RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Introduction
Cervical cancer stands as the fourth most frequently diagnosed cancer and the fourth leading cause of cancer-related deaths among women, with an estimated 604,000 new cases and 342,000 deaths globally in 2020 ( Sung et al., 2021 ). Although incidence and mortality rates have declined in most regions worldwide over the past decades, substantial disparities in cervical cancer mortality still exist between impoverished and non-impoverished areas ( Siegel, Miller & Jemal, 2019 ). Human papillomavirus (HPV) is a necessary but insufficient cause of cervical cancer ( Walboomers et al., 1999 ), with other contributing factors including certain sexually transmitted infections, smoking, aging, and prolonged use of oral contraceptives ( Burd, 2003 ). Despite significant advances in preventive vaccination, cytological screening, and cervical cancer treatment, a decline in survival rates due to late diagnosis and inadequate treatment remains a paramount challenge. Although HPV vaccination can play a positive preventive role ( Zou, Huang & Li, 2022 ), there is still no optimal therapeutic drug for cervical cancer to date. Thus, a deeper understanding of the proteomic characteristics of cervical cancer is imperative, actively seeking target molecules in the occurrence, development, and diagnosis of cervical cancer, providing a basis for the study of cervical cancer mechanisms and personalized medicine.

Proteomics plays a crucial role in elucidating the pathogenic mechanisms of tumors ( Collinson et al., 2024 ). This study employs labeled-free quantitative proteomics technology to identify differentially expressed proteins (DEPs) in cervical cancer groups compared to normal groups. Label-free protein quantitation is a mass spectrometry-based method ( Makepeace et al., 2024 ) wherein protein peptides are enzymatically digested and analyzed, obviating the need for costly stable isotope labels as internal standards and only analyzing the mass spectral data produced during large-scale protein identification ( Zou, Huang & Li, 2022 ) . This process aids in systematically understanding the proteome after the onset of cervical cancer. Utilizing bioinformatics, this study analyzes the major functions and signaling pathways of these DEPs, exploring their molecular mechanisms in the onset of cervical cancer.

In this research, we use mass spectrometry-based quantitative proteomics methods to obtain the proteomic spectrum of cervical squamous cell carcinoma, achieving comprehensive analysis of differentially expressed proteins through GO/KEGG analysis and Gene Set Enrichment Analysis (GSEA). Our analysis reveals 562 up-regulated or down-regulated proteins compared to the control. The proteins DSP, PPP1R13L and ANXA8 are selected for further analysis. Immunohistochemistry and western blotting of cervical squamous cell carcinoma tissue slices with antibodies against DSP, PPP1R13L and ANXA8 is conducted, hypothesizing these proteins’ fundamental roles in the regulation and progression of cervical cancer cells.
Figure 1: Differential expression of the proteins.
(A) A volcano plot is drawn using the protein expression difference multiple and P-value between the two groups of samples, showing the signiﬁcant differences in data between the two groups of samples. Signiﬁcantly up-regulated or down-regulated DEPs are represented in red or green, while proteins with no signiﬁcant change are shown in black. (B) Compared with the control group, hierarchical cluster analysis of DEPs in the cervical cancer group shows that DEPs are up-regulated (orange) or down-regulated (blue). Ctr1~5 are ﬁve biological replicates of the normal group. CC1~5 are ﬁve biological replicates of the cervical cancer group. Each row represents the relative expression of a protein in 10 samples, and each column represents the relative expression of differentially abundant proteins in each experimental group. (P < 0.05, log2 fold change ≥1.0 or ≤−1.0). X-axis: log2 converted difference multiple values; Y-axis: −log10 transformed P-value.

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DOI: 10.7717/peerj.17444/fig-1
Figure 2: GO analysis.
(A) GO channel enrichment statistical histogram (top 20) (B) GO enrichment analysis bubble diagram of DEPs. The largeness of the bubble indicates more DEPs. The color of the circle indicates different GO functional categories, and the size of the circle indicates the number of different proteins contained in the function. Ordinate :GO function name, ordinate :−log10 transformed P value. (C) The bar chart describes the distribution of GO major terms in the three GO categories. The horizontal coordinate represents the P-value after the −log10 transformation of the gene, and the vertical coordinate represents the GO function name of the gene, which is marked with Rich Factor value in the blue, red and yellow components respectively.

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Figure 3: Up-regulated and down-regulated differential protein GO enrichment analysis.
(A) Up-regulated protein GO function enrichment histogram (cc); (B) up-regulated protein GO function enrichment histogram (BP); (C) up-regulated protein GO function enrichment histogram (mf) Y-axis: significantly enriched GO analysis items; X-axis: P-value after −log10 transformation. The longer the column, the more significant the richness of the project. The blue and green column colors indicate the up-and-down changes in protein.

