Background

Small cell carcinoma of the cervix (SCCC) is a relatively rare subtype that accounts for < 1% of all the malignancies originating from the cervix [1, 2]. However, the extensive aggressiveness and multiple resistance to current regimens made it the most lethal cervical cancer [3]. In the United States, the 5-year survival rates of SCCC were 81.8% (stage IA), 55.4% (stage IB), 22.2% (stage IIB), 24.4% (stage IIIB), 4.1% (stage IVA), and 7.1% (stage IVB). These were much poorer than the outcomes of patients with either squamous cancer or adenocarcinoma [4]. Similar findings were also reported by Zheng et al. In a Chinese SCCC cohort, the 3-year survival rates were 100% (stage IA), 62% (stage IB1), 53% (stage IB2), 36% (stage IIA), 29% (stage IIB), 50% (stage IIIB), and 0% (stage IVA) [5]. Due to its rarity, the current publications about SCCC are mostly descriptive and based on small groups, which results in low-quality data and even controversies. For example, in a study containing 188 SCCCs, the authors concluded that adjuvant chemo- or chemoradiotherapy was associated with improved survival in patients with stages IIB-IVA tumors [6]. Furthermore, Wei et al. also reported that radiotherapy could improve the prognosis of SCCC, regardless of tumor stage [7]. Consistently, brachytherapy was associated with improved overall survival in locally advanced small cell carcinoma of the cervix (SCCC), which was underutilized in clinical practice [8]. However, in a meta-analysis that included the information of 1,904 SCCC patients, the authors found that adjuvant radiotherapy might not be helpful to improve the treatment outcomes [9]. Moreover, the follow-up of 68 Korean SCCC patients also demonstrated that a combination of chemotherapy and radiation showed no more benefits than single chemotherapy. In addition, the authors claimed that patients who received neoadjuvant chemotherapy showed a poorer prognosis than those who did not [10]. In a Chinese cohort containing 93 SCCC patients, Li et al. found that the FIGO stage was the only prognostic factor, while treatment modality did not have an impact on overall survival [11]. These different findings were largely attributed to our limited knowledge about SCCC, which significantly hindered the precise prevention and targeting therapy. Thus, further exploring the underlying molecular mechanisms in SCCC is urgently needed.

Several studies have reported the genetic alterations in SCCC. In a panel of eight SCCC cases, the common genetic events were LOH at 9p21 (42.9%) and 3p deletions (37.5%). One study found that mutations of the P53 gene occurred in 62.5% of patients while no K-Ras mutation was detected [12]. However, a later study proved that both LOH and P53 mutations were rare events in 10 SCCC patients [13]. In 2016, Lee et al. performed whole-exome sequencing to investigate the integrative mutation profiles of SCCC. They reported the most frequent mutations were found in ATRX, ERBB4, PTEN, RICTOR, and TSC1/2 genes, implying their potential functions during the initiation and development of SCCC [14]. In a recent study, the authors investigated the mutations in 10 SCCC cases using a next-generation sequencing-based 637-gene panel. The common mutations were detected in P53 (40%) and PIK3CA (30%). Rare mutations were also detected in K-RAS, c-MYC, NOTCH1, BCL6 or NCOA3, PTEN, RB1, BRCA1, BRCA2, and ARID1B [15].

Besides the alterations at DNA and RNA levels, the alterations of protein expression and activity also showed significant influences in cancers. Quantitative proteomics is a novel concept for systematically analyzing the whole-protein profiles in a cell, tissue, organ, or other biological systems [16, 17]. It can decipher not only aberrations of protein expression but also post-translational modifications that affect the functions of certain proteins [18, 19]. In the present study, we performed a quantitative proteomic analysis in a panel of SCCC patients, aiming to illustrate the key proteins and pathways involved in this lethal malignancy.

