process requiring a large number of molecular events to be coordinated in both space and time. It is not known whether this complicated regulation process constrains the organization of target genes on mammalian chromosomes. We performed a genome-wide analysis to provide a better picture of chromosomal organization of miRNA target genes. Our results showed clustering of the target genes (TGs) of miRNAs on mammalian chromosomes, and further revealed that the particular gene organization is constrained by miRNA regulation. The clustering pattern of TGs provides an insight into the complexity of miRNA regulation.