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Figure 4: KEGG analysis results.
(A) KEGG pathway enrichment histogram (top 20). Included are metabolic pathway, coronavirus disease, splicesome, actin cytoskeleton, amyotrophic lateral sclerosis, etc. Ordinate: KEGG pathway name; Ordinate: −log10 transformed P-value. The numbers in the column represent the Rich Factor values. (B) KEGG pathway concentration statistical bubble chart (top 20): including the vitamin digestion, protein export, cortisol synthesis, complement and coagulation cascades, nucleocytoplasmic transport, protein processing in endoplasmic reticulum, prion disease, pathways of neurodegeneration, multiple diseases, pathways in cancer, etc. The ordinate is the KEGG pathway name; abscissa: rich factor value; the color of the circle represents the −log10 transformed p-value; the darker the color, the smaller the p-value. (C) The KEGG Pathway Enrichment Bar Chart presents a series of related metabolic or signaling pathways, such as vitamin digestion and absorption, protein export cortisol synthesis and secretion, complement and coagulation cascades, cholesterol metabolism, nucleocytoplasmic transport, spliceosome, neuroactive ligand-receptor interaction, nicotinate and nicotinamide metabolism, vascular smooth muscle contraction, cGMP-PKG signaling pathway, and so on.

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Figure 5: GSEA analysis results.
(A) GSEA-GO enrichment analysis NES value top 20 bubble chart: Ordinate: GO function name, Abscissa: Gene_ratio, Circle size: the number of enriched genes in the GO function item; circle color: Enrichment signiﬁcance: P value. (B) GSEA-KEGG enrichment analysis NES value top 20 bubble chart; Ordinate: KEGG pathway name, abscissa: Gene_ratio; circle size: the number of genes enriched in KEGG pathway entries; circle color: enrichment signiﬁcance: P value. (C) GSEA-GO enrichment score curve graph; the GSEA-GO ES enrichment score curve graph is composed of three parts. The upper part is the enrichment score (ES) line graph, displaying the scores (running ES) throughout the scoring process. Peaks in the line graph represent the ES values of the protein function set; a larger absolute ES value indicates a higher degree of enrichment. A positive ES value signiﬁes that the functional protein set is enriched in up-regulated proteins, while a negative ES value signiﬁes enrichment in down-regulated proteins. Functional sets with peaks at the top or bottom are typically of greater concern. The middle section displays the position of members of the function set in the expression set list, with black vertical lines representing members of the function set. The colored bands, composed of members of the expression set arranged in a certain order, are red for up-regulated and blue for down-regulated. The lower part displays the rank-value distribution of all proteins. (D) GSEA-KEGG enrichment score curve graph: Similar to the GSEA-GO graph, the GSEA-KEGG ES enrichment score curve graph is divided into three parts. The upper part is the Enrichment Score (ES) line graph, displaying the ES values (running ES) throughout the scoring process. The middle section and lower parts have the same functionality as mentioned in GSEA-GO.

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Figure 6: Structural domain, transcription factor analysis, and sub-cellular localization analysis.
(A) Enrichment classiﬁcation statistical bubble chart for protein structure domain (top 20). The bubble chart shows that many proteins structure domains were involved in the study, such as guanine nucleotide exchange factor N-terminal, anaphylatoxin-complement system domain, complement_C3_C4_C5, anaphylatoxin-complement system, conserved site RNA recognition motif domain, serpin family, serpin domain, serpin superfamily, and the RNA-binding domain superfamily. Y-axis: structure domain name; X-axis: Rich Factor Value. The circle color represents the −log10 transformed P-value; a deeper color indicates a smaller P-value. The circle size indicates the number of differential proteins contained in this pathway. (B) Enrichment classification statistical bar chart for protein structure domain (Top 20). Y-axis: structure domain name; X-axis: −log10 transformed P-value. The number on the bar represents the Rich Factor value. (C) Top 10 TF family statistical chart; (D) protein subcellular localization distribution chart: Different colors represent different cellular locations, and the percentage indicates the proportion of proteins located there among all differential proteins.

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Figure 7: Protein-Protein interaction network analysis (PPI).
(A) Direct interaction network of differential proteins; (B) interaction network of differential proteins with all other identified proteins. In the network: blue nodes represent the most dramatically up-regulated proteins. Cyan nodes represent the most dramatically down-regulated proteins. Yellow nodes represent differential proteins obtained through statistical testing using ANOVA P. Grey nodes represent other identified proteins. The node size indicates the degree value.

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Figure 8: Employing immunohistochemical and western blotting methods to detect the expression of DSP, PPP1R13L and ANXA8 in cervical cancer tissues and paired adjacent cancer tissues.
(A) Compared with the para cancer, the immunohistochemical results for DSP, PPP1R13L and ANXA8 demonstrated that their protein expression increased, (B) as well as overexpression of them in the cases of cervical cancer tissues by western blotting, (C) and there is a signiﬁcant difference by the statistical results. P = 0.0015 & adjacent cancer tissues. (D) The GEPIA database was used to validate the expression of DSP, PPP1R13L and ANXA8 in human cancers and normal cervix, and it was found that the transcription levels of DSP, PPP1R13L and ANXA8 increased signiﬁcantly (P = 0.05 & adjacent cancer tissues).

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DOI: 10.7717/peerj.17444/fig-8
Top 20 upregulated proteins in cervical cancer group.