Materials and methods

Sample collection

A total of 18 SCCC and six matched normal cervix (at least 2 cm away from tumor margin) FFPE tissues were collected in the Department of Gynecology, First Affiliated Hospital of Zhengzhou University from January 2013 to December 2017. H&E and IHC slides were re-evaluated by experienced pathologists to confirm the diagnosis. The clinic-pathological information was extracted from medical records and summarized in Table 1. Tumor stages were determined according to FIGO criteria (version 2018). The detection of 21 HPV subtypes was performed using the assay kit obtained from Hybribio (Hong Kong, China). Follow-up was performed face-to-face in the outpatient office or by telephone. Overall survival (OS) was defined as the period from the diagnosis to the death. Disease-free survival (DFS) was defined as the period from the time of surgery to the diagnosis of tumor recurrence/metastasis. Formal consents were provided by all patients and our study was approved by the Ethical Committee at the First Affiliated Hospital of Zhengzhou University.

Table 1 The baseline characteristics of SCCC patients
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Protein extraction, quality control

Total protein was extracted from FFPE tissue with 10 μm thickness. Briefly, after dewaxing and rehydration, ~ 1 cm2 FFPE tissue sections were thoroughly mixed with 200 μl lysis buffer (4% SDS, 1% protease inhibitor cocktail (Sigma) and put on ice for 15 min. Then the samples were ultrasonicated for 10 min and then heated at 95 °C, 750 rpm for 1 h. After centrifugation, supernatants were collected and a BCA assay was performed to determine the protein concentration.

Sample enzymolysis and desaltation

The samples were treated with DTT solution (2 µL 500 mM) in a water bath at 56 °C for 1 h. Then 20 µL IAM (500 mM) was added and the samples were kept at room temperature blocked from light for 45 min. 60 µg protein was purified with SP3 beads (single-pot solid-phase-enhanced sample preparation, GE Healthcare) and a 20 µL digestion buffer (50 mM NH4CO3, 50 mM CaCl2, 2.4 µg trypsin) was used to resuspend the beads. The proteins were digested overnight at 37 °C and 1 µg trypsin was further added to extend the digestion for another 3 h at 37 °C. Then the resulting peptides was precipitated on the beads using final concentration of 95% ACN, and washed with 100% ACN. Peptides were eluted from SP3 beads by 2% ACN.

MS database generation

A total of 100 µg of digested peptides that mixed equally volume from different samples was pre-separated with Waters XBridge Shield C18 RP column, 3.5 um, 4.6 × 250 mm on HPLC Shimadzu LC20AD (Shimadzu, Japan) with a 90 min gradation. Mobile phases A (H2O, adjusted pH to 10.0 using ammonium hydroxide) and B (80% acetonitrile) were used to develop a gradient elution. The solvent gradient was set as follows: 0–5 min, 5%B; 5–25 min, 5–12%B; 25–60 min, 12–22%B; 60–70 min, 22–35%B; 70–75 min, 35–80%B; 75–80 min, 80%B; 80–82 min, 80–5%B, 82–90 min, 5%B, and 1 mL/min flowrate. The eluates were collected for a tube per minute and merged into 10 fractions. All fractions were dried under vacuum and reconstituted in 2% ACN in water. After mix with 0.2 µL standard peptides, the fraction samples were used for subsequent analyses.

Transition library construction was performed using an Ultimate RSLC nano 3000 UHPLC system coupled with an Orbitrap Q Exactive HF mass spectrometer (Thermo Fisher) operating in data-dependent acquisition (DDA) mode. Each fraction sample containing iRT was injected into a Thermo Acclaim PepMap RSLC C18 column (2 µm, 75 µm × 75 cm) and analyzed with gradient elution as follows ( A:2% ACN, 0.1% FA; B:80% ACN, 0.1% FA): 0–8 min, 3–6% B; 8–9 min, 6% B; 9–30 min, 6–12% B;30–105 min, 12–24% B; 105–125 min, 24–35% B; 125–126 min, 35–90% B; 126–141 min, 90% B; 141–142 min, 6% B; 142–150 min, 6% B. The Q-Exactive HF mass spectrometer was operated in positive polarity mode with spray voltage of 2.0 kV and capillary temperature of 250 °C. Full MS scans range from 350 to 1800 m/z were acquired at a resolution of 60,000 (at 200 m/z) with a gain control (AGC) target value of 1 × 106 and a maximum ion injection time of 60 ms. The top 40 most abundant precursor ions from MS1 were selected for fragmentation using higher energy collisional dissociation (HCD). The fragment ions were analyzed in MS2 at a resolution of 30,000 (at 200 m/z) with the conditions as follows: AGC target value was 1 × 105, the maximum ion injection time was 90 ms, a normalized collision energy of 29% and the dynamic exclusion parameter of 9s.

DIA data acquisition

Peptides of each sample was reconstituted in mobile phases A (2% ACN, 0.1% FA) and mixed with 0.2 μL 10 × iRT standards (Biognosys, Schlieren, Switzerland). The data of lysed peptide samples were acquired through an Orbitrap Q Exactive HF mass spectrometry (Thermo Fisher Scientific, Waltham, MA) in the data-independent acquisition (DIA) mode with spray voltage of 2.0 kV, Nanospray Flex™ (ESI) and capillary temperature of 250 °C. For DIA acquisition, the m/z range covered from 350 to 1800 m/z with 40 scan windows. The liquid conditions were the same as described above. The MS1 resolution was set to 60,000 (at 200 m/z) with the full scan AGC target value of 1 × 106 and the maximum ion injection time of 50 ms. Peptides were fragmented by HCD with aa normalized collision energy of 29% and detected in MS2 with the resolution of 30,000 (at 200 m/z) and AGC target value of 1 × 106.

Data analysis

The DIA data were searched against the human UniProt database (20,365 sequences) using Spectronaut (v14.5.200813.47784). The library generation with DDA data applied the default settings with trypsin/P digest rule. 2 missed trypsin cleavage was allowed by default. The precursor peptide mass tolerance was 20 ppm, and fragment ion mass tolerance was 0.02 Da. The FDR cutoff for precursor and protein identification was 0.01, and other parameters were set as default.

Validation of CDN2A and SYP expressions using immunohistochemistry (IHC)

First, 5 μm slides were dewaxed and re-hydrated, and antigen retrieval was done in a microwave. Then the endogenous peroxidase activity was blocked and samples were incubated with primary antibodies at 4 °C overnight. On the second day, the slides were incubated with secondary antibodies for 30 min at 37 °C. Next, the specific staining was developed using a commercial kit (Zhong Shan-Golden Bridge Biological Technology, Beijing, China). The staining results were evaluated by two pathologists in our hospital.

Quantification and statistical analysis

The output quantified proteins with at least 30% appearance in all samples were selected for further analysis. The proteins were normalized with the FOT (Fraction of Total) method that normalized every protein intensity equals to every-protein-intensity/all-protein-intensity-in-one-sample × 1,000,000. The absent values were replaced by the half of the minimum value in the original data. FC (Fold change) ≥ 2 and P < 0.05 were set as the cut-off for differential proteins (DEPs). It should be noted that we adjusted a relatively loose thresholds with FC ≥ 1.5 and P < 0.05 as the cut-off for DEPs in comparison of recurrent and non- recurrent group to gain the maximum information from limited samples. The GO and KEGG functional gene enrichments were achieved through the R package (clusterProfiler, v3.16.1). The annotation database was org.Hs.eg.db (v3.11.4). The background proteins were set with all the quantified proteins. The differential proteins were input to generate the enrichment pathway list and figures. All the data was deposited in the Integrated Proteome Resources (Project number: PXD035382).

Results

Baseline characteristics of SCCC patients

Considering its rarity, we screened all patients diagnosed with cervical cancer in our hospital from January 2013 to December 2017. In total, there were 3,092 squamous cell carcinoma (87.0%), 317 adenocarcinoma (8.9%), 25 SCCC (0.7%) and 121 other subtypes (3.4%). After re-evaluation, 18 SCCC cases with intact clinic-pathological records were qualified for the following analysis, including 7 stage IB (2 IB1, 4 IB2, 1 IB3), 4 stages II (2 IIA1, 1 IIA2, 1 IIB), and 7 stage IIIC1p tumors. The median age was 44 years old (ranging from 26 to 64). In 16 patients who had taken a high-risk HPV test, HPV18 was the most common subtype (62.5%, 10/16), while three patients were high-risk HPV-negative (18.8%). The details of patients’ characteristics were listed in Table 1.

Overview of the proteomics profiles

Using the conventional DDA mass spectrometry, we established a protein library of the normal human cervix (n = 6) and SCCC (n = 18) tissues. The DIA-MS data was searched against the DDA library and a total of 7,819 proteins were identified. After being filtered, 6786 proteins were quantified in at least 30% samples and the missing values were replaced by the half of the minimum value in the original data (Additional file 1: Table S1). To assess the reliability and reasonability of our experiment, a Pearson correlation and Principal Component Analysis (PCA) were calculated using all proteins retained. All the results indicated a clear separation between SCCC from the normal cervix (Fig. 1a).

Fig. 1
figure 1

PCA analysis and volcano plot. a PCA analysis indicated a clear separation between SCCC (blue, title with Ca + sample number) from the normal cervix samples (red, titled with Pan + sample number). b The X axis represents fold changes of proteins (log2FC), and the Y axis the indicated the corresponding P values. The up- and down-regulated proteins were indicated with green and red dots. The gray dots meant no significant changes of these proteins

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Changes in protein expression in SCCC

To examine the abnormalities in SCCC, differentially expressed proteins between SCCC and normal cervix were defined with the cut-off of FC ≥ 2 and P < 0.05. As a result, 1311 proteins were differentially expressed in SCCC with 780 up-regulated and 531 down-regulated compared with normal control (Fig. 1b and Additional file 2: Table S2). The top-30 DEPs and their details were listed in Tables 2 and 3.

Table 2 Top 30 up-regulated DEPs
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Table 3 Top 30 down-regulated DEPs
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To gain insights into the biological significance of these DEPs, the GO and KEGG enrichment analyses were performed through the R package (cluster Profiler, v3.16.1). As to the GO enrichment analysis for all these DEPs, the most enriched biological processes were DNA replication related, including nuclear DNA replication (GO:0,033,260), DNA replication (GO:0,006,260), cell cycle DNA replication (GO:0,044,786), and DNA strand elongation involved in DNA replication (GO:0,006,271), whereas the most enriched cellular components were the extracellular matrix (GO:0,031,012) and collagen-containing extracellular matrix (GO:0,062,023). Noteworthily, the GO term of the replication fork (GO:0,005,657) was also enriched in cellular components. Interestingly, most of the enriched GO terms in molecular functions were focused on DNA replication and the related energy metabolism, including DNA-dependent ATPase activity (GO:0,008,094), 3'-5' DNA helicase activity (GO:0,017,116), and others (Additional file 3: Table S3a). All these results indicated that DNA replication and cell proliferation associations were extremely frequent in SCCC. To get a more detailed view of SCCC, we further performed GO enrichment analysis for the up- and down-regulated DEPs separately. As for the up-regulated DEPs, all the enriched GO terms indicated the DNA replication and mitochondrial related in biological processes. In the molecular function, chromosomal part and mitochondrial envelope (cellular components), catalytic activity acting on DNA, and ATPase activity were mostly enriched (Fig. 2a). As for down-regulated DEPs, circulatory system development (biological processes), extracellular matrix (cellular components), and structural molecule activity (molecular function) were the most enriched terms (Fig. 2b).

Fig. 2
figure 2

GO analysis of the up- and down-regulated proteins. (a) and (b) showed results of GO enrichment analysis for up- and down-regulated proteins respectively. The Y-axis represents the GO terms, and X-axis represents the Rich factor. Rich factor means the ratio of the number of DEPs to the total number of proteins annotated in this GO terms. The colour of the dot means different P.adjust value, and the size of dot indicates the number of DEPs in this term

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Next, we performed a KEGG analysis to investigate key pathways involved in SCCC, which result in two pathways being significantly enriched, including the DNA replication (hsa03030) and Complement and coagulation cascades (hsa04610) (Additional file 3: Table S3b). Similarly, the up-and down-regulated DEPs were analyzed for KEGG enrichment. In the up-regulated group, the DEPs were enriched in the pathways of DNA replication (hsa03030), lysosome (hsa04142), mismatch repair (hsa03430), and Herpes simplex virus 1 infection (hsa05168) (Fig. 3a). For the down-regulated DEPs, the most enriched pathways were Complement and coagulation cascades (hsa04610), followed by Proteoglycans in cancer (hsa05205), Focal adhesion (hsa04510), and Drug metabolism-cytochrome P450 (hsa00982) (Fig. 3b).

Fig. 3
figure 3

KEGG analysis of the up- and down-regulated proteins. (a) and (b) showed results of KEGG enrichment analysis for up- and down-regulated proteins respectively. The Y-axis represents the KEGG pathways, and X-axis represents the Rich factor. Rich factor means the ratio of the number of DEPs to the total number of proteins annotated in this pathway. The colour of the dot means different P.adjust value, and the size of dot indicates the number of DEPs in this pathway

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Validation of CDN2A and SYP expression via IHC

As previously reported, overexpression of CDN2A (Cyclin-dependent kinase inhibitor 2A, also known as P16, P42771) and SYP (Synaptophysin, P08247) was a distinctive feature of SCCC [13, 20, 21]. As shown by our data, the two proteins were both up-regulated in our cohort (CDN2A: rank 4, FC = 45.2, P = 0.001; SYP: rank 142, FC = 7.46, P = 0.011). For the validation, we investigated the protein level of CDN2A and SYP in all the SCCC and the matched non-cancerous tissues using IHC. Consistent with the proteomics results, CDN2A and SYP were positively stained in 88.9% (16/18) and 66.7% (12/18) SCCC tissues, while negatively stained in the non-cancerous samples (the representative figures were shown in Fig. 4). For further validation, we collected 5 more SCCC tissues to investigated the protein expression of STK39 (Top 1 of the up-regulated DEPs). The results of IHC demonstrated that STK39 was over-expressed in 80% (4/5) cases, while no expression of STK39 was detected in the adjacent normal cervix tissues (the representative figures were shown in Fig. 5).

Fig. 4
figure 4

The representative staining results of CDN2A and SYP. a–d CDN2A was strongly positive in SCCC (case no. Ca0006) while negatively stained in the corresponding non-cancerous tissues; e–h SYP was positive Ca0006 but negative in Pan0006. The images were presented with 40 × and 200 × fields

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Fig. 5
figure 5

The representative images of STK39 staining. a, b The expression of STK39 was negative in normal tissues (case no. Pan0021); c and d STK39 was over-expressed in SCCC case (no. Ca0021). The images were presented with 40 × and 200 × fields

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DEPs associated with recurrence in early stage SCCC

Surgery remained the first choice for SCCC at early stages, even though the recurrence rate was much higher than other types of cervical cancer. As for the seven-stage IB (2 IB1, 4 IB2, 1 IB3) cases of our study, five patients presented recurrence/metastasis and finally died of this disease; the DFS/OS were 21/31, 24/36, 17/20, 14/32, and 37/48 months. The two cases (both were stage IB2) with no recurrence were still alive until the last follow-up (OS were 62 and 67 months). To identify the proteins involved in SCCC recurrence, we further investigated DEPs between the two groups. At the cut-off (FC ≥ 1.5 and P < 0.05), 63 DEPs (24 up-regulated and 39 down-regulated) were identified (Table 4). Among the up-regulated proteins, BDH1 (Q02338) was the key enzyme for the catabolism of fatty acid, GNS (P15586) was involved in the catabolism of heparin/heparan sulfate/keratan sulfate, and ALDH9A1 (P49189) could catalyze the dehydrogenation of gamma-aminobutyraldehyde into gamma-aminobutyric acid (GABA). NAT1 (Q8WUY8) plays a key role in the catabolism of folate. NMNAT1 (Q9HAN9) and ATIC (P31939) are crucial for the biosynthesis of nicotinamide adenine dinucleotide and purine. Due to the limited sample size, the GO enrichment and KEGG pathway analyses were not performed.

Table 4 DEPs associated with tumor recurrence
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Discussion

For SCCC, the significant aggressiveness and poor prognosis (even after extensive treatments) have been well documented in previous studies [22]. This could be mainly attributed to the native behaviors of SCCC tumors, such as rapid proliferation and innate resistance to current therapies [22]. Several previous studies have tried to explore the genetic aberrations in SCCC. The common events were LOH at chromosome 9p and 3p and TP53 mutations, which also occurred frequently in a wide range of human malignancies. Using the whole-exome sequencing, mutations of key genes of PI3K/AKT/mTOR were also detected in several SCCC cases. Compared with the findings at DNA and RNA levels, there was limited knowledge about unique protein profiles in SCCC. In our study, both GO enrichment and KEGG pathway analysis showed that the up-regulated DEPs were significantly correlated with DNA replication, chromosome duplication, allocation, and conformation change, indicating the vigorous mitosis of SCCC cancer cells. This is crucial for the extraordinary growth of SCCC tumors, especially under the pressure of radiation and chemotherapy [23]. Besides genetic materials, the abundant energy supply was inevitable for the rapid tumor growth [23]. Thus, it was not surprising to find that the most enriched molecular functions were ATPase activity, catalytic activity acting on DNA, and DNA helicase activity. Collectively, the above findings demonstrated that uncontrolled proliferation was a distinctive feature of SCCC and these abnormal proteins and pathways should be considered as potential targets for developing novel therapies.

Besides CDN2A (Cyclin-dependent kinase inhibitor 2A, also known as P16), other significantly upregulated DEPs were also candidate markers for SCCC diagnosis and treatment, including STK39 (a STE20/SPS1-related proline-alanine-rich protein kinase), ZMYM2 (Zinc finger MYM-type protein 2), CKMT1A (Creatine kinase U-type, mitochondrial), and HIP1R (Huntingtin-interacting protein 1-related protein). As previously reported, STK39 was involved in the development and progression of various human malignancies. In lung carcinoma, the over-expression of STK39 was associated with advanced tumor stage and poor prognosis [24]. Similar findings were also detected in human osteosarcoma and hepatocellular carcinoma [25, 26]. Mechanistically, it was revealed that STK39 bound with PLK1 and then activated MAPK signaling pathway, which consequently promoted tumor proliferation and aggression in hepatocellular carcinoma [25]. Furthermore, in cervical cancer, STK39 significantly enhanced tumor invasion via activating the NF-κB/p38-MAPK/MMP2 signaling pathway [27]. Consistently, our protein–protein interaction analysis between STK39, MAPT and MAPKs (including MAPK1, MAPK13 and MAP2K1). In our validation cohort, the over-expression of STK39 was detected in 80% SCCC cases, while in none of the normal control tissues. This might indicate a potential role of STK39 in SCCC, which attracted our attention for further explorations.

As our results shown, most up-regulated DEPs were associated with DNA replication, especially the nuclear DNA replication. ZMYM2 was one of them and ranked as the second mostly up-regulated DEP. The ZMYM2 protein was a zinc finger protein which participate into a histone deacetylase complex which was activated in many kinds of cancers to inhibit the functions of tumor suppressor genes [28, 29]. The latest findings proved that ZMYM2 could constrain 53BP1 from binding chromatin and thus promote the DSB (double-strand break) repair in a BRCA-dependent manner [30]. The overexpression of ZMYM2 was also detected in human ovarian cancer, which could remarkedly promote tumor growth in vitro and in vivo [31]. Furthermore, destruction of the ZMYM2-containing complex was proposed as a therapeutic strategy to overcome the stemness of ovarian cancer cells [31].

According to the current knowledge, the role of HIP1R remains controversial. In gastric cancer, HIP1R inhibited the AKT pathway and served as a tumor suppressor via promoting apoptosis and inhibiting tumor invasion [32]. On the contrast, Burnstein et al. proved that HIP1R was significantly upregulated in metastatic prostate cancer [33]. In vitro, HIP1R functioned as an oncogene to enhance the invasion and migration of human prostate cancer cells [33]. Consistently, in non-small cell lung carcinoma, patients with higher expression of HIP1R presented worse progression-free survival and overall survival than those with lower HIP1R [34]. Interestingly, the authors found that HIP1R was negatively correlated with PD-L1 level and served as an independent predictor for tumor response to the anti-PD-1 treatment [34]. Moreover, Xu et al. recently proved that HIP1R could bind PD-L1 at a conserved domain and then deliver PD-L1 to the lysosome for proteolysis. Thus, tumor cells with high expression of HIP1R presented lower PD-L1 level and poor response to the therapy targeting PD-1/PD-L1 signal [35]. As previously reported, PD-L1 was notably lower in SCCC than those in either squamous cell cancer or adenocarcinoma [36, 37]. In our current study, PD-L1 was excluded for analysis due to its extreme low abundance, which did not provide a direct correlation between high level of HIP1R and low level of PD-L1 and needed further investigation.

Compared to its low incidence at other sites, more than 95% of small cell carcinomas arise in the lung (SCLC), accounting for 15–20% of all lung cancer [38]. Therefore, most of the previous research findings of small cell carcinomas were obtained in SCLC. And unsurprisingly, most of the current treating strategies for small cell carcinoma were also learned from those for SCLC [39]. In another study, the authors found that SCCC and SCLC shared similar protein expression profiles, while different from those of squamous cell cancer or adenocarcinoma of the cervix. Although the sample size is quite small, 16 up-regulated proteins in SCCC were identified [40]. Interestingly, NT5DC2 and VRK1 were also up-regulated in the SCCC cases of our cohort. In the contrast, Schultheis et al. demonstrated that SCCC harbored a low mutation burden, few copy number alterations, and other than TP53 in two cases (2/9, 22.2%) no recurrently mutated genes. The majority of mutations were likely passenger missense mutations and only a few affected previously described cancer-related genes [41]. By screening the public database, they also concluded that the overall non-silent mutation rate of SCCC was significantly lower than that of SCLC, HPV-driven cervical adenoma- and squamous cell carcinomas, or HPV-positive head and neck squamous cell carcinomas [41]. These findings indicated that SCCC might be of unique molecular characteristics and more research is warranted to uncover the details.

Treated with surgery and appropriate adjuvant therapies, the prognosis for early-stage SCCC was relatively better than for advanced tumors [7]. However, tumor relapse and metastasis were not rare in this group. In our cohort, a different group of DEPs was identified to be associated with tumor recurrence. Their functions were associated with catabolism of fatty acid (BDH1), heparin/heparan sulfate/keratan sulfate (GNS) and gamma-aminobutyric acid (ALDH9A1), folate (NAT1), and biosynthesis of nicotinamide adenine dinucleotide (NMNAT1), purine (ATIC) [42,43,44,45]. Importantly, these products were the necessary raw material for DNA replication and mitosis. Moreover, NAT1 also could help metabolize drugs and other xenobiotics, which might be accountable for the frequent chemo-resistance in SCCC [46]. Collectively, our findings might provide novel clues for the surveillance and prognosis prediction in SCCC patients.

Angiogenesis is inevitable for the multi-stage development of most cancers, as it ensures the supply pipe to transport nutrients and oxygen into tumor mass while removing the metabolic waste [47]. Therefore, anti-angiogenesis drugs could notably suppress tumor growth and present synergistic effects when combined with other treatments [48]. Unexpectedly, in the present study, a group of proteins related to the development of the circulatory system was downregulated, implying a relatively lower activity of neovascularization in SCCC. A similar phenomenon was reported in the high-aggressive pancreatic ductal adenocarcinomas, which was largely avascular and not sensitive to anti-angiogenic regimens [47, 49]. In another retrospective study, we reviewed 24 recurrent and/or metastasized SCCC who received anti-angiogenic drugs. According to the preliminary results, most cases were not sensitive to either the specific competing antibody of VEGF (Bevacizumab) or small molecules targeting VEGFRs (Anlotinib and Apatinib) (not published yet). The poor response rate might be partially attributed to the low level of angiogenesis in SCCC, which was planned to be validated in our further study.

Conclusions

In conclusion, we first reported the protein expression signature of SCCC using quantitative proteomics analysis. Moreover, a panel of key proteins (enzymes) was shown to be associated with SCCC recurrence. Their functions were mainly related to the catabolism or biosynthesis of indispensable substances for DNA replication and organelle formation. These findings revealed novel targets for treating SCCC in the future.